System

Abstract

An object of a system according to an embodiment is to analyze an interest or a trend of a viewer and optimize program production, scheduling, and an advertisement frame.SOLUTION: A system includes a program creation part, a program scheduling part, an advertisement management part, and a viewer analysis part. The program production unit analyzes the interest and trend of the viewer and plans and produces the program. The program scheduling unit analyzes the viewing pattern of the viewer and optimizes the program scheduling. The advertisement management unit analyzes the needs of the advertiser and the interest of the viewer and optimizes the advertisement space. The audience analysis unit analyzes the reaction and the feedback of the audience and improves the program and the advertisement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to produce and organize programs that quickly and accurately reflected viewer interests and trends, and there were also challenges in optimizing advertising slots.

[0005] The system according to the embodiment aims to analyze viewer interests and trends and to optimize program production, programming, and advertising slots. [Means for solving the problem]

[0006] The system according to the embodiment comprises a program production unit, a program scheduling unit, an advertising management unit, and an audience analysis unit. The program production unit analyzes audience interests and trends and plans and produces programs. The program scheduling unit analyzes audience viewing patterns and optimizes program scheduling. The advertising management unit analyzes advertiser needs and audience interests and optimizes advertising slots. The audience analysis unit analyzes audience responses and feedback and improves programs and advertisements. [Effects of the Invention]

[0007] The system according to the embodiment can analyze viewer interests and trends, and optimize program production, programming, and advertising slots. [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) A television channel operation system according to an embodiment of the present invention is a system that realizes cost reductions and provides inexpensive commercial slots by entrusting all operation processes to AI. As a result, the television channel operation system can realize cost reductions and provide inexpensive commercial slots by entrusting all operation processes to AI.

[0029] A television channel operation system according to an embodiment includes a program production unit, a program scheduling unit, an advertising management unit, and an audience analysis unit. The program production unit analyzes audience interests and trends to plan and produce programs. For example, the generation AI analyzes audience viewing histories and social media trends to select popular themes and content. The generation AI can also generate program scripts and videos based on audience interests. The program scheduling unit analyzes audience viewing patterns to optimize program scheduling. For example, the generation AI places popular programs during times when viewers are most likely to watch. The generation AI can also create program scheduling schedules based on audience viewing patterns. The advertising management unit analyzes advertiser needs and audience interests to optimize advertising slots. For example, the generation AI places advertisements that are effective for specific audience demographics. The generation AI can also optimize advertising slot placement based on advertiser needs and audience interests. The audience analysis unit analyzes audience responses and feedback to improve programs and advertisements. For example, the generation AI analyzes viewer comments and ratings to identify areas for improvement in program content. The generation AI can also make improvement suggestions based on viewer reactions and feedback. As a result, the television channel operation system according to the embodiment can reduce costs and generate buzz among viewers by entrusting all operation processes to AI. For example, viewers will become interested in new programs produced by AI and start talking about them, which will increase viewer ratings. Furthermore, advertisers can use inexpensive commercial slots to run effective advertisements.

[0030] The program production department can analyze viewers' past viewing history and social media trends to select popular themes and content. For example, the program production department's generation AI analyzes viewers' past viewing history to select themes and content that are likely to interest the viewers. The generation AI can also analyze social media trends to select popular themes and content. For example, the generation AI can analyze the trends in programs that viewers have watched in the past based on the viewer's viewing history data to select popular themes and content. The generation AI can also analyze social media posting data to select trending themes and content. This makes it possible to produce programs based on the viewers' interests.

[0031] The program scheduling unit can arrange popular programs during the time periods when viewers watch the most. For example, the generation AI analyzes viewers' viewing patterns and arranges popular programs during the time periods when viewers watch the most. The generation AI can also identify the time periods when viewers watch the most based on viewers' viewing data and arrange popular programs during those time periods. For example, the generation AI can analyze viewers' viewing history data and identify the time periods when viewers watch the most. The generation AI can also select programs that viewers are likely to be interested in based on viewers' viewing patterns and arrange those programs during the optimal time periods. This makes it possible to organize programs based on viewers' viewing patterns.

[0032] The advertising management unit can place advertisements that are effective for specific viewer segments. For example, the generation AI analyzes viewer demographic data and places advertisements that are effective for specific viewer segments. The generation AI can also place advertisements that are effective for specific viewer segments based on the viewer's interests and concerns. For example, the generation AI analyzes demographic data such as the viewer's age, gender, and region, and selects advertisements that are effective for specific viewer segments. The generation AI can also place advertisements that are effective for specific viewer segments based on the viewer's interests and concerns. This makes it possible to place advertisements effectively according to the viewer segment.

[0033] The viewer analysis unit can analyze viewer comments and ratings and identify areas for improvement in program content. In the viewer analysis unit, for example, the generation AI analyzes viewer comment data and identifies areas for improvement in program content. The generation AI can also identify areas for improvement in program content based on viewer rating data. For example, the generation AI analyzes viewer comment data and identifies areas that viewers are dissatisfied with and areas that they would like to see improved. The generation AI can also identify areas that viewers rate highly and poorly based on viewer rating data and identify areas for improvement in program content. This makes it possible to improve programs based on viewer feedback.

[0034] The program production department can generate content that is accepted globally by taking into account the preferences of viewers from different cultures and regions. For example, the program production department's generation AI analyzes viewer data from different cultures and regions and customizes program content based on those preferences. For example, it generates scenarios that reflect the cultural background of each region. The generation AI can also automatically generate multilingual programs to accommodate different languages ​​and cultures. For example, the same program can be produced in multiple languages ​​and provided to viewers in each region. The generation AI can also select universal themes and stories for a global audience and create programs that resonate with viewers across different cultures. For example, it could focus on universal themes such as family love and friendship. This makes it possible to produce programs that are suited to different cultures and regions.

[0035] The program production department can incorporate real-time viewer reactions and dynamically change the content as the program progresses. For example, the program production department's generation AI analyzes real-time viewer reaction data and dynamically changes the content as the program progresses. For example, it cuts scenes that do not generate much reaction from viewers and adds scenes that attract interest. The generation AI can also instantly adjust the content of the program based on real-time comments and feedback from viewers. For example, it can add scenes in response to viewer requests. The generation AI can also dynamically change the progress of the program based on real-time viewing data to maintain viewer interest. For example, it can add suspense elements if viewer interest wanes. This makes it possible to progress the program based on real-time viewer reactions.

