system

Abstract

The system according to this embodiment aims to analyze viewer data and generate hit scripts. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects viewer data. The analysis unit analyzes the viewer data collected by the collection unit and extracts the factors for success. The generation unit generates a script based on the factors extracted by the analysis unit. The provision unit provides the script generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, generating a script that hits based on viewer data has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze viewer data and generate a script that hits.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects viewer data. The analysis unit analyzes the viewer data collected by the collection unit and extracts factors for hitting. The generation unit generates a script based on the factors extracted by the analysis unit. The provision unit provides the script generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze viewer data and generate hit scripts. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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 l2 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 device 32. The processor 28, the RAM 30, and the storage device 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 device 50. The processor 46, the RAM 48, and the storage device 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The script generation system according to an embodiment of the present invention is a system that uses AI to learn the story, structure, character settings, and direction of recently successful dramas, and then generates scripts that will attract viewers' interest based on that learning. The script generation system uses AI to analyze viewer data and genre trends, extract the factors that led to success, and proposes unique original script ideas and settings. The AI ​​can also consider the progression of the story and the growth patterns of the characters, enabling it to create stories that resonate with viewers. For example, the script generation system uses AI to analyze past viewer data and genre trends. In this process, it collects detailed data such as the age group, gender, viewing time, and number of views of viewers. For example, by collecting data such as genres preferred by specific age groups and time slots with high viewing numbers, the AI ​​can understand the interests and concerns of viewers. This allows it to extract the factors that led to success. Next, based on the factors extracted by the AI, it proposes unique original script ideas and settings. For example, if a particular character setting or story development is popular with viewers, the AI ​​generates a script idea that incorporates those elements. This makes it possible to create original scripts that will attract viewers' interest. Furthermore, the AI ​​considers the progression of the story and the growth patterns of the characters to create stories that resonate with viewers. For example, the AI ​​generates scenes that show characters overcoming difficulties and growing as individuals, as well as scenes that viewers can easily empathize with. This allows for the creation of stories that resonate with viewers. This mechanism makes it possible to generate engaging scripts that incorporate elements from recently successful dramas. By analyzing viewer data and genre trends and extracting the factors behind success, the AI ​​can create original scripts that capture viewers' interest. It also considers the progression of the story and the growth patterns of the characters to create stories that resonate with viewers. This makes it possible to provide dramas that are compelling to viewers. As a result, the script generation system can automatically generate and provide scripts that capture viewers' interest.

[0029] The script generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects viewer data. Viewer data includes, but is not limited to, viewing history, viewing time, and viewing device. The collection unit can collect data such as the viewer's age group, gender, viewing time, and number of views. For example, the collection unit can categorize the viewer's age group into teenagers, 20s, 30s, etc., and collect viewing data for each age group. The collection unit can also categorize the viewer's gender into male, female, and other genders, and collect viewing data for each gender. Furthermore, the collection unit can categorize the viewing time into morning, noon, and night, etc., and collect viewing data for each time slot. For example, the collection unit can categorize the number of views into one, two, multiple times, etc., and collect data for each number of views. The analysis unit analyzes the viewer data collected by the collection unit and extracts the factors for success. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis department extracts patterns from viewer data using data mining techniques. The analysis department can also analyze viewer data using statistical analysis techniques to understand viewer interests and preferences. Furthermore, the analysis department can analyze viewer data using machine learning algorithms to extract factors contributing to a show's success. For example, the analysis department can identify preferred genres and themes based on viewer viewing history. The generation department generates scripts based on the factors extracted by the analysis department. Generation can, for example, create scripts incorporating specific character settings or story developments. For example, the generation department can create scripts incorporating character settings that are popular with viewers. It can also create scripts incorporating story developments that are popular with viewers. Furthermore, the generation department can create scripts considering the progression of the story and character growth patterns. For example, the generation department can create scripts depicting characters overcoming difficulties and growing as individuals. The distribution department provides the scripts generated by the generation department. Distribution may, but is not limited to, online streaming, television broadcasting, or DVD sales.For example, the provider can provide the generated script to viewers through an online distribution platform. Alternatively, the provider can provide the generated script to viewers through television broadcasting. Furthermore, the provider can sell the generated script as a DVD. Thus, the script generation system according to this embodiment can automatically generate and provide scripts that will interest viewers.

[0030] The data collection unit collects viewer data. Viewer data includes, but is not limited to, viewing history, viewing time, and viewing device. The data collection unit can collect data such as viewer age group, gender, viewing time, and number of views. Specifically, it can categorize viewer age groups into 10s, 20s, 30s, etc., and collect viewing data for each age group. This allows for a detailed understanding of what kind of content specific age groups prefer. The data collection unit can also categorize viewer gender into male, female, and other genders and collect viewing data for each gender. This allows for analysis of viewing trends by gender and enables the provision of content tailored to target audiences. Furthermore, the data collection unit can categorize viewing time into morning, noon, and night, etc., and collect viewing data for each time slot. For example, it can identify trends such as news and information programs being preferred in the morning and dramas and movies being preferred in the evening. The number of views can also be categorized into once, twice, multiple times, etc., and data for each number of views can be collected. This allows for analysis of how often specific content is watched repeatedly and the reasons for this. The data collection unit can centrally manage and update this data in real time. For example, data is updated every time a viewer watches new content, allowing for a constant understanding of the latest viewing trends. Furthermore, the data collection unit can flexibly change the data collection methods. For instance, if new devices or platforms emerge, data collection functions corresponding to them can be added. This allows the data collection unit to efficiently collect diverse viewer data and improve the overall system performance.

[0031] The analysis department analyzes viewer data collected by the data collection department to extract the factors behind hits. Analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. Specifically, data mining techniques are used to extract patterns from viewer data. For example, genres and themes preferred by specific age groups or genders can be identified. Statistical analysis techniques can also be used to analyze viewer data and understand viewer interests. For example, viewing trends by time of day can be analyzed to identify what kind of content is preferred at what time. Furthermore, machine learning algorithms can be used to analyze viewer data and extract the factors behind hits. For example, genres and themes preferred by viewers can be identified based on viewing history, and new content can be suggested accordingly. By combining these techniques, the analysis department can gain a detailed understanding of viewer interests and identify the factors behind hits. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term trends. For example, based on viewing data from the past few years, fluctuations in the popularity of specific genres and themes can be predicted, which can be used to plan future content. This allows the analytics department to gain a detailed understanding of viewers' interests and preferences, identify the factors behind hits, and thereby improve the overall performance of the system.

