System

Abstract

An object of the system according to the embodiment is to generate characters and stories that are rich in creativity and innovation using AI technology.SOLUTION: A system includes a generation unit, a generation unit, and a construction unit. The generation unit generates the personality and appearance of the character. The generation unit generates a story on the basis of information about the character generated by the generation unit. The construction unit constructs a screenplay based on the character and the story generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, characters and stories are generated manually, which limits creativity and innovation.

[0005] The system according to the embodiment aims to generate characters and stories that are rich in creativity and innovation using AI technology. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a creation unit, and a construction unit. The generation unit generates the personality and appearance of a character. The generation unit generates a story based on information about the character generated by the generation unit. The construction unit constructs a script based on the character and story generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate highly creative and innovative characters and stories using AI technology. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A drama production system according to an embodiment of the present invention automatically generates character personalities, appearances, stories, and scripts, enabling the production of highly creative and innovative dramas. The drama production system generates character personalities and appearances, generates a story based on the generated character information, and constructs a script based on the generated characters and story. For example, the drama production system generates basic information about the characters, such as their age, gender, occupation, and hobbies. Furthermore, it generates details about the character's appearance, such as hair color, eye shape, and clothing. Next, the drama production system generates the character's background, the setting of the story, and major events. Furthermore, it generates elements such as the relationships, conflicts, and cooperation between characters. Finally, the drama production system creates specific lines and scenes while taking into account the generated character's personalities and backgrounds. This frees the system from the constraints of predictable plots and actors, expanding the possibilities for new stories. This enables the drama production system to produce highly creative and innovative dramas. This allows the drama production system to produce dramas starring fictional characters that do not exist in reality. For example, it is possible to create characters that cannot be portrayed by real actors and settings that do not exist in reality. This will provide a new experience for viewers.

[0029] A drama production system according to an embodiment includes a generation unit, a generation unit, and a construction unit. The generation unit generates a character's personality and appearance. The generation unit generates basic information such as the character's age, gender, occupation, and hobbies. The generation unit can also generate details such as the character's hair color, eye shape, and clothing. The generation unit can also generate a character's background, a setting for the story, and major events. For example, the generation unit randomly generates a character's age. The generation unit can also randomly generate a character's gender. The generation unit can also randomly generate a character's occupation. The generation unit can randomly generate a character's hair color. The generation unit can also randomly generate a character's eye shape. The generation unit can also randomly generate a character's clothing. The generation unit randomly generates a character's background. The generation unit can also randomly generate a setting for the story. The generation unit can also randomly generate major events. The generation unit generates elements such as relationships, conflicts, and cooperation between characters. The generation unit generates, for example, friendships between characters. The generation unit can also generate antagonistic relationships between characters. The generation unit can also generate cooperative relationships between characters. The construction unit constructs a script based on the characters and story generated by the generation unit. The construction unit creates specific lines and scenes, for example, while taking into consideration the personalities and backgrounds of the generated characters. The construction unit creates lines based on the characters' personalities, for example. The construction unit can also create scenes based on the characters' backgrounds. The construction unit can also create lines and scenes while taking into consideration the characters' personalities and backgrounds. As a result, the drama production system according to the embodiment automatically generates character personalities, appearances, stories, and scripts, enabling the production of dramas that are rich in creativity and innovation.

[0030] The generation unit can generate basic information such as the character's age, gender, occupation, and hobbies. The generation unit, for example, randomly generates the character's age. The generation unit, for example, randomly generates the character's age. The generation unit can also randomly generate the character's gender. The generation unit can also randomly generate the character's occupation. The generation unit can randomly generate the character's hobbies. The generation unit can thus generate the character's basic information, thereby specifying the character's settings. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the character's age, gender, occupation, and hobbies.

[0031] The generation unit can generate details of the character's hair color, eye shape, and clothing. The generation unit, for example, randomly generates the character's hair color. The generation unit, for example, randomly generates the character's hair color. The generation unit can also randomly generate the character's eye shape. The generation unit can also randomly generate the character's clothing. The generation unit can randomly generate the character's hair color. The generation unit can also randomly generate the character's eye shape. The generation unit can also randomly generate the character's clothing. In this way, by generating details of the character's appearance, the character's visual is embodied. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the character's hair color, eye shape, and clothing.

[0032] The generation unit can generate character backgrounds, story settings, and events. The generation unit, for example, randomly generates character backgrounds. The generation unit, for example, randomly generates character backgrounds. The generation unit can also randomly generate story settings. The generation unit can also randomly generate major events. The generation unit randomly generates character backgrounds. The generation unit can also randomly generate story settings. The generation unit can also randomly generate major events. In this way, by generating character backgrounds and story settings, the general flow of the story is determined. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI, for example. For example, the generation unit can cause a generation AI to generate character backgrounds, story settings, and major events.

[0033] The generation unit can generate elements of relationships, conflicts, and cooperation between characters. The generation unit, for example, generates friendships between characters. The generation unit, for example, generates friendships between characters. The generation unit can also generate hostile relationships between characters. The generation unit can also generate cooperative relationships between characters. The generation unit can generate friendships between characters. The generation unit can also generate hostile relationships between characters. The generation unit can also generate cooperative relationships between characters. In this way, by generating relationships between characters, the development of the story becomes richer. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate relationships, conflicts, and cooperation between characters.

[0034] The construction unit can create specific lines and scenes based on the personality and background of the character generated by the AI. The construction unit, for example, creates lines based on the character's personality. The construction unit, for example, creates lines based on the character's personality. The construction unit can also create scenes based on the character's background. The construction unit can also create lines and scenes while taking into account the character's personality and background. The construction unit can create lines based on the character's personality. The construction unit can also create scenes based on the character's background. The construction unit can also create lines and scenes while taking into account the character's personality and background. In this way, by taking into account the character's personality and background, specific lines and scenes are created, and the details of the story are concretized. Some or all of the above-mentioned processing in the construction unit may be performed using AI, for example, or may be performed without using AI. For example, the construction unit can cause the generation AI to create lines and scenes based on the character's personality and background.