[0036] The program production department can also be applied to the production of radio and podcast content. For example, the program production department uses generative AI to automatically generate content for radio programs and podcasts. For example, it can select topics based on listener interests and generate scripts. In radio and podcast content production, generative AI can also analyze listener feedback and adjust the content of the next episode. For example, it can continuously cover popular topics. Generative AI can also automatically generate radio and podcast content to provide programs tailored to different time periods and listener demographics. For example, it can produce news and talk shows for commuter hours and relaxing music programs in the evenings. This makes it applicable to the production of radio and podcast content.

[0037] The program production department can analyze viewers' past purchasing history and introduce related products and services within the program. For example, the program production department uses generation AI to analyze viewers' past purchasing history and introduce related products and services within the program based on that data. For example, they can create a special feature related to a product that the viewer has purchased. The generation AI can also introduce product reviews and usage instructions within the program based on the viewer's purchasing history. For example, they can highlight new products that viewers may be interested in. The generation AI can also analyze viewers' purchasing history and promote related products and services within the program. For example, they can provide coupons and special offers related to products that the viewer has purchased. This makes it possible to introduce products and services based on the viewer's purchasing history.

[0038] The program scheduling unit can analyze viewers' lifestyle rhythms and habits and determine the optimal broadcast times. For example, the generation AI in the program scheduling unit analyzes viewers' lifestyle rhythms and habits and determines the optimal broadcast times based on that data. For example, it may place popular programs during the times when viewers watch the most. The generation AI can also optimize program broadcast times based on viewers' lifestyle rhythms. For example, it may place news programs during morning commute times and variety shows during evening relaxation times. The generation AI can also analyze viewer habit data and determine the optimal broadcast times. For example, it may place dramas during times when viewers are relaxing after dinner. This makes it possible to determine broadcast times based on viewers' lifestyle rhythms and habits.

[0039] The program scheduling unit can successively update the programming schedule based on real-time viewing data of viewers. In the program scheduling unit, for example, the generation AI analyzes real-time viewing data of viewers and successively updates the programming schedule based on that data. For example, it extends the broadcast of a program that has attracted increasing viewer interest. The generation AI can also dynamically adjust the programming schedule based on real-time viewing data. For example, it rebroadcasts programs that have received a good response from viewers. The generation AI can also analyze real-time viewing data of viewers and successively update the programming schedule. For example, it can end a program that has attracted declining viewer interest early and start the next program early. This makes it possible to update the programming schedule based on real-time viewing data of viewers.

[0040] The program scheduling unit can also be applied to content distribution for online streaming services. The program scheduling unit, for example, uses a generation AI to optimize content distribution for online streaming services. For example, it automatically arranges recommended content based on the viewer's viewing history. The generation AI can also analyze the viewer's viewing patterns for online streaming services to create an optimal distribution schedule. For example, it can distribute new releases during the time periods when viewers are most likely to watch. The generation AI can also analyze viewing data for online streaming services to optimize content distribution. For example, it can customize content based on the viewer's interests. This makes it applicable to content distribution for online streaming services.

[0041] The program scheduling unit can provide individually customized program scheduling based on the viewer's past viewing history. For example, the generation AI analyzes the viewer's past viewing history and provides individually customized program scheduling based on that data. For example, programs in the viewer's favorite genres are prioritized. The generation AI can also create a program scheduling customized for each viewer based on their viewing history. For example, it can recommend new releases related to programs the viewer has previously watched. The generation AI can also analyze the viewer's past viewing history and provide individually customized program scheduling. For example, if a viewer likes a particular actor, it will prioritize programs featuring that actor. This makes it possible to customize program scheduling based on the viewer's past viewing history.

[0042] The advertising management unit can analyze the viewer's purchasing intent and select the most appropriate advertisement. For example, the generation AI in the advertising management unit analyzes the viewer's purchasing intent data and selects the most appropriate advertisement based on that data. For example, it advertises products that the viewer has a high desire to purchase. The generation AI can also customize the content of the advertisement based on the viewer's purchasing intent. For example, it advertises products and services that the viewer is likely to be interested in. The generation AI can also analyze the viewer's purchasing intent data and select the most appropriate advertisement. For example, it places advertisements related to products that the viewer has purchased in the past. This makes it possible to select the most appropriate advertisement based on the viewer's purchasing intent.

[0043] The advertising management unit can evaluate the effectiveness of an advertisement based on real-time viewer responses and reflect this in the next advertisement placement. In the advertising management unit, for example, the generation AI analyzes real-time viewer response data and evaluates the effectiveness of an advertisement based on that data. For example, an advertisement that receives a good viewer response will be placed again next time. The generation AI can also evaluate the effectiveness of an advertisement based on real-time viewing data and reflect this in the next advertisement placement. For example, an advertisement that receives a poor viewer response will not be placed again next time. The generation AI can also analyze real-time viewer response data and evaluate the effectiveness of an advertisement. For example, an advertisement will be placed during a time period when viewer responses are good. This makes it possible to evaluate the effectiveness of an advertisement based on real-time viewer responses and reflect this in the next advertisement placement.

[0044] The advertising management unit can also be applied to digital signage and online advertising. For example, the advertising management unit uses generation AI to automate advertising management for digital signage. For example, it dynamically changes advertising content based on viewer interests. In addition, generation AI can analyze viewer interest data for online advertising and automatically place optimal ads. For example, it can customize ads based on the viewer's past search history. In addition, generation AI can analyze viewing data for digital signage and online advertising and automate advertising management. For example, it can prioritize the placement of ads that receive a good viewer response. This makes it applicable to digital signage and online advertising.

[0045] The advertising management unit can analyze the viewer's past search history and provide relevant advertisements. For example, the generation AI in the advertising management unit analyzes the viewer's past search history and provides relevant advertisements based on that data. For example, it places advertisements related to products searched for by the viewer. The generation AI can also customize the content of advertisements based on the viewer's search history. For example, it advertises products and services that the viewer may be interested in. The generation AI can also analyze the viewer's past search history and provide relevant advertisements. For example, it places advertisements related to keywords searched for by the viewer. This makes it possible to provide relevant advertisements based on the viewer's past search history.