[0032] The generation unit generates scripts based on factors extracted by the analysis unit. For example, it can generate scripts incorporating specific character settings and story developments. Specifically, it can generate scripts with character settings favored by viewers. For instance, it can create scripts that reflect the personality and background of characters preferred by viewers. It can also generate scripts with story developments favored by viewers. For example, it can create scripts that incorporate dramatic plot developments and suspense elements preferred by viewers. Furthermore, the generation unit can generate scripts considering the progression of the story and character growth patterns. For example, it can generate scripts depicting the process of characters overcoming difficulties and growing. The generation unit can combine these elements to automatically generate scripts that capture the viewer's interest. Additionally, the generation unit can optimize the script generation process using AI technology. For example, it can use natural language processing technology to generate scripts that incorporate language and expressions preferred by viewers. The generation unit can also revise scripts based on viewer feedback to provide scripts that better suit viewer interests. This allows the generation unit to efficiently generate scripts that capture viewer interest and improve the overall system performance.

[0033] The distribution unit provides the scripts generated by the generation unit. Distribution is carried out by methods such as online distribution, television broadcasting, and DVD sales, but is not limited to these examples. Specifically, the generated scripts are provided to viewers through online distribution platforms. For example, streaming services can be used to allow viewers to watch the scripts anytime, anywhere. The distribution unit can also provide the generated scripts to viewers through television broadcasting. For example, the scripts can be broadcast at specific times so that viewers can watch them in real time. Furthermore, the distribution unit can sell the generated scripts as DVDs. For example, DVDs containing bonus footage and making-of footage can be sold and provided to viewers. The distribution unit can combine these methods to provide viewers with a variety of viewing options. In addition, the distribution unit can collect viewer feedback and use it to improve the distribution method. For example, the distribution method for the next time can be optimized based on viewers' viewing history and ratings. This allows the distribution unit to efficiently provide scripts that interest viewers and improve the overall performance of the system.

[0034] The data collection unit can collect data such as the age group, gender, viewing time, and number of views of viewers. For example, the data collection unit can categorize viewers by age group (e.g., teenagers, 20s, 30s) and collect viewing data for each age group. The data collection unit can also categorize viewers by gender (male, female, other) and collect viewing data for each gender. The data collection unit can also categorize viewers by viewing time (e.g., morning, noon, evening) and collect viewing data for each time slot. The data collection unit can categorize viewers by number of views (e.g., once, twice, multiple times) and collect data for each number of views. This allows for more accurate analysis by collecting detailed viewer data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the age group, gender, viewing time, and number of views of viewers into an AI, which can then automatically collect the data.

[0035] The analysis department can analyze collected viewer data to understand viewer interests and preferences. For example, the analysis department can extract patterns from viewer data using data mining techniques. The analysis department can also analyze viewer data using statistical analysis techniques to understand viewer interests and preferences. The analysis department can also analyze viewer data using machine learning algorithms to understand viewer interests and preferences. By understanding viewer interests and preferences, it becomes possible to generate scripts that are more tailored to the viewer. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input viewer data into AI, which can automatically analyze the data and understand viewer interests and preferences.

[0036] The generation unit can generate scripts that incorporate specific character settings and story developments. For example, the generation unit can generate scripts that incorporate character settings that are popular with viewers. The generation unit can also generate scripts that incorporate story developments that are popular with viewers. The generation unit can also generate scripts that take into account the progression of the story and the growth patterns of the characters. This allows the generation unit to generate scripts that incorporate character settings and story developments that are popular with viewers. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input character settings and story developments that are popular with viewers into an AI, and the AI ​​can automatically generate a script.

[0037] The generation unit can generate scripts while considering the progression of the story and the growth patterns of the characters. For example, the generation unit can generate scripts that depict the process of a character overcoming difficulties and growing as a person. The generation unit can also generate scripts that incorporate scenes that viewers can easily empathize with. This makes it possible to generate stories that resonate with viewers. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the progression of the story and the growth patterns of the characters into an AI, and the AI ​​can automatically generate a script.

[0038] The distribution unit can provide the generated script to viewers. The distribution unit can provide the generated script to viewers, for example, through an online distribution platform. The distribution unit can also provide the generated script to viewers through television broadcasting. The distribution unit can also sell the generated script as a DVD. This allows the distribution unit to attract viewers' interest by providing them with the generated script. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the generated script into an AI, and the AI ​​can automatically provide it to viewers.

[0039] The data collection unit can analyze viewers' past viewing history and select the optimal data collection method. For example, the data collection unit can analyze trends in genres and programs that viewers have watched in the past and prioritize the collection of relevant data. If viewers tend to watch during specific time slots, the data collection unit can concentrate data collection during those times. The data collection unit can also focus on collecting data on programs that viewers have previously given high ratings to. This allows the system to select the optimal data collection method based on viewers' past viewing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input viewers' past viewing history into an AI, which can then automatically select the optimal data collection method.

[0040] The data collection unit can filter viewer data based on the viewer's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data relevant to the viewer's current lifestyle. The data collection unit can also collect data related to specific genres or themes based on the viewer's areas of interest. The data collection unit can also adjust the timing of data collection based on the viewer's lifestyle and areas of interest. This allows for the priority collection of relevant data based on the viewer's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the viewer's lifestyle and areas of interest into an AI, which can then automatically filter the data collection.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the viewer's geographical location when collecting viewer data. For example, if a viewer lives in a specific region, the data collection unit can prioritize the collection of data on popular programs and genres in that region. The data collection unit can also analyze region-specific viewing trends based on the viewer's geographical location and collect relevant data. If a viewer is traveling, the data collection unit can also collect data on popular programs in the region they are visiting. This allows for the priority collection of highly relevant data based on the viewer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the viewer's geographical location into AI, which can then automatically prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze viewers' social media activity and collect relevant data when collecting viewer data. For example, the data collection unit can collect relevant data based on the programs and genres that viewers share on social media. The data collection unit can also analyze viewers' comments and ratings on social media to understand viewing trends and collect data. The data collection unit can also collect relevant data based on the accounts and groups that viewers follow. This allows for the collection of relevant data based on viewers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input viewers' social media activity into AI, and the AI ​​can automatically collect relevant data.