[0035] When generating a character, the generation unit can optimize the generation algorithm by referring to data on past popular characters. For example, the generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. For example, the generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. The generation unit can also reference appearance data of past popular characters to generate a visually attractive character. The generation unit can also optimize the character's background setting based on story data of past popular characters. The generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. The generation unit can also reference appearance data of past popular characters to generate a visually attractive character. The generation unit can also optimize the character's background setting based on story data of past popular characters. In this way, by referring to the data of past popular characters, the generation algorithm is optimized and an attractive character is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of past popular characters into the generation AI and cause the generation AI to optimize the generation algorithm.

[0036] When generating a character, the generation unit can analyze the user's past viewing history and generate a character that matches the user's preferences. For example, the generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. For example, the generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. The generation unit can also generate a character that matches a specific genre from the user's viewing history. The generation unit can also generate a character with a preferred appearance and personality based on the user's viewing history. The generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. The generation unit can also generate a character that matches a specific genre from the user's viewing history. The generation unit can also generate a character with a preferred appearance and personality based on the user's viewing history. In this way, a character that matches the user's preferences is generated by analyzing the user's past viewing history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's viewing history into the generation AI and cause the generation AI to generate a character that matches the user's preferences.

[0037] When generating a character, the generation unit can set the character based on a specific culture or historical background. For example, the generation unit generates a character with clothing and accessories based on a specific culture. For example, the generation unit generates a character with clothing and accessories based on a specific culture. The generation unit can also generate a character with a personality and occupation appropriate for the era, taking the historical background into consideration. The generation unit can also generate a character with speech and behavior patterns that reflect the cultural background. The generation unit generates a character with clothing and accessories based on a specific culture. The generation unit can also generate a character with a personality and occupation appropriate for the era, taking the historical background into consideration. The generation unit can also generate a character with speech and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive character is generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can cause a generation AI to set the character based on a specific culture or historical background.

[0038] When generating a character, the generation unit can generate a region-specific character based on the user's geographical location information. For example, if the user is in Japan, the generation unit generates a character that reflects Japanese culture and customs. For example, if the user is in Japan, the generation unit generates a character that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a character that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a character that reflects European culture and customs. For example, if the user is in Japan, the generation unit can generate a character that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a character that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a character that reflects European culture and customs. In this way, a region-specific character is generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate a region-specific character.

[0039] When generating a character, the generation unit can analyze the user's social media activity and generate a related character. The generation unit, for example, generates a character based on a theme that the user frequently mentions on social media. The generation unit, for example, generates a character based on a theme that the user frequently mentions on social media. The generation unit can also generate a related character by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's social media posts and generate a character that the user is likely to be interested in. The generation unit generates a character based on a theme that the user frequently mentions on social media. The generation unit can also generate a related character by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's social media posts and generate a character that the user is likely to be interested in. In this way, a related character is generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate a related character.

[0040] When generating a character, the generation unit can customize the generation method by reflecting the user's past feedback. For example, the generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. For example, the generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. The generation unit can also generate a new character by avoiding the characteristics of a character for which the user has given negative feedback in the past. The generation unit can also fine-tune the character's personality and appearance based on the user's feedback. The generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. The generation unit can also generate a new character by avoiding the characteristics of a character for which the user has given negative feedback in the past. The generation unit can also fine-tune the character's personality and appearance based on the user's feedback. In this way, by reflecting the user's past feedback, the generation method is customized and a character that more closely matches the user's preferences is generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the generation method.

[0041] When generating a story, the generation unit can optimize the generation algorithm by referring to data on past popular stories. For example, the generation unit analyzes plot data of past popular stories to generate a new story with common elements. For example, the generation unit analyzes plot data of past popular stories to generate a new story with common elements. The generation unit can also reference character data of past popular stories to generate a story including attractive characters. The generation unit can also generate a story with an interesting setting based on setting data of past popular stories. The generation unit analyzes plot data of past popular stories to generate a new story with common elements. The generation unit can also reference character data of past popular stories to generate a story including attractive characters. The generation unit can also generate a story with an interesting setting based on setting data of past popular stories. In this way, by referring to the data of past popular stories, the generation algorithm is optimized and an attractive story is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input data of past popular stories into the generation AI and cause the generation AI to optimize the generation algorithm.

[0042] When generating a story, the generation unit can analyze the user's past viewing history and generate a story that matches the user's preferences. The generation unit can, for example, analyze the plots of dramas the user has viewed in the past and generate a new story that matches the user's preferences. The generation unit can also generate a story that matches a specific genre from the user's viewing history. The generation unit can also generate a story with preferred characters and settings based on the user's viewing history. The generation unit can analyze the plots of dramas the user has viewed in the past and generate a new story that matches the user's preferences. The generation unit can also generate a story that matches a specific genre from the user's viewing history. The generation unit can also generate a story with preferred characters and settings based on the user's viewing history. In this way, a story that matches the user's preferences is generated by analyzing the user's past viewing history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's viewing history into the generation AI and cause the generation AI to generate a story that matches the user's preferences.

[0043] When generating a story, the generation unit can set the story based on a specific culture or historical background. For example, the generation unit generates a story including a setting or events based on a specific culture. For example, the generation unit generates a story including a setting or events based on a specific culture. The generation unit can also generate a story with a plot and characters appropriate for the era, taking the historical background into consideration. The generation unit can also generate a story including characters with language and behavior patterns that reflect the cultural background. The generation unit generates a story with a setting or events based on a specific culture. The generation unit can also generate a story with a plot and characters appropriate for the era, taking the historical background into consideration. The generation unit can also generate a story including characters with language and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive story is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to set the story based on a specific culture or historical background.

[0044] When generating a story, the generation unit can generate a region-specific story based on the user's geographical location information. For example, if the user is in Japan, the generation unit generates a story that reflects Japanese culture and customs. For example, if the user is in Japan, the generation unit generates a story that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a story that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a story that reflects European culture and customs. Furthermore, if the user is in Japan, the generation unit can generate a story that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a story that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a story that reflects European culture and customs. In this way, a region-specific story is generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate a region-specific story.

[0045] When generating a story, the generation unit can analyze the user's social media activity and generate a related story. The generation unit, for example, generates a story based on a theme that the user often mentions on social media. The generation unit, for example, generates a story based on a theme that the user often mentions on social media. The generation unit can also generate a related story by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's posts on social media and generate a story that is likely to be interesting. The generation unit generates a story based on a theme that the user often mentions on social media. The generation unit can also generate a related story by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's posts on social media and generate a story that is likely to be interesting. In this way, a related story is generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate a related story.

