System

Abstract

An object of the system according to the embodiment is to realistically produce a short drama at a low cost and allow a user to easily enjoy various stories.SOLUTION: A system includes a reception part, a generation part, and a reproduction part. The reception unit receives an outline of a story from a user. The generation unit analyzes the outline of the story received by the reception unit, and generates a scenario of a drama including a motion and lines of a character and a background. The reproducer realistically reproduces the movement and expression of the character based on the scenario generated by the generator.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, the production of short dramas required high actor fees and production costs, making it difficult to easily enjoy a variety of stories.

[0005] The system according to the embodiment aims to produce short dramas realistically at low cost and to enable viewers to easily enjoy a variety of stories. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a reproduction unit. The reception unit receives a story outline from a user. The generation unit analyzes the story outline received by the reception unit and generates a drama scenario including character movements, dialogue, and background. The reproduction unit realistically reproduces the character movements and facial expressions based on the scenario generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can produce short dramas realistically at low cost, allowing viewers to easily enjoy a variety of stories. [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 generation system according to an embodiment of the present invention accepts a story outline from a user, analyzes the outline, and generates a drama scenario including character movements, dialogue, background, and the like, realistically recreating the character movements and facial expressions. The drama generation system receives a desired story outline from the user, and the generation AI analyzes the outline and generates a drama scenario including character movements, dialogue, background, and the like. Based on the generated scenario, the generation AI realistically reproduces the character movements and facial expressions to produce a short, vertical drama. For example, the drama generation system inputs a desired story outline from the user. For example, the drama generation system inputs an outline such as "a romance drama in which the protagonist overcomes difficulties and finds happiness." This information is input to the generation AI. The drama generation system then analyzes the input information and generates a drama scenario including character movements, dialogue, background, and the like. The generation AI learns from past drama data and can generate realistic and engaging scenarios. For example, the scenario may include scenes in which the protagonist overcomes difficulties and moving lines. Based on the generated scenario, the generation AI realistically reproduces the character movements and facial expressions. The generation AI has the technology to naturally reproduce the facial expressions and movements of characters, providing viewers with realistic and engaging dramas. For example, scenes in which the protagonist sheds tears or smiles with joy are realistically reproduced. This allows the drama generation system to reduce traditional actor fees while maintaining quality and allowing viewers to easily enjoy a variety of stories. This allows the drama generation system to generate realistic and engaging short vertical dramas based on the user's desired story. For example, users can enjoy realistic and engaging short vertical dramas simply by inputting their desired story. For example, they can easily enjoy them in short bursts of time, such as during their commute or break.

[0029] A drama generation system according to an embodiment includes a reception unit, a generation unit, and a reproduction unit. The reception unit receives a story outline from a user. The story outline may include, but is not limited to, character settings, plot highlights, and scene summaries. The reception unit provides, for example, an interface for the user to input a desired story outline. The generation unit uses a generation AI to analyze the story outline received by the reception unit and generate a drama scenario including character movements, dialogue, background, and the like. The generation unit generates a scenario by learning, for example, past drama data. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a realistic and engaging scenario based on past drama data. The generation unit generates a scenario including, for example, character movements, dialogue, background, and the like. The reproduction unit realistically reproduces character movements and facial expressions based on the scenario generated by the generation unit. The reproduction unit has, for example, technology for naturally reproducing character expressions and movements, thereby providing a realistic and engaging drama for viewers. The reproduction unit realistically reproduces character expressions and movements, for example. As a result, the drama generation system according to the embodiment can generate a realistic and attractive short, vertical drama based on a story desired by a user. Some or all of the above-described processing in the reproducing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproducing unit can reproduce the character's movements and expressions using an AI model that receives the scenario generated by the generating unit as input and outputs the character's movements and expressions.

[0030] The generation unit can generate a scenario by learning past drama data. Past drama data includes, for example, data of a specific genre or a specific period, but is not limited to these examples. The generation unit, for example, learns past drama data to generate a scenario. The generation unit can learn past drama data using techniques such as deep learning and supervised learning. For example, the generation unit generates a realistic and attractive scenario based on past drama data. In this way, by learning past drama data, a realistic and attractive scenario can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past drama data into a generation AI and cause the generation AI to generate a scenario.

[0031] The reproducing unit can reproduce the character's facial expressions and movements naturally. The reproducing unit, for example, has technology for naturally reproducing the character's facial expressions and movements. The reproducing unit, for example, reproduces the character by emphasizing the smoothness of movement and the realism of facial expressions. The reproducing unit, for example, realistically reproduces the character's facial expressions and movements. This naturally reproduces the character's facial expressions and movements, making it possible to provide a realistic and appealing drama to viewers. Some or all of the above-mentioned processing in the reproducing unit may be performed using AI, or may be performed without using AI. For example, the reproducing unit can reproduce the character's movements and expressions using an AI model that receives the scenario generated by the generating unit as input and outputs the character's movements and expressions.

[0032] The reception unit can analyze the user's past story input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past story input history. The past story input history includes, for example, but is not limited to, the type, frequency, and content of stories input. The reception unit, for example, automatically displays the genres of stories input by the user in the past as candidates. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit, for example, predicts and suggests an input method to be used in a specific time period based on the user's past input history. This improves user convenience by providing an optimal input interface based on the user's past input history. Some or all of the above-described processing by the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to suggest an optimal input interface.