[0043] The analysis unit can select the optimal analysis algorithm by referring to the viewer's past viewing history during analysis. For example, the analysis unit can analyze the genres and program trends that the viewer has watched in the past and select a relevant algorithm. The analysis unit can also select an algorithm suitable for a specific time of day or day of the week based on the viewer's viewing history. The analysis unit can also select the optimal analysis algorithm based on data of programs that the viewer has given high ratings to. In this way, the optimal analysis algorithm can be selected based on the viewer's past viewing history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal analysis algorithm.

[0044] The analysis unit can perform analysis while considering viewer attribute information (age, gender, etc.). For example, the analysis unit can analyze data related to a specific genre or theme based on the viewer's age group. The analysis unit can also analyze viewing trends and extract relevant data based on the viewer's gender. The analysis unit can also select an analysis method suitable for a specific attribute group based on the viewer's attribute information. This allows for more accurate analysis based on the viewer's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input viewer attribute information into AI, and the AI ​​can perform the analysis automatically.

[0045] The analysis unit can perform analyses while considering the geographical distribution of viewers. For example, if viewers live in a specific region, the analysis unit can analyze data on popular programs and genres in that region. The analysis unit can also analyze region-specific viewing trends based on the geographical distribution of viewers. If viewers are traveling, the analysis unit can also analyze data on popular programs in the region they are visiting. This allows for the analysis of region-specific viewing trends based on the geographical distribution of viewers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical distribution data of viewers into AI, and the AI ​​can perform the analysis automatically.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the viewer's relevant literature and past viewing data during the analysis process. For example, the analysis unit can perform analysis by referring to relevant literature based on data of programs the viewer has watched in the past. The analysis unit can also refer to literature related to specific genres or themes based on the viewer's viewing history. The analysis unit can also select algorithms to improve the accuracy of the analysis based on the viewer's past viewing data. This allows the analysis to improve accuracy by referring to the viewer's relevant literature and past viewing data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the viewer's relevant literature and past viewing data into AI, which can then perform the analysis automatically.

[0047] The generation unit can select the optimal script generation algorithm by referring to the viewer's past viewing history when generating a script. For example, the generation unit can analyze the genres and program trends the viewer has watched in the past and select a relevant algorithm. The generation unit can also select an algorithm suitable for a specific time slot or day of the week based on the viewer's viewing history. The generation unit can also select the optimal script generation algorithm based on data of programs that the viewer has given high ratings to. In this way, the optimal script generation algorithm can be selected based on the viewer's past viewing history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal script generation algorithm.

[0048] The generation unit can generate scripts while considering the audience's attribute information (age, gender, etc.). For example, the generation unit can generate scripts related to a specific genre or theme based on the audience's age group. The generation unit can also analyze viewing trends based on the audience's gender and generate relevant scripts. The generation unit can also generate scripts suitable for a specific attribute group based on the audience's attribute information. This allows for the generation of scripts that are more tailored to the audience based on their attribute information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the audience's attribute information into AI, and the AI ​​can automatically generate scripts.

[0049] The generation unit can generate the optimal script by considering the viewer's geographical location information during script generation. For example, if the viewer lives in a specific region, the generation unit will generate a script related to genres and themes popular in that region. The generation unit can also generate scripts that reflect region-specific viewing trends based on the viewer's geographical location information. If the viewer is traveling, the generation unit can also generate a script related to genres and themes popular in the region they are visiting. This allows the generation of scripts that reflect region-specific viewing trends based on the viewer's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the viewer's geographical location information into AI, and the AI ​​can automatically generate the optimal script.

[0050] The generation unit can analyze viewers' social media activity and adjust the script content during script generation. For example, the generation unit can generate relevant scripts based on programs and genres that viewers have shared on social media. The generation unit can also analyze viewers' comments and ratings on social media and generate scripts that reflect their viewing trends. The generation unit can also generate relevant scripts based on accounts and groups that viewers follow. This allows for the generation of relevant scripts based on viewers' social media activity. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input viewers' social media activity into AI, which can then automatically adjust the script content.

[0051] The content provider can select the optimal content delivery method by referring to the viewer's past viewing history when providing scripts. For example, the content provider can analyze the genres and program trends that the viewer has watched in the past and provide relevant scripts. The content provider can also provide scripts suitable for specific time slots or days of the week based on the viewer's viewing history. The content provider can also select the optimal content delivery method based on data of programs that viewers have given high ratings to. This allows the content provider to select the optimal content delivery method based on the viewer's past viewing history. Some or all of the above processes in the content provider may be performed using AI, for example, or not using AI. For example, the content provider can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal content delivery method.

[0052] The distribution department can select the optimal distribution method when providing scripts, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the distribution department can provide a script adapted to the screen size. If the viewer is using a tablet, the distribution department can also provide a script optimized for a larger screen. If the viewer is using a smartwatch, the distribution department can also provide a concise and highly visible script. This allows the distribution department to select the optimal distribution method based on the viewer's device information. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input the viewer's device information into an AI, which can then automatically select the optimal distribution method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can analyze viewers' social media activity and collect data based on the programs and comments they share. For example, if a viewer leaves many comments about a particular program, the unit will prioritize collecting data related to that program. It can also collect relevant data based on the genre and theme of the programs viewers share. Furthermore, it can understand viewing trends and collect data based on the accounts and groups viewers follow. This allows for the collection of more relevant data based on viewers' social media activity.

[0055] The analytics department can analyze data while considering the geographical distribution of viewers, enabling them to understand region-specific viewing trends. For example, they can analyze popular genres and themes in a particular region and generate scripts tailored to the audience in that area. Furthermore, if viewers are traveling, they can analyze data on popular programs in their destination region to understand viewing trends there. They can also generate scripts that reflect region-specific cultures and customs based on the geographical distribution of viewers. This allows them to provide scripts that take region-specific viewing trends into account.