[0046] The generation unit can customize the generation method by reflecting the user's past feedback when generating a story. For example, the generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. For example, the generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. The generation unit can also generate a new story by avoiding features of stories for which the user gave negative feedback in the past. The generation unit can also fine-tune the story plot and characters based on the user's feedback. The generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. The generation unit can also generate a new story by avoiding features of stories for which the user gave negative feedback in the past. The generation unit can also fine-tune the story plot and characters based on the user's feedback. In this way, the generation method is customized by reflecting the user's past feedback, and a story that is more suited to the user's preferences is generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the generation method.

[0047] When constructing a script, the construction unit can optimize the construction algorithm by referring to data on past popular scripts. The construction unit, for example, analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit, for example, analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit can also reference character data of past popular scripts to construct a script including attractive characters. The construction unit can also construct a script with an interesting setting based on setting data of past popular scripts. The construction unit analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit can also reference character data of past popular scripts to construct a script including attractive characters. The construction unit can also construct a script with an interesting setting based on setting data of past popular scripts. In this way, by referring to the data of past popular scripts, the construction algorithm is optimized and an attractive script is constructed. Some or all of the above-mentioned processing in the construction unit may be performed, for example, using AI or without using AI. For example, the construction department can input data on past popular scripts into the generation AI and have the generation AI optimize the construction algorithm.

[0048] When constructing a script, the construction unit can analyze the user's past viewing history and construct a script that suits the user's preferences. The construction unit, for example, analyzes the plots of dramas the user has viewed in the past and constructs a new script that suits the user's preferences. The construction unit, for example, analyzes the plots of dramas the user has viewed in the past and constructs a new script that suits the user's preferences. The construction unit can also construct a script that suits a specific genre from the user's viewing history. The construction unit can also construct a script with preferred characters and settings based on the user's viewing history. The construction unit can analyze the plots of dramas the user has viewed in the past and construct a new script that suits the user's preferences. The construction unit can also construct a script that suits a specific genre from the user's viewing history. The construction unit can also construct a script with preferred characters and settings based on the user's viewing history. In this way, a script that suits the user's preferences is constructed by analyzing the user's past viewing history. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's viewing history into a generation AI and cause the generation AI to construct a script that suits the user's preferences.

[0049] When constructing a script, the construction unit can set the script based on a specific culture or historical background. For example, the construction unit constructs a script that includes a setting and events based on a specific culture. For example, the construction unit constructs a script that includes a setting and events based on a specific culture. The construction unit can also construct a script that has a plot and characters that fit the era, taking the historical background into consideration. The construction unit can also construct a script that includes characters with language and behavior patterns that reflect the cultural background. The construction unit constructs a script that includes a setting and events based on a specific culture. The construction unit can also construct a script that has a plot and characters that fit the era, taking the historical background into consideration. The construction unit can also construct a script that includes characters with language and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive script is constructed. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can cause a generation AI to set the script based on a specific culture or historical background.

[0050] When constructing a script, the construction unit can construct a region-specific script based on the user's geographical location information. For example, if the user is in Japan, the construction unit constructs a script that reflects Japanese culture and customs. For example, if the user is in Japan, the construction unit constructs a script that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the construction unit can construct a script that reflects American culture and customs. Furthermore, if the user is in Europe, the construction unit can construct a script that reflects European culture and customs. Furthermore, if the user is in Japan, the construction unit can construct a script that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the construction unit can construct a script that reflects American culture and customs. Furthermore, if the user is in Europe, the construction unit can construct a script that reflects European culture and customs. In this way, a region-specific script is constructed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the construction unit may be performed using AI, for example, or may be performed without using AI. For example, the construction unit can input the user's geographical location information to the generation AI and cause the generation AI to construct a region-specific script.

[0051] When constructing a script, the construction unit can analyze the user's social media activity and construct a related script. The construction unit, for example, constructs a script based on themes that the user frequently mentions on social media. The construction unit, for example, constructs a script based on themes that the user frequently mentions on social media. The construction unit can also construct a related script with reference to the user's friendships on social media. The construction unit can also analyze the content of the user's social media posts and construct a script that the user is likely to be interested in. The construction unit constructs a script based on themes that the user frequently mentions on social media. The construction unit can also construct a related script with reference to the user's friendships on social media. The construction unit can also analyze the content of the user's social media posts and construct a script that the user is likely to be interested in. In this way, a related script is constructed by analyzing the user's social media activity. Some or all of the above-mentioned processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's social media activity into a generation AI and cause the generation AI to construct a related script.

[0052] The construction unit can customize the construction method by reflecting the user's past feedback when constructing a script. For example, the construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. For example, the construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. The construction unit can also construct a new script by avoiding the features of scripts for which the user gave negative feedback in the past. The construction unit can also fine-tune the plot and characters of the script based on the user's feedback. The construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. The construction unit can also construct a new script by avoiding the features of scripts for which the user gave negative feedback in the past. The construction unit can also fine-tune the plot and characters of the script based on the user's feedback. In this way, the construction method is customized by reflecting the user's past feedback, and a script that is more suited to the user's preferences is constructed. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's past feedback into a generation AI and cause the generation AI to customize the construction method.

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

[0054] When generating the character's personality and appearance, the generation unit can analyze the user's past purchasing history and adjust the character's details based on that information. For example, the character's outfit can be determined based on fashion items purchased by the user in the past. The character's hobbies and personality can also be set based on the genres of books and movies purchased by the user. Furthermore, the character's living environment can be set based on interior items purchased by the user. In this way, a more personalized character can be generated by adjusting the character's personality and appearance based on the user's purchasing history.

[0055] When generating the character's personality and appearance, the generation unit can analyze the user's SNS activity times and set the character's lifestyle based on that information. For example, if the user is a nocturnal person, a character that is active at night can be generated. Conversely, if the user is a morning person, a character that is active in the morning can be generated. Furthermore, the character's sociability can be set based on the frequency of the user's SNS activity. In this way, a more personalized character can be generated by setting the character's lifestyle based on the user's SNS activity times.

[0056] When generating the character's personality and appearance, the generation unit can analyze the user's music playback history and set the character's hobbies and personality based on that information. For example, if the user often listens to rock music, a character with an energetic and adventurous spirit can be generated. If the user likes classical music, a character with a calm personality can be generated. Furthermore, if the user likes pop music, a cheerful and sociable character can be generated. In this way, by setting the character's hobbies and personality based on the user's music playback history, a more personalized character can be generated.