[0046] The viewer analysis unit can analyze the long-term viewing patterns of viewers and predict trends. For example, the generation AI in the viewer analysis unit analyzes the long-term viewing patterns of viewers and predicts trends based on that data. For example, it identifies the genres that viewers have been watching over a long period of time. The generation AI can also predict long-term trends based on the viewer's viewing history. For example, it can analyze data on programs that viewers have watched in the past and predict future viewing trends. The generation AI can also analyze the long-term viewing patterns of viewers and predict trends. For example, it can identify programs that viewers tend to watch in specific seasons. This makes it possible to predict trends based on the viewer's long-term viewing patterns.

[0047] The viewer analysis unit analyzes real-time comments from viewers and can instantly adjust program content. For example, the generation AI analyzes real-time comment data from viewers and instantly adjusts program content based on that data. For example, it extends scenes that receive many viewer comments. The generation AI can also dynamically adjust program content based on real-time comment data. For example, it can add scenes in response to viewer requests. The generation AI can also analyze real-time comment data from viewers and instantly adjust program content. For example, it can cut scenes that receive poor viewer responses. This makes it possible to instantly adjust program content based on real-time viewer comments.

[0048] The audience analysis unit can also be applied to improving social media content. The audience analysis unit uses, for example, generation AI to improve social media content. For example, it analyzes viewer comments and reactions and identifies areas for improvement in the content. The generation AI can also analyze viewer feedback on social media and improve content. For example, it can prioritize and display posts that have a positive viewer response. The generation AI can also analyze social media viewing data and improve content. For example, it can customize the content of posts based on viewer interests. This makes it possible to apply the system to improving social media content.

[0049] The viewer analysis unit can provide individually customized feedback based on the viewer's past feedback. In the viewer analysis unit, for example, the generation AI analyzes the viewer's past feedback and provides individually customized feedback based on that data. For example, it suggests areas for improvement based on feedback the viewer has provided in the past. The generation AI can also provide individually customized feedback based on the viewer's feedback history. For example, it suggests areas for improvement that the viewer might be interested in. The generation AI can also analyze the viewer's past feedback and provide individually customized feedback. For example, it adjusts program content based on feedback the viewer has provided in the past. This makes it possible to provide individually customized feedback based on the viewer's past feedback.

[0050] The advertising management department can optimize the entire operational process and eliminate waste. In the advertising management department, for example, generative AI analyzes the entire operational process and performs optimization to eliminate waste. For example, it proposes process improvements to improve the efficiency of program production and programming. Generative AI can also monitor each step of the operational process and reduce wasteful resource use. For example, it can eliminate unnecessary staffing and duplicated work. Generative AI can also optimize the entire operational process and achieve cost reductions. For example, it can propose optimization of energy consumption and efficient use of materials. This makes it possible to optimize the entire operational process and eliminate waste.

[0051] The advertising management unit can predict demand for advertising space and set optimal prices based on viewers' real-time viewing data. In the advertising management unit, for example, the generation AI analyzes viewers' real-time viewing data and predicts demand for advertising space based on that data. For example, it sets higher prices for advertising space during times when there are more viewers. The generation AI can also predict demand for advertising space based on real-time viewing data and set optimal prices. For example, it sets higher prices for advertising space for programs that viewers are interested in. The generation AI can also analyze viewers' real-time viewing data and predict demand for advertising space. For example, it sets higher prices for advertising space for programs that viewers respond well to. This makes it possible to predict demand for advertising space and set optimal prices based on viewers' real-time viewing data.

[0052] The ad management unit can also be applied to online platforms and mobile apps. For example, the ad management unit uses generation AI to automate ad management for online platforms and mobile apps, thereby reducing costs. For example, it dynamically changes ad content based on viewer interests. In addition, generation AI can analyze viewer interest data on online platforms and mobile apps and automatically place optimal ads. For example, it customizes ads based on the viewer's past search history. In addition, generation AI can analyze viewing data for online platforms and mobile apps and automate ad management. For example, it prioritizes the placement of ads that receive a good viewer response. This makes it applicable to online platforms and mobile apps.

[0053] The advertising management unit can analyze viewers' past purchasing history and provide special advertising space to viewers with a high intent to purchase. For example, the generation AI analyzes viewers' past purchasing history and provides special advertising space to viewers with a high intent to purchase based on that data. For example, it places special advertisements related to products purchased by the viewer. The generation AI can also provide special advertising space to viewers with a high intent to purchase based on the viewer's purchasing history. For example, it can provide special introductions to new products that the viewer might be interested in. The generation AI can also analyze viewers' past purchasing history and provide special advertising space to viewers with a high intent to purchase. For example, it can provide coupons or benefits related to products purchased by the viewer. This makes it possible to provide special advertising space based on the viewer's past purchasing history.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The TV channel management system can also analyze viewers' health data and provide health-related programs and advertisements. For example, programs related to exercise and health can be organized based on the viewer's fitness data. It can also analyze the viewer's dietary data and provide programs related to healthy recipes and meals. It can also place programs and advertisements with a relaxing effect based on the viewer's sleep data. This makes it possible to provide content tailored to the viewer's health condition.

[0056] The TV channel management system can also analyze viewers' purchasing history and introduce related products and services within the program. For example, it can create a special feature on products that viewers have purchased in the past. It can also introduce product reviews and usage instructions within the program based on viewers' purchasing history. It can also feature new products that viewers may be interested in. This makes it possible to introduce products and services based on viewers' purchasing history.

[0057] The TV channel operation system can also incorporate real-time viewer reactions and dynamically change the content of a program while it is running. For example, it can cut scenes that do not generate much interest from viewers and add more interesting scenes. It can also instantly adjust the content of a program based on real-time viewer comments and feedback. It can even add suspenseful elements if viewer interest wanes. This allows the program to progress based on real-time viewer reactions.