[0056] The generation unit can refer to the viewer's past viewing history to generate scripts that incorporate character settings and story developments preferred by the viewer. For example, it can create new characters based on character settings that viewers have previously given high ratings to. It can also generate scripts that incorporate story developments preferred by the viewer. Furthermore, it can generate scripts related to specific genres or themes based on the viewer's viewing history. This allows for the provision of scripts that are more tailored to the viewer based on their past viewing history.

[0057] The content provider can select the optimal delivery method by considering the viewer's device information. For example, if the viewer is using a smartphone, they can provide a script adapted to the screen size. If the viewer is using a tablet, they can provide a script optimized for a larger screen. Furthermore, if the viewer is using a smartwatch, they can provide a concise and highly visible script. This allows for the selection of the optimal delivery method based on the viewer's device information.

[0058] The content delivery department can select the optimal delivery method by referring to viewers' past viewing history. For example, it can analyze the genres and program trends that viewers have watched in the past and provide relevant scripts. Based on viewers' viewing history, it can also provide scripts suitable for specific time slots or days of the week. Furthermore, it can select the optimal delivery method based on data of programs that viewers have given high ratings to. In this way, the optimal delivery method can be selected based on viewers' past viewing history.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects viewer data. Viewer data includes viewing history, viewing time, viewing device, age group, gender, viewing time, and number of views. For example, the data collection unit categorizes viewers by age group (teens, 20s, 30s, etc.) and collects viewing data for each age group. It can also categorize viewers by gender (male, female, other) and collect viewing data for each gender. Furthermore, it can categorize viewing time (morning, noon, evening, etc.) and collect viewing data for each time slot. It can also categorize the number of views (1 time, 2 times, multiple times, etc.) and collect data for each number of views. Step 2: The analysis department analyzes the viewer data collected by the data collection department and extracts the factors contributing to its success. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns from viewer data, and statistical analysis techniques are used to understand the interests and concerns of viewers. Furthermore, machine learning algorithms are used to analyze viewer data and identify genres and themes that viewers prefer. Step 3: The generation unit generates a script based on the factors extracted by the analysis unit. The generation unit can generate scripts that incorporate specific character settings and story developments. For example, it can generate scripts that incorporate character settings and story developments that are popular with viewers, and generate scripts that take into account the progression of the story and the growth patterns of the characters. Step 4: The Provider provides the script generated by the Producer. The provision can be done through methods such as online distribution, television broadcasting, or DVD sales. For example, the generated script can be provided to viewers through an online distribution platform or through television broadcasting. Furthermore, the generated script can be sold as a DVD.

[0061] (Example of form 2) The script generation system according to an embodiment of the present invention is a system that uses AI to learn the story, structure, character settings, and direction of recently successful dramas, and then generates scripts that will attract viewers' interest based on that learning. The script generation system uses AI to analyze viewer data and genre trends, extract the factors that led to success, and proposes unique original script ideas and settings. The AI ​​can also consider the progression of the story and the growth patterns of the characters, enabling it to create stories that resonate with viewers. For example, the script generation system uses AI to analyze past viewer data and genre trends. In this process, it collects detailed data such as the age group, gender, viewing time, and number of views of viewers. For example, by collecting data such as genres preferred by specific age groups and time slots with high viewing numbers, the AI ​​can understand the interests and concerns of viewers. This allows it to extract the factors that led to success. Next, based on the factors extracted by the AI, it proposes unique original script ideas and settings. For example, if a particular character setting or story development is popular with viewers, the AI ​​generates a script idea that incorporates those elements. This makes it possible to create original scripts that will attract viewers' interest. Furthermore, the AI ​​considers the progression of the story and the growth patterns of the characters to create stories that resonate with viewers. For example, the AI ​​generates scenes that show characters overcoming difficulties and growing as individuals, as well as scenes that viewers can easily empathize with. This allows for the creation of stories that resonate with viewers. This mechanism makes it possible to generate engaging scripts that incorporate elements from recently successful dramas. By analyzing viewer data and genre trends and extracting the factors behind success, the AI ​​can create original scripts that capture viewers' interest. It also considers the progression of the story and the growth patterns of the characters to create stories that resonate with viewers. This makes it possible to provide dramas that are compelling to viewers. As a result, the script generation system can automatically generate and provide scripts that capture viewers' interest.

[0062] The script generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects viewer data. Viewer data includes, but is not limited to, viewing history, viewing time, and viewing device. The collection unit can collect data such as the viewer's age group, gender, viewing time, and number of views. For example, the collection unit can categorize the viewer's age group into teenagers, 20s, 30s, etc., and collect viewing data for each age group. The collection unit can also categorize the viewer's gender into male, female, and other genders, and collect viewing data for each gender. Furthermore, the collection unit can categorize the viewing time into morning, noon, and night, etc., and collect viewing data for each time slot. For example, the collection unit can categorize the number of views into one, two, multiple times, etc., and collect data for each number of views. The analysis unit analyzes the viewer data collected by the collection unit and extracts the factors for success. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis department extracts patterns from viewer data using data mining techniques. The analysis department can also analyze viewer data using statistical analysis techniques to understand viewer interests and preferences. Furthermore, the analysis department can analyze viewer data using machine learning algorithms to extract factors contributing to a show's success. For example, the analysis department can identify preferred genres and themes based on viewer viewing history. The generation department generates scripts based on the factors extracted by the analysis department. Generation can, for example, create scripts incorporating specific character settings or story developments. For example, the generation department can create scripts incorporating character settings that are popular with viewers. It can also create scripts incorporating story developments that are popular with viewers. Furthermore, the generation department can create scripts considering the progression of the story and character growth patterns. For example, the generation department can create scripts depicting characters overcoming difficulties and growing as individuals. The distribution department provides the scripts generated by the generation department. Distribution may, but is not limited to, online streaming, television broadcasting, or DVD sales.For example, the provider can provide the generated script to viewers through an online distribution platform. Alternatively, the provider can provide the generated script to viewers through television broadcasting. Furthermore, the provider can sell the generated script as a DVD. Thus, the script generation system according to this embodiment can automatically generate and provide scripts that will interest viewers.