[0033] The reception unit can provide input assistance based on the user's current interests and trends when inputting a story summary. For example, the reception unit refers to social media trends, news articles, etc. to acquire the user's current interests and trends. For example, the reception unit suggests a related story summary based on keywords recently searched by the user. For example, the reception unit analyzes the user's social media activity and suggests a story summary based on the user's current interests. For example, the reception unit suggests a story summary that the user is likely to be interested in based on the latest trend information. This allows the input assistance based on the user's interests and trends to allow the user to input a story that is likely to interest the user. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to perform input assistance based on the interests and trends.

[0034] When a story outline is input, the reception unit can select an appropriate input means according to the user's input method. The reception unit, for example, uses voice recognition technology, text analysis technology, image analysis technology, etc. to detect the user's input method. For example, when a user inputs a story outline by voice, the reception unit converts the input into text using voice recognition technology. For example, when a user inputs a story outline using text, the reception unit provides an input completion function to enable efficient input. For example, when a user inputs a story outline using an image, the reception unit generates related text using image analysis technology. This allows the user to efficiently input a story outline by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0035] When inputting a story summary, the reception unit can prioritize accepting highly relevant stories by taking into account the user's geographical location information. The reception unit, for example, uses GPS data, an IP address, or the like to acquire the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting stories related to that area. For example, when the user is traveling, the reception unit prioritizes accepting stories related to the travel destination. For example, when the user is at home, the reception unit prioritizes accepting stories related to the home. In this way, by taking the user's geographical location information into account, highly relevant stories can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize highly relevant stories.

[0036] The reception unit can analyze the user's social media activity when inputting a story summary and suggest related stories. For example, the reception unit refers to the content of posts, the number of likes, the number of followers, etc. to analyze the user's social media activity. For example, the reception unit suggests related stories based on the content shared by the user on social media. For example, the reception unit suggests related stories by referring to the activity of the user's friends on social media. For example, the reception unit analyzes the content of the user's social media posts and suggests related stories. In this way, related stories can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related stories.

[0037] The reception unit can customize the input method by reflecting the user's past feedback when inputting a story outline. The reception unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit, for example, suggests an optimal input method based on the user's previously preferred input method. The reception unit, for example, analyzes the user's past feedback and improves the input interface. The reception unit, for example, suggests an optimal input method by avoiding input methods that the user has previously been dissatisfied with. This makes it possible to provide an optimal input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0038] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the story. For example, the generation unit refers to user ratings, the impact of the story, etc. to evaluate the importance of the story. For example, the generation unit generates a scenario that includes detailed descriptions of important scenes. For example, the generation unit generates a scenario that includes concise descriptions of unimportant scenes. For example, the generation unit generates a scenario that includes particularly detailed descriptions of climax scenes. In this way, by adjusting the level of detail of the scenario based on the importance of the story, important scenes can be depicted in more detail. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input story importance data into the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0039] When generating a scenario, the generation unit can apply different generation algorithms depending on the story category. For example, the generation unit refers to genres, themes, etc. to evaluate the story category. For example, in the case of a romance drama, the generation unit applies a generation algorithm that emphasizes emotional expression. For example, in the case of an action drama, the generation unit applies a generation algorithm that emphasizes depiction of movement. For example, in the case of a comedy drama, the generation unit applies a generation algorithm that emphasizes humor. In this way, by applying a generation algorithm according to the story category, it is possible to generate a scenario that is optimal for each category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story category data into the generation AI and have the generation AI apply the generation algorithm.

[0040] When generating a scenario, the generation unit can improve the accuracy of generation by referring to the user's past scenario results. The generation unit, for example, analyzes the user's past scenario results. Past scenario results include, but are not limited to, the user's evaluation and the success rate of the scenario. The generation unit, for example, generates an optimal scenario based on scenario patterns that the user previously preferred. The generation unit, for example, analyzes the user's past scenario results and improves the generation algorithm. The generation unit, for example, generates an optimal scenario by avoiding scenario patterns that the user previously dissatisfied with. This allows the accuracy of generation to be improved by referring to the user's past scenario results. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past scenario result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0041] When generating a scenario, the generation unit can determine the priority of the scenario based on the submission date and time of the story. For example, the generation unit refers to the submission date and time, submission frequency, etc. to evaluate the submission date and time of the story. For example, the generation unit generates scenarios with priority for stories with an approaching deadline. For example, the generation unit generates scenarios with priority for stories with an early submission date. For example, the generation unit generates scenarios with later submission dates at a later date. In this way, by determining the priority of scenarios based on the submission date of the story, it is possible to generate appropriate scenarios according to the submission date. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story submission date data into the generation AI and have the generation AI determine the priority of the scenarios.

[0042] When generating a scenario, the generation unit can adjust the order of the scenarios based on the relevance of the stories. For example, the generation unit refers to thematic consistency, character commonality, etc. to evaluate the relevance of the stories. For example, the generation unit generates a scenario in which highly relevant scenes are arranged consecutively. For example, the generation unit generates a scenario in which less relevant scenes are arranged at intervals. For example, the generation unit generates a scenario in which highly relevant scenes are arranged preferentially in accordance with the flow of the story. In this way, by adjusting the order of the scenarios based on the relevance of the stories, the flow of the story can be made more natural. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story relevance data into the generation AI and cause the generation AI to adjust the order of the scenarios.

[0043] When generating a scenario, the generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise. For example, the generation unit refers to the user's occupation, past learning history, etc. to evaluate the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a scenario that makes heavy use of technical terms. For example, if the user does not have technical expertise, the generation unit generates a scenario that avoids technical terms. For example, the generation unit generates a scenario that uses appropriate technical terms according to the user's level of expertise. This makes it possible to provide a scenario that is easy for the user to understand by adjusting the use of technical terms in the scenario according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's technical expertise data into the generation AI and cause the generation AI to use technical terms in the scenario.