[0058] The TV channel management system can also generate content that is accepted globally, taking into account the preferences of viewers from different cultures and regions. For example, it can generate scenarios that reflect the cultural background of each region. It can also automatically generate multilingual programs to accommodate different languages ​​and cultures. Furthermore, it can select universal themes and stories for a global audience and produce programs that resonate with viewers in different cultural spheres. This makes it possible to produce programs that are suited to different cultures and regions.

[0059] The television channel operation system can also continuously update the programming schedule based on real-time viewing data of viewers. For example, it can extend the duration of a program that has attracted increasing interest from viewers. It can also dynamically adjust the programming schedule based on real-time viewing data. It can also end a program that has attracted less interest from viewers early and start the next program early. This makes it possible to update the programming schedule based on real-time viewing data of viewers.

[0060] The television channel operating system can also analyze the viewer's past search history and provide relevant advertisements. For example, it can place advertisements related to products the viewer has searched for. It can also customize the content of advertisements based on the viewer's search history. It can also advertise products and services that the viewer may be interested in. This makes it possible to provide relevant advertisements based on the viewer's past search history.

[0061] The television channel operation system can also analyze viewers' long-term viewing patterns and predict trends. For example, it can identify the genres that viewers have been watching over a long period of time. It can also predict long-term trends based on viewing history. It can also identify programs that viewers tend to watch in specific seasons. This makes it possible to predict trends based on viewers' long-term viewing patterns.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The program production department analyzes viewer interests and trends and plans and produces programs. For example, the generative AI analyzes viewers' past viewing history and social media trends to select popular themes and content. The generative AI can also generate program scripts and footage based on viewer interests. Step 2: The program scheduling unit analyzes viewers' viewing patterns and optimizes program scheduling. For example, the generation AI places popular programs during the times when viewers watch the most. The generation AI can also create program scheduling schedules based on viewers' viewing patterns. Step 3: The advertising management unit analyzes the advertiser's needs and viewer interests and optimizes the ad slots. For example, the generation AI places ads that are effective for a specific viewer demographic. The generation AI can also optimize the placement of ad slots based on the advertiser's needs and viewer interests. Step 4: The viewer analysis unit analyzes viewer reactions and feedback to improve programs and advertisements. For example, the generation AI analyzes viewer comments and ratings to identify areas for improvement in program content. The generation AI can also make improvement suggestions based on viewer reactions and feedback.

[0064] (Example 2) A television channel operation system according to an embodiment of the present invention is a system that realizes cost reductions and provides inexpensive commercial slots by entrusting all operation processes to AI. As a result, the television channel operation system can realize cost reductions and provide inexpensive commercial slots by entrusting all operation processes to AI.

[0065] A television channel operation system according to an embodiment includes a program production unit, a program scheduling unit, an advertising management unit, and an audience analysis unit. The program production unit analyzes audience interests and trends to plan and produce programs. For example, the generation AI analyzes audience viewing histories and social media trends to select popular themes and content. The generation AI can also generate program scripts and videos based on audience interests. The program scheduling unit analyzes audience viewing patterns to optimize program scheduling. For example, the generation AI places popular programs during times when viewers are most likely to watch. The generation AI can also create program scheduling schedules based on audience viewing patterns. The advertising management unit analyzes advertiser needs and audience interests to optimize advertising slots. For example, the generation AI places advertisements that are effective for specific audience demographics. The generation AI can also optimize advertising slot placement based on advertiser needs and audience interests. The audience analysis unit analyzes audience responses and feedback to improve programs and advertisements. For example, the generation AI analyzes viewer comments and ratings to identify areas for improvement in program content. The generation AI can also make improvement suggestions based on viewer reactions and feedback. As a result, the television channel operation system according to the embodiment can reduce costs and generate buzz among viewers by entrusting all operation processes to AI. For example, viewers will become interested in new programs produced by AI and start talking about them, which will increase viewer ratings. Furthermore, advertisers can use inexpensive commercial slots to run effective advertisements.

[0066] The program production department can analyze viewers' past viewing history and social media trends to select popular themes and content. For example, the program production department's generation AI analyzes viewers' past viewing history to select themes and content that are likely to interest the viewers. The generation AI can also analyze social media trends to select popular themes and content. For example, the generation AI can analyze the trends in programs that viewers have watched in the past based on the viewer's viewing history data to select popular themes and content. The generation AI can also analyze social media posting data to select trending themes and content. This makes it possible to produce programs based on the viewers' interests.

[0067] The program scheduling unit can arrange popular programs during the time periods when viewers watch the most. For example, the generation AI analyzes viewers' viewing patterns and arranges popular programs during the time periods when viewers watch the most. The generation AI can also identify the time periods when viewers watch the most based on viewers' viewing data and arrange popular programs during those time periods. For example, the generation AI can analyze viewers' viewing history data and identify the time periods when viewers watch the most. The generation AI can also select programs that viewers are likely to be interested in based on viewers' viewing patterns and arrange those programs during the optimal time periods. This makes it possible to organize programs based on viewers' viewing patterns.

[0068] The advertising management unit can place advertisements that are effective for specific viewer segments. For example, the generation AI analyzes viewer demographic data and places advertisements that are effective for specific viewer segments. The generation AI can also place advertisements that are effective for specific viewer segments based on the viewer's interests and concerns. For example, the generation AI analyzes demographic data such as the viewer's age, gender, and region, and selects advertisements that are effective for specific viewer segments. The generation AI can also place advertisements that are effective for specific viewer segments based on the viewer's interests and concerns. This makes it possible to place advertisements effectively according to the viewer segment.

[0069] The viewer analysis unit can analyze viewer comments and ratings and identify areas for improvement in program content. In the viewer analysis unit, for example, the generation AI analyzes viewer comment data and identifies areas for improvement in program content. The generation AI can also identify areas for improvement in program content based on viewer rating data. For example, the generation AI analyzes viewer comment data and identifies areas that viewers are dissatisfied with and areas that they would like to see improved. The generation AI can also identify areas that viewers rate highly and poorly based on viewer rating data and identify areas for improvement in program content. This makes it possible to improve programs based on viewer feedback.