[0063] The data collection unit collects viewer data. Viewer data includes, but is not limited to, viewing history, viewing time, and viewing device. The data collection unit can collect data such as viewer age group, gender, viewing time, and number of views. Specifically, it can categorize viewer age groups into 10s, 20s, 30s, etc., and collect viewing data for each age group. This allows for a detailed understanding of what kind of content specific age groups prefer. The data collection unit can also categorize viewer gender into male, female, and other genders and collect viewing data for each gender. This allows for analysis of viewing trends by gender and enables the provision of content tailored to target audiences. Furthermore, the data collection unit can categorize viewing time into morning, noon, and night, etc., and collect viewing data for each time slot. For example, it can identify trends such as news and information programs being preferred in the morning and dramas and movies being preferred in the evening. The number of views can also be categorized into once, twice, multiple times, etc., and data for each number of views can be collected. This allows for analysis of how often specific content is watched repeatedly and the reasons for this. The data collection unit can centrally manage and update this data in real time. For example, data is updated every time a viewer watches new content, allowing for a constant understanding of the latest viewing trends. Furthermore, the data collection unit can flexibly change the data collection methods. For instance, if new devices or platforms emerge, data collection functions corresponding to them can be added. This allows the data collection unit to efficiently collect diverse viewer data and improve the overall system performance.

[0064] The analysis department analyzes viewer data collected by the data collection department to extract the factors behind hits. Analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. Specifically, data mining techniques are used to extract patterns from viewer data. For example, genres and themes preferred by specific age groups or genders can be identified. Statistical analysis techniques can also be used to analyze viewer data and understand viewer interests. For example, viewing trends by time of day can be analyzed to identify what kind of content is preferred at what time. Furthermore, machine learning algorithms can be used to analyze viewer data and extract the factors behind hits. For example, genres and themes preferred by viewers can be identified based on viewing history, and new content can be suggested accordingly. By combining these techniques, the analysis department can gain a detailed understanding of viewer interests and identify the factors behind hits. Additionally, the analysis department can utilize historical data and statistical information to analyze long-term trends. For example, based on viewing data from the past few years, fluctuations in the popularity of specific genres and themes can be predicted, which can be used to plan future content. This allows the analytics department to gain a detailed understanding of viewers' interests and preferences, identify the factors behind hits, and thereby improve the overall performance of the system.

[0065] The generation unit generates scripts based on factors extracted by the analysis unit. For example, it can generate scripts incorporating specific character settings and story developments. Specifically, it can generate scripts with character settings favored by viewers. For instance, it can create scripts that reflect the personality and background of characters preferred by viewers. It can also generate scripts with story developments favored by viewers. For example, it can create scripts that incorporate dramatic plot developments and suspense elements preferred by viewers. Furthermore, the generation unit can generate scripts considering the progression of the story and character growth patterns. For example, it can generate scripts depicting the process of characters overcoming difficulties and growing. The generation unit can combine these elements to automatically generate scripts that capture the viewer's interest. Additionally, the generation unit can optimize the script generation process using AI technology. For example, it can use natural language processing technology to generate scripts that incorporate language and expressions preferred by viewers. The generation unit can also revise scripts based on viewer feedback to provide scripts that better suit viewer interests. This allows the generation unit to efficiently generate scripts that capture viewer interest and improve the overall system performance.

[0066] The distribution unit provides the scripts generated by the generation unit. Distribution is carried out by methods such as online distribution, television broadcasting, and DVD sales, but is not limited to these examples. Specifically, the generated scripts are provided to viewers through online distribution platforms. For example, streaming services can be used to allow viewers to watch the scripts anytime, anywhere. The distribution unit can also provide the generated scripts to viewers through television broadcasting. For example, the scripts can be broadcast at specific times so that viewers can watch them in real time. Furthermore, the distribution unit can sell the generated scripts as DVDs. For example, DVDs containing bonus footage and making-of footage can be sold and provided to viewers. The distribution unit can combine these methods to provide viewers with a variety of viewing options. In addition, the distribution unit can collect viewer feedback and use it to improve the distribution method. For example, the distribution method for the next time can be optimized based on viewers' viewing history and ratings. This allows the distribution unit to efficiently provide scripts that interest viewers and improve the overall performance of the system.

[0067] The data collection unit can collect data such as the age group, gender, viewing time, and number of views of viewers. For example, the data collection unit can categorize viewers by age group (e.g., teenagers, 20s, 30s) and collect viewing data for each age group. The data collection unit can also categorize viewers by gender (male, female, other) and collect viewing data for each gender. The data collection unit can also categorize viewers by viewing time (e.g., morning, noon, evening) and collect viewing data for each time slot. The data collection unit can categorize viewers by number of views (e.g., once, twice, multiple times) and collect data for each number of views. This allows for more accurate analysis by collecting detailed viewer data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the age group, gender, viewing time, and number of views of viewers into an AI, which can then automatically collect the data.

[0068] The analysis department can analyze collected viewer data to understand viewer interests and preferences. For example, the analysis department can extract patterns from viewer data using data mining techniques. The analysis department can also analyze viewer data using statistical analysis techniques to understand viewer interests and preferences. The analysis department can also analyze viewer data using machine learning algorithms to understand viewer interests and preferences. By understanding viewer interests and preferences, it becomes possible to generate scripts that are more tailored to the viewer. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input viewer data into AI, which can automatically analyze the data and understand viewer interests and preferences.

[0069] The generation unit can generate scripts that incorporate specific character settings and story developments. For example, the generation unit can generate scripts that incorporate character settings that are popular with viewers. The generation unit can also generate scripts that incorporate story developments that are popular with viewers. The generation unit can also generate scripts that take into account the progression of the story and the growth patterns of the characters. This allows the generation unit to generate scripts that incorporate character settings and story developments that are popular with viewers. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input character settings and story developments that are popular with viewers into an AI, and the AI ​​can automatically generate a script.

[0070] The generation unit can generate scripts while considering the progression of the story and the growth patterns of the characters. For example, the generation unit can generate scripts that depict the process of a character overcoming difficulties and growing as a person. The generation unit can also generate scripts that incorporate scenes that viewers can easily empathize with. This makes it possible to generate stories that resonate with viewers. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the progression of the story and the growth patterns of the characters into an AI, and the AI ​​can automatically generate a script.