[0044] When reproducing the character's movements and facial expressions, the reproduction unit can optimize the reproduction algorithm by referring to past reproduction data. The reproduction unit, for example, refers to past reproduction data. Past reproduction data includes, but is not limited to, data on reproduced scenes and user ratings. The reproduction unit, for example, reproduces optimal character movements based on the past reproduction data. The reproduction unit, for example, analyzes the past reproduction data and improves the reproduction algorithm. The reproduction unit, for example, refers to the past reproduction data to reproduce the character's facial expressions more naturally. By referring to the past reproduction data, the accuracy of reproducing the character's movements and facial expressions can be improved. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit may input past reproduction data into a generation AI and cause the generation AI to optimize the reproduction algorithm.

[0045] The reproduction unit can improve the reproduction method by reflecting user feedback when reproducing the character's movements and facial expressions. The reproduction unit, for example, analyzes the user's feedback. User feedback includes, but is not limited to, ratings, comments, and usage history. The reproduction unit, for example, improves the character's movements based on the user's feedback. The reproduction unit, for example, analyzes the user's feedback and improves the reproduction algorithm. The reproduction unit, for example, reflects the user's feedback to reproduce the character's facial expressions more naturally. This allows the method for reproducing the character's movements and facial expressions to be improved by reflecting the user's feedback. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input user's feedback data into the generation AI and cause the generation AI to improve the reproduction method.

[0046] When reproducing the movements and expressions of a character, the reproducing unit can adjust the level of detail of the reproduction based on important scenes in the scenario. For example, the reproducing unit refers to climax scenes, emotional scenes, etc. to evaluate important scenes in the scenario. For example, the reproducing unit reproduces the movements and expressions of a character in detail in important scenes. For example, the reproducing unit reproduces the movements and expressions of a character in simplified form in unimportant scenes. For example, the reproducing unit reproduces the movements and expressions of a character in particularly detailed form in a climax scene. In this way, by adjusting the level of detail of the reproduction based on important scenes in the scenario, important scenes can be reproduced in more detail. Some or all of the above-described processing in the reproducing unit may be performed using AI or without AI. For example, the reproducing unit may input important scene data of the scenario to a generation AI and cause the generation AI to adjust the level of detail of the reproduction.

[0047] When reproducing the character's movements and facial expressions, the reproduction unit can select an appropriate reproduction method by taking into account the user's geographical location information. The reproduction unit, for example, uses GPS data, an IP address, or the like to acquire the user's geographical location information. For example, if the user is in a specific area, the reproduction unit reproduces the character's movements and facial expressions related to that area. For example, if the user is traveling, the reproduction unit reproduces the character's movements and facial expressions related to the travel destination. For example, if the user is at home, the reproduction unit reproduces the character's movements and facial expressions related to the home. In this way, by taking the user's geographical location information into account, it is possible to reproduce the movements and facial expressions of a highly relevant character. Some or all of the above-described processing in the reproduction unit may be performed using AI, or may be performed without using AI. For example, the reproduction unit can input the user's geographical location data into the generation AI and cause the generation AI to select an appropriate reproduction method.

[0048] When reproducing the character's movements and expressions, the reproduction unit can analyze the user's social media activity and suggest means for reproduction. For example, the reproduction unit refers to the content of posts, the number of likes, the number of followers, etc. to analyze the user's social media activity. For example, the reproduction unit reproduces the movements and expressions of related characters based on content shared by the user on social media. For example, the reproduction unit reproduces the movements and expressions of related characters based on the activities of the user's friends on social media. For example, the reproduction unit analyzes the content of the user's social media posts and reproduces the movements and expressions of related characters. In this way, the movements and expressions of related characters can be reproduced by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit may input the user's social media data into a generation AI and cause the generation AI to suggest means for reproduction.

[0049] The reproduction unit can customize the reproduction method by reflecting the user's past feedback when reproducing the character's movements and facial expressions. The reproduction unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, ratings, comments, and usage history. The reproduction unit, for example, proposes an optimal reproduction method based on the user's previously preferred character movements and facial expressions. The reproduction unit, for example, analyzes the user's past feedback and improves the reproduction algorithm. The reproduction unit, for example, proposes an optimal reproduction method by avoiding character movements and facial expressions that the user has previously dissatisfied with. This makes it possible to provide an optimal reproduction method by reflecting the user's past feedback. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reproduction method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can automatically classify the story genre based on the user's input. For example, the reception unit can analyze the story summary input by the user and classify it into genres such as romance, action, and comedy. Furthermore, the reception unit can learn the genres of stories previously input by the user and preferentially suggest genres that match the user's preferences. This allows the user to easily input a story that suits their preferences. The reception unit can also provide related scenario templates based on the story genre input by the user. For example, the reception unit can provide a romance drama template or an action scene template, making it easier for the user to input a story more specifically.

[0052] The generation unit can adjust the tempo of the scenario based on the user's input. For example, the generation unit can analyze the story outline entered by the user and speed up or slow down the tempo of the scenario. Furthermore, the generation unit can learn the tempo of scenarios that the user has previously preferred and automatically set the tempo according to the user's preferences. This allows the user to enjoy a scenario with a tempo that suits their preferences. The generation unit can also adjust the speed of character movements and dialogue according to the tempo of the scenario. For example, the generation unit can generate a scenario with a fast tempo for action scenes and a slow tempo for emotional scenes.