[0057] When generating the character's personality and appearance, the generation unit can analyze the user's past travel history and set the character's background and story setting based on that information. For example, the character's hometown can be set based on cities and countries the user has visited in the past. The story setting can also be set based on tourist spots the user has visited. Furthermore, the character's hobbies and special skills can be set based on activities the user has experienced. In this way, a more personalized story can be generated by setting the character's background and story setting based on the user's travel history.

[0058] When generating the character's personality and appearance, the generation unit can also analyze the user's past sports activity history and set the character's special skills and hobbies based on that information. For example, if the user played soccer in the past, the character can be set to be good at soccer. If the user played basketball in the past, the character can be set to be good at basketball. Furthermore, if the user swam in the past, the character can be set to be good at swimming. In this way, by setting the character's special skills and hobbies based on the user's sports activity history, a more personalized character can be generated.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The generator generates the character's personality and appearance. The generator generates basic information such as the character's age, gender, occupation, and hobbies, as well as details such as hair color, eye shape, and clothing. It also generates elements such as the character's background, the setting of the story, major events, and the relationships, conflicts, and cooperation between characters. Step 2: The generator generates a story based on the generated character information. The generator generates the story development and major events, taking into account the character's personality and background. Step 3: The construction department creates a script based on the characters and story generated by the generation department. The construction department creates specific lines and scenes, taking into account the personalities and backgrounds of the generated characters.

[0061] (Example 2) A drama production system according to an embodiment of the present invention automatically generates character personalities, appearances, stories, and scripts, enabling the production of highly creative and innovative dramas. The drama production system generates character personalities and appearances, generates a story based on the generated character information, and constructs a script based on the generated characters and story. For example, the drama production system generates basic information about the characters, such as their age, gender, occupation, and hobbies. Furthermore, it generates details about the character's appearance, such as hair color, eye shape, and clothing. Next, the drama production system generates the character's background, the setting of the story, and major events. Furthermore, it generates elements such as the relationships, conflicts, and cooperation between characters. Finally, the drama production system creates specific lines and scenes while taking into account the generated character's personalities and backgrounds. This frees the system from the constraints of predictable plots and actors, expanding the possibilities for new stories. This enables the drama production system to produce highly creative and innovative dramas. This allows the drama production system to produce dramas starring fictional characters that do not exist in reality. For example, it is possible to create characters that cannot be portrayed by real actors and settings that do not exist in reality. This will provide a new experience for viewers.

[0062] A drama production system according to an embodiment includes a generation unit, a generation unit, and a construction unit. The generation unit generates a character's personality and appearance. The generation unit generates basic information such as the character's age, gender, occupation, and hobbies. The generation unit can also generate details such as the character's hair color, eye shape, and clothing. The generation unit can also generate a character's background, a setting for the story, and major events. For example, the generation unit randomly generates a character's age. The generation unit can also randomly generate a character's gender. The generation unit can also randomly generate a character's occupation. The generation unit can randomly generate a character's hair color. The generation unit can also randomly generate a character's eye shape. The generation unit can also randomly generate a character's clothing. The generation unit randomly generates a character's background. The generation unit can also randomly generate a setting for the story. The generation unit can also randomly generate major events. The generation unit generates elements such as relationships, conflicts, and cooperation between characters. The generation unit generates, for example, friendships between characters. The generation unit can also generate antagonistic relationships between characters. The generation unit can also generate cooperative relationships between characters. The construction unit constructs a script based on the characters and story generated by the generation unit. The construction unit creates specific lines and scenes, for example, while taking into consideration the personalities and backgrounds of the generated characters. The construction unit creates lines based on the characters' personalities, for example. The construction unit can also create scenes based on the characters' backgrounds. The construction unit can also create lines and scenes while taking into consideration the characters' personalities and backgrounds. As a result, the drama production system according to the embodiment automatically generates character personalities, appearances, stories, and scripts, enabling the production of dramas that are rich in creativity and innovation.

[0063] The generation unit can generate basic information such as the character's age, gender, occupation, and hobbies. The generation unit, for example, randomly generates the character's age. The generation unit, for example, randomly generates the character's age. The generation unit can also randomly generate the character's gender. The generation unit can also randomly generate the character's occupation. The generation unit can randomly generate the character's hobbies. The generation unit can thus generate the character's basic information, thereby specifying the character's settings. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the character's age, gender, occupation, and hobbies.

[0064] The generation unit can generate details of the character's hair color, eye shape, and clothing. The generation unit, for example, randomly generates the character's hair color. The generation unit, for example, randomly generates the character's hair color. The generation unit can also randomly generate the character's eye shape. The generation unit can also randomly generate the character's clothing. The generation unit can randomly generate the character's hair color. The generation unit can also randomly generate the character's eye shape. The generation unit can also randomly generate the character's clothing. In this way, by generating details of the character's appearance, the character's visual is embodied. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the character's hair color, eye shape, and clothing.

[0065] The generation unit can generate character backgrounds, story settings, and events. The generation unit, for example, randomly generates character backgrounds. The generation unit, for example, randomly generates character backgrounds. The generation unit can also randomly generate story settings. The generation unit can also randomly generate major events. The generation unit randomly generates character backgrounds. The generation unit can also randomly generate story settings. The generation unit can also randomly generate major events. In this way, by generating character backgrounds and story settings, the general flow of the story is determined. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI, for example. For example, the generation unit can cause a generation AI to generate character backgrounds, story settings, and major events.

[0066] The generation unit can generate elements of relationships, conflicts, and cooperation between characters. The generation unit, for example, generates friendships between characters. The generation unit, for example, generates friendships between characters. The generation unit can also generate hostile relationships between characters. The generation unit can also generate cooperative relationships between characters. The generation unit can generate friendships between characters. The generation unit can also generate hostile relationships between characters. The generation unit can also generate cooperative relationships between characters. In this way, by generating relationships between characters, the development of the story becomes richer. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate relationships, conflicts, and cooperation between characters.