[0071] The distribution unit can provide the generated script to viewers. The distribution unit can provide the generated script to viewers, for example, through an online distribution platform. The distribution unit can also provide the generated script to viewers through television broadcasting. The distribution unit can also sell the generated script as a DVD. This allows the distribution unit to attract viewers' interest by providing them with the generated script. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the generated script into an AI, and the AI ​​can automatically provide it to viewers.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of viewer data collection based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will collect viewer data more frequently to obtain detailed data. If the user is stressed, the data collection unit can also reduce the frequency of data collection to lessen the user's burden. If the user is excited, the data collection unit can also focus on collecting data from specific events or scenes. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then automatically adjust the timing of data collection.

[0073] The data collection unit can analyze viewers' past viewing history and select the optimal data collection method. For example, the data collection unit can analyze trends in genres and programs that viewers have watched in the past and prioritize the collection of relevant data. If viewers tend to watch during specific time slots, the data collection unit can concentrate data collection during those times. The data collection unit can also focus on collecting data on programs that viewers have previously given high ratings to. This allows the system to select the optimal data collection method based on viewers' past viewing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input viewers' past viewing history into an AI, which can then automatically select the optimal data collection method.

[0074] The data collection unit can filter viewer data based on the viewer's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data relevant to the viewer's current lifestyle. The data collection unit can also collect data related to specific genres or themes based on the viewer's areas of interest. The data collection unit can also adjust the timing of data collection based on the viewer's lifestyle and areas of interest. This allows for the priority collection of relevant data based on the viewer's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the viewer's lifestyle and areas of interest into an AI, which can then automatically filter the data collection.

[0075] The data collection unit can estimate the user's emotions and determine the priority of viewer data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed viewing history and rating data. If the user is stressed, the data collection unit may also collect only basic viewing data to reduce the burden. If the user is excited, the data collection unit may also prioritize collecting data related to specific scenes or characters. This allows for the collection of more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then automatically determine the data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the viewer's geographical location when collecting viewer data. For example, if a viewer lives in a specific region, the data collection unit can prioritize the collection of data on popular programs and genres in that region. The data collection unit can also analyze region-specific viewing trends based on the viewer's geographical location and collect relevant data. If a viewer is traveling, the data collection unit can also collect data on popular programs in the region they are visiting. This allows for the priority collection of highly relevant data based on the viewer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the viewer's geographical location into AI, which can then automatically prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze viewers' social media activity and collect relevant data when collecting viewer data. For example, the data collection unit can collect relevant data based on the programs and genres that viewers share on social media. The data collection unit can also analyze viewers' comments and ratings on social media to understand viewing trends and collect data. The data collection unit can also collect relevant data based on the accounts and groups that viewers follow. This allows for the collection of relevant data based on viewers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input viewers' social media activity into AI, and the AI ​​can automatically collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method of viewer data based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform detailed data analysis to delve deeper into viewing trends. If the user is stressed, the analysis unit can perform only basic data analysis and provide results quickly. If the user is excited, the analysis unit can perform data analysis that focuses on specific scenes or characters. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI, which can then automatically adjust the analysis method.

[0079] The analysis unit can select the optimal analysis algorithm by referring to the viewer's past viewing history during analysis. For example, the analysis unit can analyze the genres and program trends that the viewer has watched in the past and select a relevant algorithm. The analysis unit can also select an algorithm suitable for a specific time of day or day of the week based on the viewer's viewing history. The analysis unit can also select the optimal analysis algorithm based on data of programs that the viewer has given high ratings to. In this way, the optimal analysis algorithm can be selected based on the viewer's past viewing history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal analysis algorithm.

[0080] The analysis unit can perform analysis while considering viewer attribute information (age, gender, etc.). For example, the analysis unit can analyze data related to a specific genre or theme based on the viewer's age group. The analysis unit can also analyze viewing trends and extract relevant data based on the viewer's gender. The analysis unit can also select an analysis method suitable for a specific attribute group based on the viewer's attribute information. This allows for more accurate analysis based on the viewer's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input viewer attribute information into AI, and the AI ​​can perform the analysis automatically.

[0081] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and delve deeper into viewing trends. If the user is stressed, the analysis unit can also provide only basic analysis results and display the results quickly. If the user is excited, the analysis unit can also display analysis results that focus on specific scenes or characters. This allows for more appropriate results by adjusting how the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can automatically adjust how the analysis results are displayed.

[0082] The analysis unit can perform analyses while considering the geographical distribution of viewers. For example, if viewers live in a specific region, the analysis unit can analyze data on popular programs and genres in that region. The analysis unit can also analyze region-specific viewing trends based on the geographical distribution of viewers. If viewers are traveling, the analysis unit can also analyze data on popular programs in the region they are visiting. This allows for the analysis of region-specific viewing trends based on the geographical distribution of viewers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical distribution data of viewers into AI, and the AI ​​can perform the analysis automatically.

[0083] The analysis unit can improve the accuracy of its analysis by referring to the viewer's relevant literature and past viewing data during the analysis process. For example, the analysis unit can perform analysis by referring to relevant literature based on data of programs the viewer has watched in the past. The analysis unit can also refer to literature related to specific genres or themes based on the viewer's viewing history. The analysis unit can also select algorithms to improve the accuracy of the analysis based on the viewer's past viewing data. This allows the analysis to improve accuracy by referring to the viewer's relevant literature and past viewing data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the viewer's relevant literature and past viewing data into AI, which can then perform the analysis automatically.

[0084] The generation unit can estimate the user's emotions and adjust the script generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a script that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a script that emphasizes the shortest route. If the user is excited, the generation unit can also generate a script with visually stimulating effects. In this way, by adjusting the script generation method according to the user's emotions, a script that is more suitable for the audience can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI, and the AI ​​can automatically adjust the script generation method.

[0085] The generation unit can select the optimal script generation algorithm by referring to the viewer's past viewing history when generating a script. For example, the generation unit can analyze the genres and program trends the viewer has watched in the past and select a relevant algorithm. The generation unit can also select an algorithm suitable for a specific time slot or day of the week based on the viewer's viewing history. The generation unit can also select the optimal script generation algorithm based on data of programs that the viewer has given high ratings to. In this way, the optimal script generation algorithm can be selected based on the viewer's past viewing history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal script generation algorithm.