[0053] When reproducing character movements and facial expressions, the reproduction unit can adjust the reproduction method by taking into account the user's viewing history. For example, the reproduction unit analyzes data on dramas the user has previously watched and reproduces the character movements and facial expressions that the user prefers. Furthermore, the reproduction unit can customize the reproduction method based on the genres and scene types of dramas the user has watched. This allows the user to enjoy character movements and facial expressions that suit their preferences. The reproduction unit can also improve the accuracy of reproducing character movements and facial expressions based on the user's viewing history. For example, by preferentially reproducing the facial expressions and movements that the user prefers, a more realistic and appealing drama can be provided.

[0054] The reception unit can analyze the user's past story input history and provide an optimal input interface. For example, the reception unit can automatically display the genres of stories that the user has previously input as candidates. Furthermore, the reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it easier for the user to input a story outline using an input method that suits their preferences. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user inputs at night, voice input can be preferentially suggested.

[0055] The reception unit can provide input assistance based on the user's current interests and trends when inputting a story summary. For example, the reception unit can refer to social media trends, news articles, etc. to obtain the user's current interests and trends. Furthermore, the reception unit can also suggest related story summaries based on keywords recently searched by the user. This makes it easier for the user to input a story based on their interests. The reception unit can also analyze the user's social media activity and suggest story summaries based on the user's current interests. For example, if the user has recently posted many stories about "travel," the reception unit can suggest story summaries related to travel.

[0056] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the story. For example, the generation unit generates a scenario that includes detailed descriptions for important scenes. Furthermore, the generation unit can also generate a scenario that includes brief descriptions for unimportant scenes. This allows the user to enjoy important scenes in more detail. The generation unit can also generate a scenario that includes particularly detailed descriptions for climax scenes. For example, scenes that the user particularly pays attention to, such as moving scenes or action scenes, are described in detail.

[0057] The reproduction unit can improve the reproduction method by reflecting user feedback when reproducing the character's movements and facial expressions. For example, the reproduction unit improves the character's movements based on user feedback. Furthermore, the reproduction unit can analyze user feedback and improve the reproduction algorithm. This allows users to enjoy character movements and facial expressions that suit their preferences. The reproduction unit can also reflect user feedback to reproduce the character's facial expressions more naturally. For example, if the user provides feedback such as "I want more smiles," the reproduction unit increases the number of smiles on the character.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives a story outline from the user. The story outline includes character settings, plot highlights, and scene summaries. The reception unit provides an interface for the user to input the desired story outline. Step 2: The generation unit uses a generation AI to analyze the outline of the story received by the reception unit and generate a drama scenario including character movements, dialogue, background, etc. The generation unit learns from past drama data to generate a scenario, and uses a text generation AI (e.g., LLM) and a multimodal generation AI to generate a realistic and engaging scenario. Step 3: The re-enactment unit realistically reproduces the character's movements and facial expressions based on the scenario generated by the generation unit. The re-enactment unit has the technology to naturally reproduce the character's facial expressions and movements, providing a realistic and engaging drama for the viewer. The re-enactment unit can reproduce the character's movements and facial expressions using an AI model that takes the scenario generated by the generation unit as input and outputs the character's movements and facial expressions.

[0060] (Example 2) A drama generation system according to an embodiment of the present invention accepts a story outline from a user, analyzes the outline, and generates a drama scenario including character movements, dialogue, background, and the like, realistically recreating the character movements and facial expressions. The drama generation system receives a desired story outline from the user, and the generation AI analyzes the outline and generates a drama scenario including character movements, dialogue, background, and the like. Based on the generated scenario, the generation AI realistically reproduces the character movements and facial expressions to produce a short, vertical drama. For example, the drama generation system inputs a desired story outline from the user. For example, the drama generation system inputs an outline such as "a romance drama in which the protagonist overcomes difficulties and finds happiness." This information is input to the generation AI. The drama generation system then analyzes the input information and generates a drama scenario including character movements, dialogue, background, and the like. The generation AI learns from past drama data and can generate realistic and engaging scenarios. For example, the scenario may include scenes in which the protagonist overcomes difficulties and moving lines. Based on the generated scenario, the generation AI realistically reproduces the character movements and facial expressions. The generation AI has the technology to naturally reproduce the facial expressions and movements of characters, providing viewers with realistic and engaging dramas. For example, scenes in which the protagonist sheds tears or smiles with joy are realistically reproduced. This allows the drama generation system to reduce traditional actor fees while maintaining quality and allowing viewers to easily enjoy a variety of stories. This allows the drama generation system to generate realistic and engaging short vertical dramas based on the user's desired story. For example, users can enjoy realistic and engaging short vertical dramas simply by inputting their desired story. For example, they can easily enjoy them in short bursts of time, such as during their commute or break.

[0061] A drama generation system according to an embodiment includes a reception unit, a generation unit, and a reproduction unit. The reception unit receives a story outline from a user. The story outline may include, but is not limited to, character settings, plot highlights, and scene summaries. The reception unit provides, for example, an interface for the user to input a desired story outline. The generation unit uses a generation AI to analyze the story outline received by the reception unit and generate a drama scenario including character movements, dialogue, background, and the like. The generation unit generates a scenario by learning, for example, past drama data. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a realistic and engaging scenario based on past drama data. The generation unit generates a scenario including, for example, character movements, dialogue, background, and the like. The reproduction unit realistically reproduces character movements and facial expressions based on the scenario generated by the generation unit. The reproduction unit has, for example, technology for naturally reproducing character expressions and movements, thereby providing a realistic and engaging drama for viewers. The reproduction unit realistically reproduces character expressions and movements, for example. As a result, the drama generation system according to the embodiment can generate a realistic and attractive short, vertical drama based on a story desired by a user. Some or all of the above-described processing in the reproducing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproducing unit can reproduce the character's movements and expressions using an AI model that receives the scenario generated by the generating unit as input and outputs the character's movements and expressions.