[0070] The program production department can estimate viewer emotions and adjust the tone and theme of the program based on those emotions. For example, the program production department's generation AI analyzes viewer emotion data and dynamically adjusts the tone and theme of the program according to changes in emotion. For example, if the viewer is moved, it will add more moving scenes. The generation AI can also monitor the viewer's emotion score in real time and adjust the program's progress based on their emotions. For example, if the viewer is excited, it will add an action scene. The generation AI can also automatically generate the scene that will most move the viewer based on the emotion estimation data and place it as the climax of the program. For example, it can identify the moment when the viewer's emotions reach their peak and insert an emotional scene at that time. This makes it possible to produce programs based on the viewer's emotions.

[0071] The program production department can generate content that is accepted globally by taking into account the preferences of viewers from different cultures and regions. For example, the program production department's generation AI analyzes viewer data from different cultures and regions and customizes program content based on those preferences. For example, it generates scenarios that reflect the cultural background of each region. The generation AI can also automatically generate multilingual programs to accommodate different languages ​​and cultures. For example, the same program can be produced in multiple languages ​​and provided to viewers in each region. The generation AI can also select universal themes and stories for a global audience and create programs that resonate with viewers across different cultures. For example, it could focus on universal themes such as family love and friendship. This makes it possible to produce programs that are suited to different cultures and regions.

[0072] The program production department can incorporate real-time viewer reactions and dynamically change the content as the program progresses. For example, the program production department's generation AI analyzes real-time viewer reaction data and dynamically changes the content as the program progresses. For example, it cuts scenes that do not generate much reaction from viewers and adds scenes that attract interest. The generation AI can also instantly adjust the content of the program based on real-time comments and feedback from viewers. For example, it can add scenes in response to viewer requests. The generation AI can also dynamically change the progress of the program based on real-time viewing data to maintain viewer interest. For example, it can add suspense elements if viewer interest wanes. This makes it possible to progress the program based on real-time viewer reactions.

[0073] The program production department can also be applied to the production of radio and podcast content. For example, the program production department uses generative AI to automatically generate content for radio programs and podcasts. For example, it can select topics based on listener interests and generate scripts. In radio and podcast content production, generative AI can also analyze listener feedback and adjust the content of the next episode. For example, it can continuously cover popular topics. Generative AI can also automatically generate radio and podcast content to provide programs tailored to different time periods and listener demographics. For example, it can produce news and talk shows for commuter hours and relaxing music programs in the evenings. This makes it applicable to the production of radio and podcast content.

[0074] The program production department can analyze viewers' past purchasing history and introduce related products and services within the program. For example, the program production department uses generation AI to analyze viewers' past purchasing history and introduce related products and services within the program based on that data. For example, they can create a special feature related to a product that the viewer has purchased. The generation AI can also introduce product reviews and usage instructions within the program based on the viewer's purchasing history. For example, they can highlight new products that viewers may be interested in. The generation AI can also analyze viewers' purchasing history and promote related products and services within the program. For example, they can provide coupons and special offers related to products that the viewer has purchased. This makes it possible to introduce products and services based on the viewer's purchasing history.

[0075] The program production department can use the emotion estimation function to automatically generate scenes that will move viewers the most and broadcast them as special programs. The program production department can, for example, use the emotion estimation function to automatically generate scenes that will move viewers the most and broadcast them as special programs. For example, by editing moving scenes based on viewer emotion data. The generation AI can also analyze viewers' emotional reactions and automatically generate moving scenes to broadcast as special programs. For example, by identifying the moment when the viewer's emotion reaches its peak and emphasizing that scene. The generation AI can also automatically generate scenes that will move viewers the most based on emotion estimation data and broadcast them as special programs. For example, by collecting and editing scenes with high viewer emotion scores. This makes it possible to broadcast scenes that will move viewers the most as special programs.

[0076] The program scheduling unit can estimate the viewer's emotions and dynamically adjust program scheduling according to changes in emotions. For example, the generation AI in the program scheduling unit analyzes the viewer's emotional data and dynamically adjusts program scheduling according to changes in emotions. For example, programs with a relaxing effect are placed during times when the viewer is relaxed. The generation AI can also monitor the viewer's emotional scores in real time and adjust program scheduling based on emotions. For example, action movies are placed during times when the viewer is excited. The generation AI can also dynamically adjust program scheduling according to changes in the viewer's emotions based on the emotion estimation data. For example, moving dramas are placed during times when the viewer is moved. This makes it possible to schedule programs based on the viewer's emotions.

[0077] The program scheduling unit can analyze viewers' lifestyle rhythms and habits and determine the optimal broadcast times. For example, the generation AI in the program scheduling unit analyzes viewers' lifestyle rhythms and habits and determines the optimal broadcast times based on that data. For example, it may place popular programs during the times when viewers watch the most. The generation AI can also optimize program broadcast times based on viewers' lifestyle rhythms. For example, it may place news programs during morning commute times and variety shows during evening relaxation times. The generation AI can also analyze viewer habit data and determine the optimal broadcast times. For example, it may place dramas during times when viewers are relaxing after dinner. This makes it possible to determine broadcast times based on viewers' lifestyle rhythms and habits.

[0078] The program scheduling unit can successively update the programming schedule based on real-time viewing data of viewers. In the program scheduling unit, for example, the generation AI analyzes real-time viewing data of viewers and successively updates the programming schedule based on that data. For example, it extends the broadcast of a program that has attracted increasing viewer interest. The generation AI can also dynamically adjust the programming schedule based on real-time viewing data. For example, it rebroadcasts programs that have received a good response from viewers. The generation AI can also analyze real-time viewing data of viewers and successively update the programming schedule. For example, it can end a program that has attracted declining viewer interest early and start the next program early. This makes it possible to update the programming schedule based on real-time viewing data of viewers.

[0079] The program scheduling unit can also be applied to content distribution for online streaming services. The program scheduling unit, for example, uses a generation AI to optimize content distribution for online streaming services. For example, it automatically arranges recommended content based on the viewer's viewing history. The generation AI can also analyze the viewer's viewing patterns for online streaming services to create an optimal distribution schedule. For example, it can distribute new releases during the time periods when viewers are most likely to watch. The generation AI can also analyze viewing data for online streaming services to optimize content distribution. For example, it can customize content based on the viewer's interests. This makes it applicable to content distribution for online streaming services.