[0067] The construction unit can create specific lines and scenes based on the personality and background of the character generated by the AI. The construction unit, for example, creates lines based on the character's personality. The construction unit, for example, creates lines based on the character's personality. The construction unit can also create scenes based on the character's background. The construction unit can also create lines and scenes while taking into account the character's personality and background. The construction unit can create lines based on the character's personality. The construction unit can also create scenes based on the character's background. The construction unit can also create lines and scenes while taking into account the character's personality and background. In this way, by taking into account the character's personality and background, specific lines and scenes are created, and the details of the story are concretized. Some or all of the above-mentioned processing in the construction unit may be performed using AI, for example, or may be performed without using AI. For example, the construction unit can cause the generation AI to create lines and scenes based on the character's personality and background.

[0068] The generation unit can estimate the user's emotions and adjust the character's personality and appearance based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a character with a gentle and friendly personality. For example, if the user is relaxed, the generation unit generates a character with a gentle and friendly personality. For example, if the user is excited, the generation unit can generate a character with an adventurous personality. For example, if the user is sad, the generation unit can generate a character with a comforting and kind personality. For example, if the user is relaxed, the generation unit can generate a character with a gentle and friendly personality. For example, if the user is excited, the generation unit can generate a character with an adventurous personality. For example, if the user is sad, the generation unit can generate a character with a comforting and kind personality. In this way, by adjusting the character's personality and appearance based on the user's emotions, a more personalized character is generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause the generation AI to estimate the user's emotions and adjust the character's personality and appearance based on the estimated emotions.

[0069] When generating a character, the generation unit can optimize the generation algorithm by referring to data on past popular characters. For example, the generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. For example, the generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. The generation unit can also reference appearance data of past popular characters to generate a visually attractive character. The generation unit can also optimize the character's background setting based on story data of past popular characters. The generation unit analyzes personality data of past popular characters to generate a new character with common characteristics. The generation unit can also reference appearance data of past popular characters to generate a visually attractive character. The generation unit can also optimize the character's background setting based on story data of past popular characters. In this way, by referring to the data of past popular characters, the generation algorithm is optimized and an attractive character is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of past popular characters into the generation AI and cause the generation AI to optimize the generation algorithm.

[0070] When generating a character, the generation unit can analyze the user's past viewing history and generate a character that matches the user's preferences. For example, the generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. For example, the generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. The generation unit can also generate a character that matches a specific genre from the user's viewing history. The generation unit can also generate a character with a preferred appearance and personality based on the user's viewing history. The generation unit can analyze the characteristics of characters in dramas the user has watched in the past and generate a new character that matches the user's preferences. The generation unit can also generate a character that matches a specific genre from the user's viewing history. The generation unit can also generate a character with a preferred appearance and personality based on the user's viewing history. In this way, a character that matches the user's preferences is generated by analyzing the user's past viewing history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's viewing history into the generation AI and cause the generation AI to generate a character that matches the user's preferences.

[0071] When generating a character, the generation unit can set the character based on a specific culture or historical background. For example, the generation unit generates a character with clothing and accessories based on a specific culture. For example, the generation unit generates a character with clothing and accessories based on a specific culture. The generation unit can also generate a character with a personality and occupation appropriate for the era, taking the historical background into consideration. The generation unit can also generate a character with speech and behavior patterns that reflect the cultural background. The generation unit generates a character with clothing and accessories based on a specific culture. The generation unit can also generate a character with a personality and occupation appropriate for the era, taking the historical background into consideration. The generation unit can also generate a character with speech and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive character is generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can cause a generation AI to set the character based on a specific culture or historical background.

[0072] The generation unit can estimate the user's emotions and determine the character's behavior pattern based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates a character that behaves calmly and calmly. For example, when the user is relaxed, the generation unit generates a character that behaves calmly and calmly. For example, when the user is relaxed, the generation unit can generate a character that behaves calmly and calmly. For example, when the user is excited, the generation unit can generate a character that behaves aggressively and adventurously. For example, when the user is sad, the generation unit can generate a character that behaves in a comforting manner. For example, when the user is relaxed, the generation unit can generate a character that behaves calmly and calmly. For example, when the user is excited, the generation unit can generate a character that behaves aggressively and adventurously. For example, when the user is sad, the generation unit can generate a character that behaves in a comforting manner. In this way, by determining the character's behavior pattern based on the user's emotions, a more personalized character is generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause the generation AI to estimate the user's emotions and determine the character's behavior pattern based on the estimated emotions.

[0073] When generating a character, the generation unit can generate a region-specific character based on the user's geographical location information. For example, if the user is in Japan, the generation unit generates a character that reflects Japanese culture and customs. For example, if the user is in Japan, the generation unit generates a character that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a character that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a character that reflects European culture and customs. For example, if the user is in Japan, the generation unit can generate a character that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a character that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a character that reflects European culture and customs. In this way, a region-specific character is generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate a region-specific character.

[0074] When generating a character, the generation unit can analyze the user's social media activity and generate a related character. The generation unit, for example, generates a character based on a theme that the user frequently mentions on social media. The generation unit, for example, generates a character based on a theme that the user frequently mentions on social media. The generation unit can also generate a related character by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's social media posts and generate a character that the user is likely to be interested in. The generation unit generates a character based on a theme that the user frequently mentions on social media. The generation unit can also generate a related character by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's social media posts and generate a character that the user is likely to be interested in. In this way, a related character is generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate a related character.

[0075] When generating a character, the generation unit can customize the generation method by reflecting the user's past feedback. For example, the generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. For example, the generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. The generation unit can also generate a new character by avoiding the characteristics of a character for which the user has given negative feedback in the past. The generation unit can also fine-tune the character's personality and appearance based on the user's feedback. The generation unit generates a new character by reflecting the characteristics of a character for which the user has given favorable feedback in the past. The generation unit can also generate a new character by avoiding the characteristics of a character for which the user has given negative feedback in the past. The generation unit can also fine-tune the character's personality and appearance based on the user's feedback. In this way, by reflecting the user's past feedback, the generation method is customized and a character that more closely matches the user's preferences is generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the generation method.