[0062] The generation unit can generate a scenario by learning past drama data. Past drama data includes, for example, data of a specific genre or a specific period, but is not limited to these examples. The generation unit, for example, learns past drama data to generate a scenario. The generation unit can learn past drama data using techniques such as deep learning and supervised learning. For example, the generation unit generates a realistic and attractive scenario based on past drama data. In this way, by learning past drama data, a realistic and attractive scenario can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past drama data into a generation AI and cause the generation AI to generate a scenario.

[0063] The reproducing unit can reproduce the character's facial expressions and movements naturally. The reproducing unit, for example, has technology for naturally reproducing the character's facial expressions and movements. The reproducing unit, for example, reproduces the character by emphasizing the smoothness of movement and the realism of facial expressions. The reproducing unit, for example, realistically reproduces the character's facial expressions and movements. This naturally reproduces the character's facial expressions and movements, making it possible to provide a realistic and appealing drama to viewers. Some or all of the above-mentioned processing in the reproducing unit may be performed using AI, or may be performed without using AI. For example, the reproducing unit can reproduce the character's movements and expressions using an AI model that receives the scenario generated by the generating unit as input and outputs the character's movements and expressions.

[0064] The reception unit can estimate the user's emotions and adjust the story summary input method based on the emotions. The reception unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is sad, the reception unit provides a simple and intuitive interface and minimizes input steps. For example, if the user is excited, the reception unit provides detailed input options and suggests a customizable input method. For example, if the user is relaxed, the reception unit prioritizes voice input and allows the user to input the story summary in a natural conversational format. This makes it easier for the user to input the story summary by providing an input method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past story input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past story input history. The past story input history includes, for example, but is not limited to, the type, frequency, and content of stories input. The reception unit, for example, automatically displays the genres of stories input by the user in the past as candidates. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit, for example, predicts and suggests an input method to be used in a specific time period based on the user's past input history. This improves user convenience by providing an optimal input interface based on the user's past input history. Some or all of the above-described processing by the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to suggest an optimal input interface.

[0066] The reception unit can provide input assistance based on the user's current interests and trends when inputting a story summary. For example, the reception unit refers to social media trends, news articles, etc. to acquire the user's current interests and trends. For example, the reception unit suggests a related story summary based on keywords recently searched by the user. For example, the reception unit analyzes the user's social media activity and suggests a story summary based on the user's current interests. For example, the reception unit suggests a story summary that the user is likely to be interested in based on the latest trend information. This allows the input assistance based on the user's interests and trends to allow the user to input a story that is likely to interest the user. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to perform input assistance based on the interests and trends.

[0067] When a story outline is input, the reception unit can select an appropriate input means according to the user's input method. The reception unit, for example, uses voice recognition technology, text analysis technology, image analysis technology, etc. to detect the user's input method. For example, when a user inputs a story outline by voice, the reception unit converts the input into text using voice recognition technology. For example, when a user inputs a story outline using text, the reception unit provides an input completion function to enable efficient input. For example, when a user inputs a story outline using an image, the reception unit generates related text using image analysis technology. This allows the user to efficiently input a story outline by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0068] The reception unit can estimate the user's emotions and prioritize the input stories based on the emotions. The reception unit uses, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is excited, the reception unit prioritizes processing the story. For example, if the user is sad, the reception unit prioritizes processing the story. For example, if the user is relaxed, the reception unit processes the story with normal priority. This allows the priority of stories to be determined based on the user's emotions, thereby prioritizing processing of appropriate stories according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When inputting a story summary, the reception unit can prioritize accepting highly relevant stories by taking into account the user's geographical location information. The reception unit, for example, uses GPS data, an IP address, or the like to acquire the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting stories related to that area. For example, when the user is traveling, the reception unit prioritizes accepting stories related to the travel destination. For example, when the user is at home, the reception unit prioritizes accepting stories related to the home. In this way, by taking the user's geographical location information into account, highly relevant stories can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize highly relevant stories.

[0070] The reception unit can analyze the user's social media activity when inputting a story summary and suggest related stories. For example, the reception unit refers to the content of posts, the number of likes, the number of followers, etc. to analyze the user's social media activity. For example, the reception unit suggests related stories based on the content shared by the user on social media. For example, the reception unit suggests related stories by referring to the activity of the user's friends on social media. For example, the reception unit analyzes the content of the user's social media posts and suggests related stories. In this way, related stories can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related stories.

[0071] The reception unit can customize the input method by reflecting the user's past feedback when inputting a story outline. The reception unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. The reception unit, for example, suggests an optimal input method based on the user's previously preferred input method. The reception unit, for example, analyzes the user's past feedback and improves the input interface. The reception unit, for example, suggests an optimal input method by avoiding input methods that the user has previously been dissatisfied with. This makes it possible to provide an optimal input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0072] The generation unit can estimate the user's emotions and adjust the scenario expression method based on the emotions. The generation unit uses, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is sad, the generation unit generates a scenario that emphasizes moving scenes. For example, if the user is excited, the generation unit generates a scenario that emphasizes action scenes. For example, if the user is relaxed, the generation unit generates a scenario that emphasizes calm scenes. This allows the generation of a more appealing scenario by adjusting the scenario expression method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, 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 the generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the scenario expression method.