[0080] The program scheduling unit can provide individually customized program scheduling based on the viewer's past viewing history. For example, the generation AI analyzes the viewer's past viewing history and provides individually customized program scheduling based on that data. For example, programs in the viewer's favorite genres are prioritized. The generation AI can also create a program scheduling customized for each viewer based on their viewing history. For example, it can recommend new releases related to programs the viewer has previously watched. The generation AI can also analyze the viewer's past viewing history and provide individually customized program scheduling. For example, if a viewer likes a particular actor, it will prioritize programs featuring that actor. This makes it possible to customize program scheduling based on the viewer's past viewing history.

[0081] The program scheduling unit can use the emotion estimation function to schedule programs with a relaxing effect for times when the viewer can be most relaxed. The program scheduling unit, for example, uses the emotion estimation function to schedule programs with a relaxing effect for times when the viewer can be most relaxed. For example, a relaxing music program may be scheduled based on the viewer's emotion data. The generation AI can also analyze the viewer's emotion score and schedule programs with a relaxing effect for the optimal time period. For example, a documentary with a relaxing effect may be scheduled for times when the viewer is feeling stressed. The generation AI can also schedule programs with a relaxing effect for times when the viewer can be most relaxed based on the emotion estimation data. For example, a nature program with a relaxing effect may be scheduled for times when the viewer's emotions are calm. This makes it possible to schedule programs with a relaxing effect for times when the viewer can be most relaxed.

[0082] The advertising management unit can estimate the viewer's emotions and adjust the content and timing of advertisements based on those emotions. For example, the generation AI analyzes the viewer's emotional data and adjusts the content and timing of advertisements based on those emotions. For example, it places advertisements with a relaxing effect during times when the viewer is relaxed. The generation AI can also monitor the viewer's emotional score in real time and adjust the timing of advertisements based on their emotions. For example, it places an action movie trailer during times when the viewer is excited. The generation AI can also dynamically change the content of advertisements based on the emotion estimation data in response to changes in the viewer's emotions. For example, it places an advertisement with an emotional story during times when the viewer is emotional. This makes it possible to adjust the content and timing of advertisements based on the viewer's emotions.

[0083] The advertising management unit can analyze the viewer's purchasing intent and select the most appropriate advertisement. For example, the generation AI in the advertising management unit analyzes the viewer's purchasing intent data and selects the most appropriate advertisement based on that data. For example, it advertises products that the viewer has a high desire to purchase. The generation AI can also customize the content of the advertisement based on the viewer's purchasing intent. For example, it advertises products and services that the viewer is likely to be interested in. The generation AI can also analyze the viewer's purchasing intent data and select the most appropriate advertisement. For example, it places advertisements related to products that the viewer has purchased in the past. This makes it possible to select the most appropriate advertisement based on the viewer's purchasing intent.

[0084] The advertising management unit can evaluate the effectiveness of an advertisement based on real-time viewer responses and reflect this in the next advertisement placement. In the advertising management unit, for example, the generation AI analyzes real-time viewer response data and evaluates the effectiveness of an advertisement based on that data. For example, an advertisement that receives a good viewer response will be placed again next time. The generation AI can also evaluate the effectiveness of an advertisement based on real-time viewing data and reflect this in the next advertisement placement. For example, an advertisement that receives a poor viewer response will not be placed again next time. The generation AI can also analyze real-time viewer response data and evaluate the effectiveness of an advertisement. For example, an advertisement will be placed during a time period when viewer responses are good. This makes it possible to evaluate the effectiveness of an advertisement based on real-time viewer responses and reflect this in the next advertisement placement.

[0085] The advertising management unit can also be applied to digital signage and online advertising. For example, the advertising management unit uses generation AI to automate advertising management for digital signage. For example, it dynamically changes advertising content based on viewer interests. In addition, generation AI can analyze viewer interest data for online advertising and automatically place optimal ads. For example, it can customize ads based on the viewer's past search history. In addition, generation AI can analyze viewing data for digital signage and online advertising and automate advertising management. For example, it can prioritize the placement of ads that receive a good viewer response. This makes it applicable to digital signage and online advertising.

[0086] The advertising management unit can analyze the viewer's past search history and provide relevant advertisements. For example, the generation AI in the advertising management unit analyzes the viewer's past search history and provides relevant advertisements based on that data. For example, it places advertisements related to products searched for by the viewer. The generation AI can also customize the content of advertisements based on the viewer's search history. For example, it advertises products and services that the viewer may be interested in. The generation AI can also analyze the viewer's past search history and provide relevant advertisements. For example, it places advertisements related to keywords searched for by the viewer. This makes it possible to provide relevant advertisements based on the viewer's past search history.

[0087] The advertising management unit can use the emotion estimation function to insert advertisements at the moments when viewers are most excited, thereby maximizing advertising effectiveness. The advertising management unit, for example, uses the emotion estimation function to insert advertisements at the moments when viewers are most excited. For example, advertisements are placed in scenes with high levels of excitement based on viewer emotion data. The generation AI can also analyze the viewer's emotion score and insert advertisements at exciting moments. For example, an action movie trailer is placed at a time when viewers are excited. The generation AI can also insert advertisements at the moments when viewers are most excited, based on emotion estimation data, thereby maximizing advertising effectiveness. For example, an advertisement is placed at the moment when the viewer's emotions reach their peak. This makes it possible to insert advertisements at the moments when viewers are most excited, thereby maximizing advertising effectiveness.

[0088] The viewer analysis unit estimates viewer emotions and classifies feedback based on those emotions, which can be used to improve programs. In the viewer analysis unit, for example, the generation AI analyzes viewer emotion data and classifies feedback based on emotions. For example, it classifies feedback into positive and negative based on the viewer's emotion score. The generation AI can also monitor the viewer's emotion score in real time and classify feedback based on emotion. For example, if the viewer is moved, it classifies the feedback as emotional. The generation AI can also classify feedback according to the viewer's emotions based on the emotion estimation data, which can be used to improve programs. For example, it can identify feedback at the moment when the viewer's emotions reach their peak and emphasize those scenes. This makes it possible to classify feedback based on viewer emotions and improve programs.