[0086] The generation unit can generate scripts while considering the audience's attribute information (age, gender, etc.). For example, the generation unit can generate scripts related to a specific genre or theme based on the audience's age group. The generation unit can also analyze viewing trends based on the audience's gender and generate relevant scripts. The generation unit can also generate scripts suitable for a specific attribute group based on the audience's attribute information. This allows for the generation of scripts that are more tailored to the audience based on their attribute information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the audience's attribute information into AI, and the AI ​​can automatically generate scripts.

[0087] The generation unit can estimate the user's emotions and determine the priority of the scripts to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize generating detailed scripts. If the user is in a hurry, the generation unit may also prioritize generating concise scripts. If the user is excited, the generation unit may also prioritize generating scripts with visually stimulating effects. This allows for the generation of more important scripts by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without AI. For example, the generation unit can input user emotion data into an AI, which can then automatically determine the script priorities.

[0088] The generation unit can generate the optimal script by considering the viewer's geographical location information during script generation. For example, if the viewer lives in a specific region, the generation unit will generate a script related to genres and themes popular in that region. The generation unit can also generate scripts that reflect region-specific viewing trends based on the viewer's geographical location information. If the viewer is traveling, the generation unit can also generate a script related to genres and themes popular in the region they are visiting. This allows the generation of scripts that reflect region-specific viewing trends based on the viewer's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the viewer's geographical location information into AI, and the AI ​​can automatically generate the optimal script.

[0089] The generation unit can analyze viewers' social media activity and adjust the script content during script generation. For example, the generation unit can generate relevant scripts based on programs and genres that viewers have shared on social media. The generation unit can also analyze viewers' comments and ratings on social media and generate scripts that reflect their viewing trends. The generation unit can also generate relevant scripts based on accounts and groups that viewers follow. This allows for the generation of relevant scripts based on viewers' social media activity. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input viewers' social media activity into AI, which can then automatically adjust the script content.

[0090] The delivery unit can estimate the user's emotions and adjust the way the script is delivered based on the estimated emotions. For example, if the user is relaxed, the delivery unit can provide a detailed script to capture the viewer's interest. If the user is in a hurry, the delivery unit can also provide a concise script that gets straight to the point. If the user is excited, the delivery unit can also provide a script with visually stimulating effects. This allows for the delivery of scripts that are more relevant to the viewer by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into an AI, and the AI ​​can automatically adjust the way the script is delivered.

[0091] The content provider can select the optimal content delivery method by referring to the viewer's past viewing history when providing scripts. For example, the content provider can analyze the genres and program trends that the viewer has watched in the past and provide relevant scripts. The content provider can also provide scripts suitable for specific time slots or days of the week based on the viewer's viewing history. The content provider can also select the optimal content delivery method based on data of programs that viewers have given high ratings to. This allows the content provider to select the optimal content delivery method based on the viewer's past viewing history. Some or all of the above processes in the content provider may be performed using AI, for example, or not using AI. For example, the content provider can input the viewer's past viewing history into AI, and the AI ​​can automatically select the optimal content delivery method.

[0092] The service provider can estimate the user's emotions and determine the order in which scripts are presented based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize providing detailed scripts. If the user is in a hurry, the service provider may also prioritize providing concise scripts. If the user is excited, the service provider may also prioritize providing scripts with visually stimulating effects. This allows for the prioritization of more important scripts by determining the order in which scripts are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, which can then automatically determine the order in which scripts are presented.

[0093] The distribution department can select the optimal distribution method when providing scripts, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the distribution department can provide a script adapted to the screen size. If the viewer is using a tablet, the distribution department can also provide a script optimized for a larger screen. If the viewer is using a smartwatch, the distribution department can also provide a concise and highly visible script. This allows the distribution department to select the optimal distribution method based on the viewer's device information. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input the viewer's device information into an AI, which can then automatically select the optimal distribution method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The script generation system can further estimate the user's emotions and adjust the tone and tempo of the script based on those emotions. For example, if the user is relaxed, it can generate a script that proceeds in a calm tone. If the user is excited, it can generate a script that includes tense scenes and sudden plot twists. Furthermore, if the user is sad, it can generate a script that includes touching scenes and encouraging messages. By providing scripts that match the user's emotions, it can evoke deeper empathy.

[0096] The data collection unit can analyze viewers' social media activity and collect data based on the programs and comments they share. For example, if a viewer leaves many comments about a particular program, the unit will prioritize collecting data related to that program. It can also collect relevant data based on the genre and theme of the programs viewers share. Furthermore, it can understand viewing trends and collect data based on the accounts and groups viewers follow. This allows for the collection of more relevant data based on viewers' social media activity.

[0097] The analytics department can analyze data while considering the geographical distribution of viewers, enabling them to understand region-specific viewing trends. For example, they can analyze popular genres and themes in a particular region and generate scripts tailored to the audience in that area. Furthermore, if viewers are traveling, they can analyze data on popular programs in their destination region to understand viewing trends there. They can also generate scripts that reflect region-specific cultures and customs based on the geographical distribution of viewers. This allows them to provide scripts that take region-specific viewing trends into account.

[0098] The generation unit can refer to the viewer's past viewing history to generate scripts that incorporate character settings and story developments preferred by the viewer. For example, it can create new characters based on character settings that viewers have previously given high ratings to. It can also generate scripts that incorporate story developments preferred by the viewer. Furthermore, it can generate scripts related to specific genres or themes based on the viewer's viewing history. This allows for the provision of scripts that are more tailored to the viewer based on their past viewing history.

[0099] The content provider can select the optimal delivery method by considering the viewer's device information. For example, if the viewer is using a smartphone, they can provide a script adapted to the screen size. If the viewer is using a tablet, they can provide a script optimized for a larger screen. Furthermore, if the viewer is using a smartwatch, they can provide a concise and highly visible script. This allows for the selection of the optimal delivery method based on the viewer's device information.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of viewer data collection based on those estimates. For example, if the user is relaxed, viewer data can be collected more frequently to obtain detailed data. If the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Furthermore, if the user is excited, data can be collected focusing on specific events or scenes. This allows for the collection of more relevant data by adjusting the timing of data collection according to the user's emotions.