[0073] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the story. For example, the generation unit refers to user ratings, the impact of the story, etc. to evaluate the importance of the story. For example, the generation unit generates a scenario that includes detailed descriptions of important scenes. For example, the generation unit generates a scenario that includes concise descriptions of unimportant scenes. For example, the generation unit generates a scenario that includes particularly detailed descriptions of climax scenes. In this way, by adjusting the level of detail of the scenario based on the importance of the story, important scenes can be depicted in more detail. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input story importance data into the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0074] When generating a scenario, the generation unit can apply different generation algorithms depending on the story category. For example, the generation unit refers to genres, themes, etc. to evaluate the story category. For example, in the case of a romance drama, the generation unit applies a generation algorithm that emphasizes emotional expression. For example, in the case of an action drama, the generation unit applies a generation algorithm that emphasizes depiction of movement. For example, in the case of a comedy drama, the generation unit applies a generation algorithm that emphasizes humor. In this way, by applying a generation algorithm according to the story category, it is possible to generate a scenario that is optimal for each category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story category data into the generation AI and have the generation AI apply the generation algorithm.

[0075] When generating a scenario, the generation unit can improve the accuracy of generation by referring to the user's past scenario results. The generation unit, for example, analyzes the user's past scenario results. Past scenario results include, but are not limited to, the user's evaluation and the success rate of the scenario. The generation unit, for example, generates an optimal scenario based on scenario patterns that the user previously preferred. The generation unit, for example, analyzes the user's past scenario results and improves the generation algorithm. The generation unit, for example, generates an optimal scenario by avoiding scenario patterns that the user previously dissatisfied with. This allows the accuracy of generation to be improved by referring to the user's past scenario results. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past scenario result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0076] The generation unit can estimate the user's emotions and adjust the length of the scenario based on the emotions. The generation unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is in a hurry, the generation unit generates a short scenario. For example, if the user is relaxed, the generation unit generates a longer scenario. For example, if the user is excited, the generation unit generates a fast-paced scenario. By adjusting the length of the scenario based on the user's emotions, it is possible to provide an optimal scenario according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the scenario.

[0077] When generating a scenario, the generation unit can determine the priority of the scenario based on the submission date and time of the story. For example, the generation unit refers to the submission date and time, submission frequency, etc. to evaluate the submission date and time of the story. For example, the generation unit generates scenarios with priority for stories with an approaching deadline. For example, the generation unit generates scenarios with priority for stories with an early submission date. For example, the generation unit generates scenarios with later submission dates at a later date. In this way, by determining the priority of scenarios based on the submission date of the story, it is possible to generate appropriate scenarios according to the submission date. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story submission date data into the generation AI and have the generation AI determine the priority of the scenarios.

[0078] When generating a scenario, the generation unit can adjust the order of the scenarios based on the relevance of the stories. For example, the generation unit refers to thematic consistency, character commonality, etc. to evaluate the relevance of the stories. For example, the generation unit generates a scenario in which highly relevant scenes are arranged consecutively. For example, the generation unit generates a scenario in which less relevant scenes are arranged at intervals. For example, the generation unit generates a scenario in which highly relevant scenes are arranged preferentially in accordance with the flow of the story. In this way, by adjusting the order of the scenarios based on the relevance of the stories, the flow of the story can be made more natural. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story relevance data into the generation AI and cause the generation AI to adjust the order of the scenarios.

[0079] When generating a scenario, the generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise. For example, the generation unit refers to the user's occupation, past learning history, etc. to evaluate the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a scenario that makes heavy use of technical terms. For example, if the user does not have technical expertise, the generation unit generates a scenario that avoids technical terms. For example, the generation unit generates a scenario that uses appropriate technical terms according to the user's level of expertise. This makes it possible to provide a scenario that is easy for the user to understand by adjusting the use of technical terms in the scenario according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's technical expertise data into the generation AI and cause the generation AI to use technical terms in the scenario.

[0080] The reproduction unit can estimate the user's emotions and adjust the character's movements and facial expressions based on the user's emotions. The reproduction unit uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is sad, the reproduction unit reproduces the character's facial expressions more emotionally. For example, if the user is excited, the reproduction unit reproduces the character's movements more dynamically. For example, if the user is relaxed, the reproduction unit reproduces the character's movements more gently. This allows for a more emotional scene to be presented by adjusting the character's movements and facial expressions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the character's movements and facial expressions.

[0081] When reproducing the character's movements and facial expressions, the reproduction unit can optimize the reproduction algorithm by referring to past reproduction data. The reproduction unit, for example, refers to past reproduction data. Past reproduction data includes, but is not limited to, data on reproduced scenes and user ratings. The reproduction unit, for example, reproduces optimal character movements based on the past reproduction data. The reproduction unit, for example, analyzes the past reproduction data and improves the reproduction algorithm. The reproduction unit, for example, refers to the past reproduction data to reproduce the character's facial expressions more naturally. By referring to the past reproduction data, the accuracy of reproducing the character's movements and facial expressions can be improved. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit may input past reproduction data into a generation AI and cause the generation AI to optimize the reproduction algorithm.

[0082] The reproduction unit can improve the reproduction method by reflecting user feedback when reproducing the character's movements and facial expressions. The reproduction unit, for example, analyzes the user's feedback. User feedback includes, but is not limited to, ratings, comments, and usage history. The reproduction unit, for example, improves the character's movements based on the user's feedback. The reproduction unit, for example, analyzes the user's feedback and improves the reproduction algorithm. The reproduction unit, for example, reflects the user's feedback to reproduce the character's facial expressions more naturally. This allows the method for reproducing the character's movements and facial expressions to be improved by reflecting the user's feedback. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input user's feedback data into the generation AI and cause the generation AI to improve the reproduction method.