[0089] The viewer analysis unit can analyze the long-term viewing patterns of viewers and predict trends. For example, the generation AI in the viewer analysis unit analyzes the long-term viewing patterns of viewers and predicts trends based on that data. For example, it identifies the genres that viewers have been watching over a long period of time. The generation AI can also predict long-term trends based on the viewer's viewing history. For example, it can analyze data on programs that viewers have watched in the past and predict future viewing trends. The generation AI can also analyze the long-term viewing patterns of viewers and predict trends. For example, it can identify programs that viewers tend to watch in specific seasons. This makes it possible to predict trends based on the viewer's long-term viewing patterns.

[0090] The viewer analysis unit analyzes real-time comments from viewers and can instantly adjust program content. For example, the generation AI analyzes real-time comment data from viewers and instantly adjusts program content based on that data. For example, it extends scenes that receive many viewer comments. The generation AI can also dynamically adjust program content based on real-time comment data. For example, it can add scenes in response to viewer requests. The generation AI can also analyze real-time comment data from viewers and instantly adjust program content. For example, it can cut scenes that receive poor viewer responses. This makes it possible to instantly adjust program content based on real-time viewer comments.

[0091] The audience analysis unit can also be applied to improving social media content. The audience analysis unit uses, for example, generation AI to improve social media content. For example, it analyzes viewer comments and reactions and identifies areas for improvement in the content. The generation AI can also analyze viewer feedback on social media and improve content. For example, it can prioritize and display posts that have a positive viewer response. The generation AI can also analyze social media viewing data and improve content. For example, it can customize the content of posts based on viewer interests. This makes it possible to apply the system to improving social media content.

[0092] The viewer analysis unit can provide individually customized feedback based on the viewer's past feedback. In the viewer analysis unit, for example, the generation AI analyzes the viewer's past feedback and provides individually customized feedback based on that data. For example, it suggests areas for improvement based on feedback the viewer has provided in the past. The generation AI can also provide individually customized feedback based on the viewer's feedback history. For example, it suggests areas for improvement that the viewer might be interested in. The generation AI can also analyze the viewer's past feedback and provide individually customized feedback. For example, it adjusts program content based on feedback the viewer has provided in the past. This makes it possible to provide individually customized feedback based on the viewer's past feedback.

[0093] The viewer analysis unit can use the emotion estimation function to identify the scene that moved the viewer the most and produce a special program that emphasizes that scene. The viewer analysis unit, for example, uses the emotion estimation function to identify the scene that moved the viewer the most and produce a special program that emphasizes that scene. For example, it edits moving scenes based on viewer emotion data. The generation AI can also analyze the viewer's emotional reactions, identify moving scenes, and broadcast them as special programs. For example, it identifies the moment when the viewer's emotions reach their peak and emphasizes that scene. The generation AI can also identify the scene that moved the viewer the most based on emotion estimation data and produce a special program that emphasizes that scene. For example, it collects and edits scenes with high viewer emotion scores. This makes it possible to identify the scene that moved the viewer the most and produce a special program that emphasizes that scene.

[0094] The advertising management unit can estimate viewer emotions and dynamically set advertising rates based on those emotions. For example, the generation AI analyzes viewer emotion data and dynamically sets advertising rates based on those emotions. For example, it sets higher advertising rates during times when the viewer is excited. The generation AI can also monitor viewer emotion scores in real time and adjust advertising rates based on those emotions. For example, it sets lower advertising rates during times when the viewer is relaxed. The generation AI can also dynamically set advertising rates according to changes in viewer emotions based on emotion estimation data. For example, it sets higher advertising rates during times when the viewer is emotional. This makes it possible to dynamically set advertising rates based on viewer emotions.

[0095] The advertising management department can optimize the entire operational process and eliminate waste. In the advertising management department, for example, generative AI analyzes the entire operational process and performs optimization to eliminate waste. For example, it proposes process improvements to improve the efficiency of program production and programming. Generative AI can also monitor each step of the operational process and reduce wasteful resource use. For example, it can eliminate unnecessary staffing and duplicated work. Generative AI can also optimize the entire operational process and achieve cost reductions. For example, it can propose optimization of energy consumption and efficient use of materials. This makes it possible to optimize the entire operational process and eliminate waste.

[0096] The advertising management unit can predict demand for advertising space and set optimal prices based on viewers' real-time viewing data. In the advertising management unit, for example, the generation AI analyzes viewers' real-time viewing data and predicts demand for advertising space based on that data. For example, it sets higher prices for advertising space during times when there are more viewers. The generation AI can also predict demand for advertising space based on real-time viewing data and set optimal prices. For example, it sets higher prices for advertising space for programs that viewers are interested in. The generation AI can also analyze viewers' real-time viewing data and predict demand for advertising space. For example, it sets higher prices for advertising space for programs that viewers respond well to. This makes it possible to predict demand for advertising space and set optimal prices based on viewers' real-time viewing data.

[0097] The ad management unit can also be applied to online platforms and mobile apps. For example, the ad management unit uses generation AI to automate ad management for online platforms and mobile apps, thereby reducing costs. For example, it dynamically changes ad content based on viewer interests. In addition, generation AI can analyze viewer interest data on online platforms and mobile apps and automatically place optimal ads. For example, it customizes ads based on the viewer's past search history. In addition, generation AI can analyze viewing data for online platforms and mobile apps and automate ad management. For example, it prioritizes the placement of ads that receive a good viewer response. This makes it applicable to online platforms and mobile apps.

[0098] The advertising management unit can analyze viewers' past purchasing history and provide special advertising space to viewers with a high intent to purchase. For example, the generation AI analyzes viewers' past purchasing history and provides special advertising space to viewers with a high intent to purchase based on that data. For example, it places special advertisements related to products purchased by the viewer. The generation AI can also provide special advertising space to viewers with a high intent to purchase based on the viewer's purchasing history. For example, it can provide special introductions to new products that the viewer might be interested in. The generation AI can also analyze viewers' past purchasing history and provide special advertising space to viewers with a high intent to purchase. For example, it can provide coupons or benefits related to products purchased by the viewer. This makes it possible to provide special advertising space based on the viewer's past purchasing history.