[0076] The generation unit can estimate the user's emotions and adjust the development of the story based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a calm and heartwarming story. For example, if the user is relaxed, the generation unit generates a calm and heartwarming story. For example, if the user is relaxed, the generation unit can generate a calm and heartwarming story. For example, if the user is excited, the generation unit can generate a thrilling and action-packed story. For example, if the user is sad, the generation unit can generate a moving and comforting story. For example, if the user is relaxed, the generation unit can generate a calm and heartwarming story. For example, if the user is excited, the generation unit can generate a thrilling and action-packed story. For example, if the user is sad, the generation unit can generate a moving and comforting story. In this way, by adjusting the development of the story based on the user's emotions, a more personalized story is generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause the generation AI to estimate the user's emotions and adjust the development of the story based on the estimated emotions.

[0077] When generating a story, the generation unit can optimize the generation algorithm by referring to data on past popular stories. For example, the generation unit analyzes plot data of past popular stories to generate a new story with common elements. For example, the generation unit analyzes plot data of past popular stories to generate a new story with common elements. The generation unit can also reference character data of past popular stories to generate a story including attractive characters. The generation unit can also generate a story with an interesting setting based on setting data of past popular stories. The generation unit analyzes plot data of past popular stories to generate a new story with common elements. The generation unit can also reference character data of past popular stories to generate a story including attractive characters. The generation unit can also generate a story with an interesting setting based on setting data of past popular stories. In this way, by referring to the data of past popular stories, the generation algorithm is optimized and an attractive story is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input data of past popular stories into the generation AI and cause the generation AI to optimize the generation algorithm.

[0078] When generating a story, the generation unit can analyze the user's past viewing history and generate a story that matches the user's preferences. The generation unit can, for example, analyze the plots of dramas the user has viewed in the past and generate a new story that matches the user's preferences. The generation unit can also generate a story that matches a specific genre from the user's viewing history. The generation unit can also generate a story with preferred characters and settings based on the user's viewing history. The generation unit can analyze the plots of dramas the user has viewed in the past and generate a new story that matches the user's preferences. The generation unit can also generate a story that matches a specific genre from the user's viewing history. The generation unit can also generate a story with preferred characters and settings based on the user's viewing history. In this way, a story that matches the user's preferences is generated by analyzing the user's past viewing history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's viewing history into the generation AI and cause the generation AI to generate a story that matches the user's preferences.

[0079] When generating a story, the generation unit can set the story based on a specific culture or historical background. For example, the generation unit generates a story including a setting or events based on a specific culture. For example, the generation unit generates a story including a setting or events based on a specific culture. The generation unit can also generate a story with a plot and characters appropriate for the era, taking the historical background into consideration. The generation unit can also generate a story including characters with language and behavior patterns that reflect the cultural background. The generation unit generates a story with a setting or events based on a specific culture. The generation unit can also generate a story with a plot and characters appropriate for the era, taking the historical background into consideration. The generation unit can also generate a story including characters with language and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive story is generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to set the story based on a specific culture or historical background.

[0080] The generation unit can estimate the user's emotions and determine important events in the story based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a story including calm and heartwarming events. For example, if the user is relaxed, the generation unit generates a story including calm and heartwarming events. For example, if the user is excited, the generation unit can generate a story including thrilling and action-packed events. For example, if the user is sad, the generation unit can generate a story including moving and comforting events. For example, if the user is relaxed, the generation unit can generate a story including calm and heartwarming events. For example, if the user is excited, the generation unit can generate a story including thrilling and action-packed events. For example, if the user is sad, the generation unit can generate a story including moving and comforting events. In this way, by determining important events in the story based on the user's emotions, a more personalized story is generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause the generation AI to estimate the user's emotions and determine important events in the story based on the estimated emotions.

[0081] When generating a story, the generation unit can generate a region-specific story based on the user's geographical location information. For example, if the user is in Japan, the generation unit generates a story that reflects Japanese culture and customs. For example, if the user is in Japan, the generation unit generates a story that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a story that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a story that reflects European culture and customs. Furthermore, if the user is in Japan, the generation unit can generate a story that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the generation unit can generate a story that reflects American culture and customs. Furthermore, if the user is in Europe, the generation unit can generate a story that reflects European culture and customs. In this way, a region-specific story is generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to generate a region-specific story.

[0082] When generating a story, the generation unit can analyze the user's social media activity and generate a related story. The generation unit, for example, generates a story based on a theme that the user often mentions on social media. The generation unit, for example, generates a story based on a theme that the user often mentions on social media. The generation unit can also generate a related story by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's posts on social media and generate a story that is likely to be interesting. The generation unit generates a story based on a theme that the user often mentions on social media. The generation unit can also generate a related story by referring to the user's friendships on social media. The generation unit can also analyze the content of the user's posts on social media and generate a story that is likely to be interesting. In this way, a related story is generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate a related story.

[0083] The generation unit can customize the generation method by reflecting the user's past feedback when generating a story. For example, the generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. For example, the generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. The generation unit can also generate a new story by avoiding features of stories for which the user gave negative feedback in the past. The generation unit can also fine-tune the story plot and characters based on the user's feedback. The generation unit generates a new story by reflecting features of stories for which the user gave favorable feedback in the past. The generation unit can also generate a new story by avoiding features of stories for which the user gave negative feedback in the past. The generation unit can also fine-tune the story plot and characters based on the user's feedback. In this way, the generation method is customized by reflecting the user's past feedback, and a story that is more suited to the user's preferences is generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the generation method.

[0084] The construction unit can estimate the user's emotions and adjust the way the script is expressed based on the estimated user's emotions. For example, if the user is relaxed, the construction unit constructs a script using calm and heartwarming expressions. For example, if the user is relaxed, the construction unit constructs a script using calm and heartwarming expressions. For example, if the user is relaxed, the construction unit constructs a script using calm and heartwarming expressions. Furthermore, if the user is excited, the construction unit can construct a script using thrilling and action-packed expressions. Furthermore, if the user is sad, the construction unit can construct a script using moving and comforting expressions. For example, if the user is relaxed, the construction unit constructs a script using calm and heartwarming expressions. Furthermore, if the user is excited, the construction unit can construct a script using thrilling and action-packed expressions. Furthermore, if the user is sad, the construction unit can construct a script using moving and comforting expressions. In this way, by adjusting the way the script is expressed based on the user's emotions, a more personalized script is constructed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit may cause the generation AI to estimate the user's emotions and adjust the way the script is expressed based on the estimated emotions.