[0083] When reproducing the movements and expressions of a character, the reproducing unit can adjust the level of detail of the reproduction based on important scenes in the scenario. For example, the reproducing unit refers to climax scenes, emotional scenes, etc. to evaluate important scenes in the scenario. For example, the reproducing unit reproduces the movements and expressions of a character in detail in important scenes. For example, the reproducing unit reproduces the movements and expressions of a character in simplified form in unimportant scenes. For example, the reproducing unit reproduces the movements and expressions of a character in particularly detailed form in a climax scene. In this way, by adjusting the level of detail of the reproduction based on important scenes in the scenario, important scenes can be reproduced in more detail. Some or all of the above-described processing in the reproducing unit may be performed using AI or without AI. For example, the reproducing unit may input important scene data of the scenario to a generation AI and cause the generation AI to adjust the level of detail of the reproduction.

[0084] The reproduction unit can estimate the user's emotions and prioritize the character's movements and facial expressions based on the emotions. The reproduction unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is sad, the reproduction unit prioritizes reproducing emotional scenes. For example, if the user is excited, the reproduction unit prioritizes reproducing action scenes. For example, if the user is relaxed, the reproduction unit prioritizes reproducing calm scenes. This allows emotional scenes to be prioritized by determining the priority of the character's movements and facial expressions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, 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 reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the character's movements and facial expressions.

[0085] When reproducing the character's movements and facial expressions, the reproduction unit can select an appropriate reproduction method by taking into account the user's geographical location information. The reproduction unit, for example, uses GPS data, an IP address, or the like to acquire the user's geographical location information. For example, if the user is in a specific area, the reproduction unit reproduces the character's movements and facial expressions related to that area. For example, if the user is traveling, the reproduction unit reproduces the character's movements and facial expressions related to the travel destination. For example, if the user is at home, the reproduction unit reproduces the character's movements and facial expressions related to the home. In this way, by taking the user's geographical location information into account, it is possible to reproduce the movements and facial expressions of a highly relevant character. Some or all of the above-described processing in the reproduction unit may be performed using AI, or may be performed without using AI. For example, the reproduction unit can input the user's geographical location data into the generation AI and cause the generation AI to select an appropriate reproduction method.

[0086] When reproducing the character's movements and expressions, the reproduction unit can analyze the user's social media activity and suggest means for reproduction. For example, the reproduction unit refers to the content of posts, the number of likes, the number of followers, etc. to analyze the user's social media activity. For example, the reproduction unit reproduces the movements and expressions of related characters based on content shared by the user on social media. For example, the reproduction unit reproduces the movements and expressions of related characters based on the activities of the user's friends on social media. For example, the reproduction unit analyzes the content of the user's social media posts and reproduces the movements and expressions of related characters. In this way, the movements and expressions of related characters can be reproduced by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit may input the user's social media data into a generation AI and cause the generation AI to suggest means for reproduction.

[0087] The reproduction unit can customize the reproduction method by reflecting the user's past feedback when reproducing the character's movements and facial expressions. The reproduction unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, ratings, comments, and usage history. The reproduction unit, for example, proposes an optimal reproduction method based on the user's previously preferred character movements and facial expressions. The reproduction unit, for example, analyzes the user's past feedback and improves the reproduction algorithm. The reproduction unit, for example, proposes an optimal reproduction method by avoiding character movements and facial expressions that the user has previously dissatisfied with. This makes it possible to provide an optimal reproduction method by reflecting the user's past feedback. Some or all of the above-described processing in the reproduction unit may be performed using AI or without AI. For example, the reproduction unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reproduction method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reproduction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to input a desired story outline. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the story outline using a generation AI to generate a drama scenario including character movements, dialogue, background, etc. The reproduction unit is realized, for example, by the control unit 46A of the smart device 14 and realistically reproduces character movements and facial expressions based on the generated scenario. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and reproduction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input a desired story outline. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the story outline using a generation AI to generate a drama scenario including character movements, dialogue, background, etc. The reproduction unit is realized, for example, by the control unit 46A of the smart glasses 214 and realistically reproduces character movements and facial expressions based on the generated scenario. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reproduction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to input a desired story outline. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the story outline using a generation AI to generate a drama scenario including character movements, dialogue, background, etc. The reproduction unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and realistically reproduces character movements and facial expressions based on the generated scenario. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reproduction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to input a desired story outline. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the story outline using a generation AI to generate a drama scenario including character movements, lines, background, etc. The reproduction unit is realized, for example, by the control unit 46A of the robot 414 and realistically reproduces character movements and facial expressions based on the generated scenario.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can automatically classify the story genre based on the user's input. For example, the reception unit can analyze the story summary input by the user and classify it into genres such as romance, action, and comedy. Furthermore, the reception unit can learn the genres of stories previously input by the user and preferentially suggest genres that match the user's preferences. This allows the user to easily input a story that suits their preferences. The reception unit can also provide related scenario templates based on the story genre input by the user. For example, the reception unit can provide a romance drama template or an action scene template, making it easier for the user to input a story more specifically.

[0090] The generation unit can adjust the tempo of the scenario based on the user's input. For example, the generation unit can analyze the story outline entered by the user and speed up or slow down the tempo of the scenario. Furthermore, the generation unit can learn the tempo of scenarios that the user has previously preferred and automatically set the tempo according to the user's preferences. This allows the user to enjoy a scenario with a tempo that suits their preferences. The generation unit can also adjust the speed of character movements and dialogue according to the tempo of the scenario. For example, the generation unit can generate a scenario with a fast tempo for action scenes and a slow tempo for emotional scenes.