[0101] The analytics department can estimate the user's emotions and adjust the analysis method of viewer data based on those estimated emotions. For example, if the user is relaxed, it can perform a detailed data analysis to delve deeper into viewing trends. If the user is stressed, it can perform only basic data analysis and provide results quickly. Furthermore, if the user is excited, it can perform data analysis that focuses on specific scenes or characters. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained.

[0102] The generation unit can estimate the user's emotions and adjust the script generation method based on those emotions. For example, if the user is relaxed, it can generate a script that proceeds at a leisurely pace. If the user is in a hurry, it can generate a script that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a script with visually stimulating effects. In this way, by adjusting the script generation method according to the user's emotions, it is possible to generate scripts that are more relevant to the audience.

[0103] The delivery unit can estimate the user's emotions and adjust the way the script is delivered based on those estimates. For example, if the user is relaxed, a detailed script can be provided to capture the viewer's interest. If the user is in a hurry, a concise script that gets straight to the point can be provided. Furthermore, if the user is excited, a script with visually stimulating effects can be provided. In this way, by adjusting the way the script is delivered according to the user's emotions, a script that is more relevant to the viewer can be delivered.

[0104] The content delivery department can select the optimal delivery method by referring to viewers' past viewing history. For example, it can analyze the genres and program trends that viewers have watched in the past and provide relevant scripts. Based on viewers' viewing history, it can also provide scripts suitable for specific time slots or days of the week. Furthermore, it can select the optimal delivery method based on data of programs that viewers have given high ratings to. In this way, the optimal delivery method can be selected based on viewers' past viewing history.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects viewer data. Viewer data includes viewing history, viewing time, viewing device, age group, gender, viewing time, and number of views. For example, the data collection unit categorizes viewers by age group (teens, 20s, 30s, etc.) and collects viewing data for each age group. It can also categorize viewers by gender (male, female, other) and collect viewing data for each gender. Furthermore, it can categorize viewing time (morning, noon, evening, etc.) and collect viewing data for each time slot. It can also categorize the number of views (1 time, 2 times, multiple times, etc.) and collect data for each number of views. Step 2: The analysis department analyzes the viewer data collected by the data collection department and extracts the factors contributing to its success. The analysis is carried out using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns from viewer data, and statistical analysis techniques are used to understand the interests and concerns of viewers. Furthermore, machine learning algorithms are used to analyze viewer data and identify genres and themes that viewers prefer. Step 3: The generation unit generates a script based on the factors extracted by the analysis unit. The generation unit can generate scripts that incorporate specific character settings and story developments. For example, it can generate scripts that incorporate character settings and story developments that are popular with viewers, and generate scripts that take into account the progression of the story and the growth patterns of the characters. Step 4: The Provider provides the script generated by the Producer. The provision can be done through methods such as online distribution, television broadcasting, or DVD sales. For example, the generated script can be provided to viewers through an online distribution platform or through television broadcasting. Furthermore, the generated script can be sold as a DVD.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects viewer data using the control unit 46A of the smart device 14 and analyzes the viewer data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the viewer data using the identification processing unit 290 of the data processing unit 12 and extracts the factors for success. The generation unit generates a script using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the script generated by the control unit 46A of the smart device 14 to the viewer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects viewer data using the control unit 46A of the smart glasses 214 and analyzes the viewer data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the viewer data using the identification processing unit 290 of the data processing unit 12 and extracts the factors for success. The generation unit generates a script using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the script generated by the control unit 46A of the smart glasses 214 to the viewer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects viewer data using the control unit 46A of the headset terminal 314 and analyzes the viewer data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the viewer data using the identification processing unit 290 of the data processing unit 12 and extracts the factors for success. The generation unit generates a script using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the script generated by the control unit 46A of the headset terminal 314 to the viewer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects viewer data using the control unit 46A of the robot 414 and analyzes the viewer data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the viewer data using the identification processing unit 290 of the data processing unit 12 and extracts the factors for success. The generation unit generates a script using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the script generated by the control unit 46A of the robot 414 to the viewer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) The data collection department collects viewer data, The analysis unit analyzes viewer data collected by the aforementioned collection unit and extracts the factors contributing to its success. A generation unit that generates a script based on the factors extracted by the analysis unit, The system comprises a providing unit that provides the script generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as the age range, gender, viewing time, and number of views of our viewers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is By analyzing collected viewer data, we can understand viewers' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a script that incorporates specific character settings and story developments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate a script while considering the progression of the story and the growth patterns of the characters. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated script to the audience. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user emotions and adjust the timing of viewer data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze viewers' past viewing history to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting viewer data, filtering is performed based on the viewer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes the audience data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting viewer data, the system prioritizes collecting highly relevant data by considering the viewer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting viewer data, analyze viewers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of audience data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, the optimal analysis algorithm is selected by referring to the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When performing analysis, the analysis will take into account the demographic information of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of the audience will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to viewers' relevant literature and past viewing data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the script generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During script generation, the system selects the optimal script generation algorithm by referencing the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a script, consider the audience's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the scripts to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a script, the system takes into account the viewer's geographical location to create the optimal script. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During script generation, we analyze viewers' social media activity and adjust the script content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the script is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing scripts, the optimal delivery method is selected by referring to the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which scripts are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the script, the optimal delivery method will be selected considering the viewer's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects viewer data, The analysis unit analyzes viewer data collected by the aforementioned collection unit and extracts the factors contributing to its success. A generation unit that generates a script based on the factors extracted by the analysis unit, The system comprises a providing unit that provides the script generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as the age range, gender, viewing time, and number of views of our viewers. The system according to feature 1.

3. The aforementioned analysis unit is By analyzing collected viewer data, we can understand viewers' interests and concerns. The system according to feature 1.

4. The generating unit is Generate a script that incorporates specific character settings and story developments. The system according to feature 1.

5. The generating unit is Generate a script while considering the progression of the story and the growth patterns of the characters. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated script to the audience. The system according to feature 1.

7. The aforementioned collection unit is We estimate user emotions and adjust the timing of viewer data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze viewers' past viewing history to select the most suitable data collection method. The system according to feature 1.

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