[0085] When constructing a script, the construction unit can optimize the construction algorithm by referring to data on past popular scripts. The construction unit, for example, analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit, for example, analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit can also reference character data of past popular scripts to construct a script including attractive characters. The construction unit can also construct a script with an interesting setting based on setting data of past popular scripts. The construction unit analyzes plot data of past popular scripts and constructs a new script with common elements. The construction unit can also reference character data of past popular scripts to construct a script including attractive characters. The construction unit can also construct a script with an interesting setting based on setting data of past popular scripts. In this way, by referring to the data of past popular scripts, the construction algorithm is optimized and an attractive script is constructed. Some or all of the above-mentioned processing in the construction unit may be performed, for example, using AI or without using AI. For example, the construction department can input data on past popular scripts into the generation AI and have the generation AI optimize the construction algorithm.

[0086] When constructing a script, the construction unit can analyze the user's past viewing history and construct a script that suits the user's preferences. The construction unit, for example, analyzes the plots of dramas the user has viewed in the past and constructs a new script that suits the user's preferences. The construction unit, for example, analyzes the plots of dramas the user has viewed in the past and constructs a new script that suits the user's preferences. The construction unit can also construct a script that suits a specific genre from the user's viewing history. The construction unit can also construct a script with preferred characters and settings based on the user's viewing history. The construction unit can analyze the plots of dramas the user has viewed in the past and construct a new script that suits the user's preferences. The construction unit can also construct a script that suits a specific genre from the user's viewing history. The construction unit can also construct a script with preferred characters and settings based on the user's viewing history. In this way, a script that suits the user's preferences is constructed by analyzing the user's past viewing history. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's viewing history into a generation AI and cause the generation AI to construct a script that suits the user's preferences.

[0087] When constructing a script, the construction unit can set the script based on a specific culture or historical background. For example, the construction unit constructs a script that includes a setting and events based on a specific culture. For example, the construction unit constructs a script that includes a setting and events based on a specific culture. The construction unit can also construct a script that has a plot and characters that fit the era, taking the historical background into consideration. The construction unit can also construct a script that includes characters with language and behavior patterns that reflect the cultural background. The construction unit constructs a script that includes a setting and events based on a specific culture. The construction unit can also construct a script that has a plot and characters that fit the era, taking the historical background into consideration. The construction unit can also construct a script that includes characters with language and behavior patterns that reflect the cultural background. In this way, by taking the specific culture and historical background into consideration, a more realistic and attractive script is constructed. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can cause a generation AI to set the script based on a specific culture or historical background.

[0088] The construction unit can estimate the user's emotions and determine important scenes in the script based on the estimated user's emotions. For example, if the user is relaxed, the construction unit constructs a script including calm and heartwarming scenes. For example, if the user is relaxed, the construction unit constructs a script including calm and heartwarming scenes. For example, if the user is excited, the construction unit can construct a script including thrilling and action-packed scenes. For example, if the user is sad, the construction unit can construct a script including moving and comforting scenes. For example, if the user is relaxed, the construction unit constructs a script including calm and heartwarming scenes. For example, if the user is excited, the construction unit can construct a script including thrilling and action-packed scenes. For example, if the user is sad, the construction unit can construct a script including moving and comforting scenes. In this way, by determining important scenes in the script based on the user's emotions, a more personalized script is constructed. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit may cause the generation AI to estimate the user's emotions and determine important scenes in the script based on the estimated emotions.

[0089] When constructing a script, the construction unit can construct a region-specific script based on the user's geographical location information. For example, if the user is in Japan, the construction unit constructs a script that reflects Japanese culture and customs. For example, if the user is in Japan, the construction unit constructs a script that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the construction unit can construct a script that reflects American culture and customs. Furthermore, if the user is in Europe, the construction unit can construct a script that reflects European culture and customs. Furthermore, if the user is in Japan, the construction unit can construct a script that reflects Japanese culture and customs. Furthermore, if the user is in the United States, the construction unit can construct a script that reflects American culture and customs. Furthermore, if the user is in Europe, the construction unit can construct a script that reflects European culture and customs. In this way, a region-specific script is constructed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the construction unit may be performed using AI, for example, or may be performed without using AI. For example, the construction unit can input the user's geographical location information to the generation AI and cause the generation AI to construct a region-specific script.

[0090] When constructing a script, the construction unit can analyze the user's social media activity and construct a related script. The construction unit, for example, constructs a script based on themes that the user frequently mentions on social media. The construction unit, for example, constructs a script based on themes that the user frequently mentions on social media. The construction unit can also construct a related script with reference to the user's friendships on social media. The construction unit can also analyze the content of the user's social media posts and construct a script that the user is likely to be interested in. The construction unit constructs a script based on themes that the user frequently mentions on social media. The construction unit can also construct a related script with reference to the user's friendships on social media. The construction unit can also analyze the content of the user's social media posts and construct a script that the user is likely to be interested in. In this way, a related script is constructed by analyzing the user's social media activity. Some or all of the above-mentioned processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's social media activity into a generation AI and cause the generation AI to construct a related script.

[0091] The construction unit can customize the construction method by reflecting the user's past feedback when constructing a script. For example, the construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. For example, the construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. The construction unit can also construct a new script by avoiding the features of scripts for which the user gave negative feedback in the past. The construction unit can also fine-tune the plot and characters of the script based on the user's feedback. The construction unit constructs a new script by reflecting the features of scripts for which the user gave favorable feedback in the past. The construction unit can also construct a new script by avoiding the features of scripts for which the user gave negative feedback in the past. The construction unit can also fine-tune the plot and characters of the script based on the user's feedback. In this way, the construction method is customized by reflecting the user's past feedback, and a script that is more suited to the user's preferences is constructed. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's past feedback into a generation AI and cause the generation AI to customize the construction method. === Hard Collateral 1-1 === Each of the multiple elements including the generation unit and construction unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit generates the character's personality and appearance using the control unit 46A of the smart device 14, and generates the character's background and the setting of the story using the specific processing unit 290 of the data processing device 12. Furthermore, the construction unit constructs a script based on the characters and story generated by the specific processing unit 290 of the data processing device 12, and creates specific lines and scenes using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the generation unit and construction unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit generates the character's personality and appearance using the control unit 46A of the smart glasses 214, and generates the character's background and the setting of the story using the specific processing unit 290 of the data processing device 12. In addition, the construction unit constructs a script based on the characters and story generated by the specific processing unit 290 of the data processing device 12, and creates specific lines and scenes using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the generation unit and construction unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit generates the character's personality and appearance by the control unit 46A of the headset type terminal 314, and generates the character's background and the setting of the story by the specific processing unit 290 of the data processing device 12. Furthermore, the construction unit constructs a script based on the characters and story generated by the specific processing unit 290 of the data processing device 12, and creates specific lines and scenes by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the generation unit and construction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit generates the character's personality and appearance using the control unit 46A of the robot 414, and generates the character's background and the setting of the story using the specific processing unit 290 of the data processing device 12. Furthermore, the construction unit constructs a script based on the characters and story generated by the specific processing unit 290 of the data processing device 12, and creates specific lines and scenes using the control unit 46A of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When generating the character's personality and appearance, the generation unit can also acquire the user's real-time biometric information and adjust the character's details based on that information. For example, if the user's heart rate is high, a tense character can be generated. Also, if the user's body temperature is rising, a character reflecting an excited state can be generated. Furthermore, if the user's breathing pattern is stable, a relaxed character can be generated. In this way, a more personalized character can be generated by adjusting the character's personality and appearance based on the user's biometric information.