[0091] When reproducing character movements and facial expressions, the reproduction unit can adjust the reproduction method by taking into account the user's viewing history. For example, the reproduction unit analyzes data on dramas the user has previously watched and reproduces the character movements and facial expressions that the user prefers. Furthermore, the reproduction unit can customize the reproduction method based on the genres and scene types of dramas the user has watched. This allows the user to enjoy character movements and facial expressions that suit their preferences. The reproduction unit can also improve the accuracy of reproducing character movements and facial expressions based on the user's viewing history. For example, by preferentially reproducing the facial expressions and movements that the user prefers, a more realistic and appealing drama can be provided.

[0092] The reception unit can estimate the user's emotions and adjust the story outline input method based on the emotions. For example, if the user is sad, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is excited, the reception unit can provide detailed input options and suggest a customizable input method. This makes it easier for the user to input a story outline using an input method that suits their emotions. Furthermore, if the user is relaxed, the reception unit can prioritize voice input and allow the user to input a story outline in a natural conversational style.

[0093] The reception unit can analyze the user's past story input history and provide an optimal input interface. For example, the reception unit can automatically display the genres of stories that the user has previously input as candidates. Furthermore, the reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it easier for the user to input a story outline using an input method that suits their preferences. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user inputs at night, voice input can be preferentially suggested.

[0094] The generation unit can estimate the user's emotions and adjust the way the scenario is presented based on the emotions. For example, if the user is sad, the generation unit can generate a scenario that emphasizes moving scenes. Furthermore, if the user is excited, the generation unit can also generate a scenario that emphasizes action scenes. This allows the user to enjoy a scenario that matches their emotions. Furthermore, if the user is relaxed, the generation unit can also generate a scenario that emphasizes calm scenes. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI.

[0095] The reproduction unit can estimate the user's emotions when reproducing the character's movements and facial expressions and adjust the reproduction method based on the emotions. For example, if the user is sad, the reproduction unit can reproduce the character's facial expressions more emotionally. Furthermore, if the user is excited, the reproduction unit can also reproduce the character's movements more dynamically. This allows the user to enjoy the character's movements and facial expressions that correspond to their emotions. Furthermore, if the user is relaxed, the reproduction unit can also reproduce the character's movements calmly. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI.

[0096] The reception unit can provide input assistance based on the user's current interests and trends when inputting a story summary. For example, the reception unit can refer to social media trends, news articles, etc. to obtain the user's current interests and trends. Furthermore, the reception unit can also suggest related story summaries based on keywords recently searched by the user. This makes it easier for the user to input a story based on their interests. The reception unit can also analyze the user's social media activity and suggest story summaries based on the user's current interests. For example, if the user has recently posted many stories about "travel," the reception unit can suggest story summaries related to travel.

[0097] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the story. For example, the generation unit generates a scenario that includes detailed descriptions for important scenes. Furthermore, the generation unit can also generate a scenario that includes brief descriptions for unimportant scenes. This allows the user to enjoy important scenes in more detail. The generation unit can also generate a scenario that includes particularly detailed descriptions for climax scenes. For example, scenes that the user particularly pays attention to, such as moving scenes or action scenes, are described in detail.

[0098] The reproduction unit can improve the reproduction method by reflecting user feedback when reproducing the character's movements and facial expressions. For example, the reproduction unit improves the character's movements based on user feedback. Furthermore, the reproduction unit can analyze user feedback and improve the reproduction algorithm. This allows users to enjoy character movements and facial expressions that suit their preferences. The reproduction unit can also reflect user feedback to reproduce the character's facial expressions more naturally. For example, if the user provides feedback such as "I want more smiles," the reproduction unit increases the number of smiles on the character.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives a story outline from the user. The story outline includes character settings, plot highlights, and scene summaries. The reception unit provides an interface for the user to input the desired story outline. Step 2: The generation unit uses a generation AI to analyze the outline of the story received by the reception unit and generate a drama scenario including character movements, dialogue, background, etc. The generation unit learns from past drama data to generate a scenario, and uses a text generation AI (e.g., LLM) and a multimodal generation AI to generate a realistic and engaging scenario. Step 3: The re-enactment unit realistically reproduces the character's movements and facial expressions based on the scenario generated by the generation unit. The re-enactment unit has the technology to naturally reproduce the character's facial expressions and movements, providing a realistic and engaging drama for the viewer. The re-enactment unit can reproduce the character's movements and facial expressions using an AI model that takes the scenario generated by the generation unit as input and outputs the character's movements and facial expressions.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 AI 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a story outline from a user; a generation unit that analyzes the outline of the story received by the reception unit and generates a drama scenario including character movements, lines, and background; a reproduction unit that realistically reproduces the movements and expressions of the character based on the scenario generated by the generation unit. A system characterized by:

2. The generation unit Generate scenarios by learning from past drama data 2. The system of claim 1.

3. The reproducing section Reproducing character expressions and movements naturally 2. The system of claim 1.

4. The reception unit Describe a specific method for estimating a user's emotions and adjusting the story summary input method based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyze the user's past story input history and provide an appropriate input interface 2. The system of claim 1.

6. The reception unit When writing a story summary, the app provides input assistance based on the user's current interests and trends.

2. The system of claim 1.

7. The reception unit When entering a story summary, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

8. The reception unit Describe a specific method for estimating user emotions and prioritizing input stories based on the estimated user emotions.

2. The system of claim 1.

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