[0099] The advertising management unit can use the emotion estimation function to provide special advertising slots at moments when the viewer is most moved, thereby maximizing advertising effectiveness. The advertising management unit, for example, uses the emotion estimation function to provide special advertising slots at moments when the viewer is most moved. For example, special advertisements are placed in emotional scenes based on viewer emotion data. The generation AI can also analyze the viewer's emotion score and provide special advertising slots at emotional moments. For example, special promotions are run during times when the viewer is most moved. The generation AI can also provide special advertising slots at moments when the viewer is most moved, based on emotion estimation data, thereby maximizing advertising effectiveness. For example, special advertisements are placed at moments when the viewer's emotions reach their peak. This makes it possible to provide special advertising slots at moments when the viewer is most moved, thereby maximizing advertising effectiveness.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The TV channel management system can also analyze viewers' health data and provide health-related programs and advertisements. For example, programs related to exercise and health can be organized based on the viewer's fitness data. It can also analyze the viewer's dietary data and provide programs related to healthy recipes and meals. It can also place programs and advertisements with a relaxing effect based on the viewer's sleep data. This makes it possible to provide content tailored to the viewer's health condition.

[0102] The TV channel management system can also analyze viewers' purchasing history and introduce related products and services within the program. For example, it can create a special feature on products that viewers have purchased in the past. It can also introduce product reviews and usage instructions within the program based on viewers' purchasing history. It can also feature new products that viewers may be interested in. This makes it possible to introduce products and services based on viewers' purchasing history.

[0103] The TV channel operation system can also incorporate real-time viewer reactions and dynamically change the content of a program while it is running. For example, it can cut scenes that do not generate much interest from viewers and add more interesting scenes. It can also instantly adjust the content of a program based on real-time viewer comments and feedback. It can even add suspenseful elements if viewer interest wanes. This allows the program to progress based on real-time viewer reactions.

[0104] The TV channel operation system can also estimate the viewer's emotions and adjust the tone and theme of the program based on the emotion. For example, if the viewer is emotional, it can add more emotional scenes. It can also monitor the viewer's emotion score in real time and adjust the program's progress based on the emotion. Furthermore, it can add action scenes if the viewer is excited. This makes it possible to create programs based on the viewer's emotions.

[0105] The TV channel management system can also generate content that is accepted globally, taking into account the preferences of viewers from different cultures and regions. For example, it can generate scenarios that reflect the cultural background of each region. It can also automatically generate multilingual programs to accommodate different languages ​​and cultures. Furthermore, it can select universal themes and stories for a global audience and produce programs that resonate with viewers in different cultural spheres. This makes it possible to produce programs that are suited to different cultures and regions.

[0106] The television channel operation system can also continuously update the programming schedule based on real-time viewing data of viewers. For example, it can extend the duration of a program that has attracted increasing interest from viewers. It can also dynamically adjust the programming schedule based on real-time viewing data. It can also end a program that has attracted less interest from viewers early and start the next program early. This makes it possible to update the programming schedule based on real-time viewing data of viewers.

[0107] The television channel operating system can also estimate the viewer's emotions and adjust the content and timing of advertisements based on the emotions. For example, advertisements with a relaxing effect can be placed during times when the viewer is relaxed. The system can also monitor the viewer's emotion scores in real time and adjust the timing of advertisements based on the viewer's emotions. Furthermore, it can place trailers for action movies during times when the viewer is excited. This makes it possible to adjust the content and timing of advertisements based on the viewer's emotions.

[0108] The television channel operating system can also analyze the viewer's past search history and provide relevant advertisements. For example, it can place advertisements related to products the viewer has searched for. It can also customize the content of advertisements based on the viewer's search history. It can also advertise products and services that the viewer may be interested in. This makes it possible to provide relevant advertisements based on the viewer's past search history.

[0109] The television channel operating system can also estimate the viewer's emotions and dynamically set advertising rates based on the emotions. For example, it can set higher advertising rates during times when the viewer is excited. It can also monitor the viewer's emotion scores in real time and adjust advertising rates based on the viewer's emotions. It can also set lower advertising rates during times when the viewer is relaxed. This makes it possible to dynamically set advertising rates based on the viewer's emotions.

[0110] The television channel operation system can also analyze viewers' long-term viewing patterns and predict trends. For example, it can identify the genres that viewers have been watching over a long period of time. It can also predict long-term trends based on viewing history. It can also identify programs that viewers tend to watch in specific seasons. This makes it possible to predict trends based on viewers' long-term viewing patterns.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The program production department analyzes viewer interests and trends and plans and produces programs. For example, the generative AI analyzes viewers' past viewing history and social media trends to select popular themes and content. The generative AI can also generate program scripts and footage based on viewer interests. Step 2: The program scheduling unit analyzes viewers' viewing patterns and optimizes program scheduling. For example, the generation AI places popular programs during the times when viewers watch the most. The generation AI can also create program scheduling schedules based on viewers' viewing patterns. Step 3: The advertising management unit analyzes the advertiser's needs and viewer interests and optimizes the ad slots. For example, the generation AI places ads that are effective for a specific viewer demographic. The generation AI can also optimize the placement of ad slots based on the advertiser's needs and viewer interests. Step 4: The viewer analysis unit analyzes viewer reactions and feedback to improve programs and advertisements. For example, the generation AI analyzes viewer comments and ratings to identify areas for improvement in program content. The generation AI can also make improvement suggestions based on viewer reactions and feedback.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 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 system equipped with a generative AI, The Program Production Department analyzes viewer interests and trends and plans and produces programs. a program scheduling unit that analyzes viewers' viewing patterns and optimizes program scheduling; The advertising management department analyzes advertisers' needs and viewers' interests and optimizes advertising space. and an audience analysis unit that analyzes audience reactions and feedback to improve programs and advertisements. A system characterized by:

2. The program production department Analyzing the viewers' past viewing history and social media trends to select popular themes and content 2. The system of claim 1.

3. The program planning unit Place the popular program during the time slot when the viewer is most likely to watch it.

2. The system of claim 1.

4. The advertisement management unit Placing the advertisements effectively for specific audiences 2. The system of claim 1.

5. The audience analysis unit Analyzing the viewers' comments and ratings and identifying areas for improvement in the program content 2. The system of claim 1.

Images