[0094] When generating the character's personality and appearance, the generation unit can analyze the user's past purchasing history and adjust the character's details based on that information. For example, the character's outfit can be determined based on fashion items purchased by the user in the past. The character's hobbies and personality can also be set based on the genres of books and movies purchased by the user. Furthermore, the character's living environment can be set based on interior items purchased by the user. In this way, a more personalized character can be generated by adjusting the character's personality and appearance based on the user's purchasing history.

[0095] When generating the character's personality and appearance, the generation unit can analyze the user's voice tone and adjust the character's details based on that information. For example, if the user has a high-pitched voice, a bright and lively character can be generated. Conversely, if the user has a low-pitched voice, a calm character can be generated. Furthermore, the character's personality and speaking style can be adjusted based on the user's voice speed. In this way, a more personalized character can be generated by adjusting the character's personality and appearance based on the user's voice tone.

[0096] When generating the character's personality and appearance, the generation unit can analyze the user's SNS activity times and set the character's lifestyle based on that information. For example, if the user is a nocturnal person, a character that is active at night can be generated. Conversely, if the user is a morning person, a character that is active in the morning can be generated. Furthermore, the character's sociability can be set based on the frequency of the user's SNS activity. In this way, a more personalized character can be generated by setting the character's lifestyle based on the user's SNS activity times.

[0097] When generating the character's personality and appearance, the generation unit can analyze the user's music playback history and set the character's hobbies and personality based on that information. For example, if the user often listens to rock music, a character with an energetic and adventurous spirit can be generated. If the user likes classical music, a character with a calm personality can be generated. Furthermore, if the user likes pop music, a cheerful and sociable character can be generated. In this way, by setting the character's hobbies and personality based on the user's music playback history, a more personalized character can be generated.

[0098] When generating the character's personality and appearance, the generation unit can estimate the user's emotions and change the character's facial expression in real time based on those emotions. For example, if the user is laughing, the character can also smile. If the user is surprised, the character can also have a surprised expression. Furthermore, if the user is sad, the character can also have a sad expression. This allows the character's facial expression to change in real time based on the user's emotions, providing a more interactive experience.

[0099] When generating the character's personality and appearance, the generation unit can analyze the user's past travel history and set the character's background and story setting based on that information. For example, the character's hometown can be set based on cities and countries the user has visited in the past. The story setting can also be set based on tourist spots the user has visited. Furthermore, the character's hobbies and special skills can be set based on activities the user has experienced. In this way, a more personalized story can be generated by setting the character's background and story setting based on the user's travel history.

[0100] When generating the character's personality and appearance, the generation unit can estimate the user's emotions and adjust the character's tone of voice and speaking style based on those emotions. For example, if the user is relaxed, the character can speak in a calm voice. If the user is excited, the character can speak in an energetic voice. Furthermore, if the user is sad, the character can speak in a gentle voice. In this way, a more personalized character can be generated by adjusting the character's tone of voice and speaking style based on the user's emotions.

[0101] When generating the character's personality and appearance, the generation unit can also analyze the user's past sports activity history and set the character's special skills and hobbies based on that information. For example, if the user played soccer in the past, the character can be set to be good at soccer. If the user played basketball in the past, the character can be set to be good at basketball. Furthermore, if the user swam in the past, the character can be set to be good at swimming. In this way, by setting the character's special skills and hobbies based on the user's sports activity history, a more personalized character can be generated.

[0102] When generating the character's personality and appearance, the generation unit can estimate the user's emotions and change the character's movements and gestures in real time based on the emotions. For example, if the user is relaxed, the character can move leisurely. If the user is excited, the character can move lively. If the user is sad, the character can move calmly. This allows the character's movements and gestures to change in real time based on the user's emotions, providing a more interactive experience.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The generator generates the character's personality and appearance. The generator generates basic information such as the character's age, gender, occupation, and hobbies, as well as details such as hair color, eye shape, and clothing. It also generates elements such as the character's background, the setting of the story, major events, and the relationships, conflicts, and cooperation between characters. Step 2: The generator generates a story based on the generated character information. The generator generates the story development and major events, taking into account the character's personality and background. Step 3: The construction department creates a script based on the characters and story generated by the generation department. The construction department creates specific lines and scenes, taking into account the personalities and backgrounds of the generated characters.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generator that generates the character's personality and appearance; a generation unit that generates a story based on information about the character generated by the generation unit; a construction unit that constructs a script based on the characters and story generated by the generation unit. A system characterized by:

2. The generation unit Generate basic information about the character's age, gender, occupation, and hobbies 2. The system of claim 1.

3. The generation unit Generate details for character hair color, eye shape, and clothing 2. The system of claim 1.

4. The generation unit Generate character backgrounds, story settings, and events 2. The system of claim 1.

5. The generation unit Generate elements of relationships, conflicts, and cooperation between characters 2. The system of claim 1.

6. The construction unit Create specific lines and scenes based on the personality and background of AI-generated characters 2. The system of claim 1.

7. The generation unit Estimate the user's emotions and adjust the character's personality and appearance based on the estimated user emotions.

2. The system of claim 1.

8. The generation unit When generating characters, the generation algorithm is optimized by referencing data on popular characters from the past.

2. The system of claim 1.

Images