system

Abstract

We provide the system. [Solution] A means for analyzing a document input as text data and extracting emotional states, A means for visualizing the extracted emotional states over a continuous time frame and generating an emotional arc, A means for generating appropriate scenario structure modification suggestions using the aforementioned emotional arc, A method for analyzing characters' emotions and relationships, and based on that, proposing character settings and deepening relationships, A means of adjusting input documents based on different cultural backgrounds, taking into account differences in emotional expression across cultures, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When improving the quality and global expansion of scenarios in video works, it is important to evoke emotional empathy in viewers. Conventionally, however, the adjustment of the emotional ups and downs of the script and the relationship between characters has been done manually, which requires time and labor, and it has been difficult to predict the effect. In addition, optimizing scenarios considering cultural differences in the international market is very complicated and poses problems that require specialized knowledge.

Means for Solving the Problems

[0005] This invention provides means for analyzing text data of an input scenario and extracting emotional states. This generates an emotional arc, visualizing the progression of emotions over time. Furthermore, based on the generated emotional arc, it provides proposed revisions to the scenario structure to optimize it. It can also analyze character emotions and relationships, and based on the results, propose character settings and deepening of relationships. This system, equipped with a cultural emotional filter for adjusting scenarios to consider different cultural backgrounds, enables the optimization of emotional resonance in the global market.

[0006] "Text data" refers to information that is represented in a digital format, such as sentences or words, and is in a format that can be processed by a computer.

[0007] "Emotional state" refers to the type and intensity of emotions expressed in specific text or content, and can reflect human feelings.

[0008] An "emotional arc" is a way of representing the changes in emotions over time in a story or script as a graph or diagram, visually showing the flow of emotions.

[0009] "Character setting" refers to the specific details of a character's personality, background, and goals within a story, and serves as the basis for their actions and thoughts within the narrative.

[0010] "Deepening relationships" is the process of developing the emotional and narrative connections between multiple characters in a work in a detailed and profound way, thereby strengthening the viewer's understanding of and empathy for the characters.

[0011] A "cultural emotional filter" is a mechanism for recognizing and adjusting differences in emotional expression stemming from different cultural backgrounds, thereby improving the international relevance of scenarios. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for generating screenplays for video works that maximize emotional resonance with the viewer. Specific embodiments for carrying out the invention are described below.

[0034] First, the user inputs the scenario text data via their device and sends it to the server. Upon receiving this scenario data, the server uses a natural language processing engine to analyze the document and extract the emotional state in each scene. This visualizes the emotional fluctuations contained in the scenario and generates an emotional arc.

[0035] Next, the server analyzes the generated emotional arc and creates suggestions to optimize the emotional flow of the script. These suggestions may include ideas for rearranging specific scenes, adjusting dialogue, and strengthening character backgrounds. The server also analyzes the characters' emotional expressions and actions and provides additional information and suggestions for modifications to deepen the relationships between the characters.

[0036] Furthermore, the system has a function to apply cultural emotional filters, taking the international market into consideration. The server takes into account how emotions are perceived in different cultural spheres and adjusts dialogue and scenes so that the scenario content is suitable for each culture. This adjustment is optimized according to the viewer's cultural background.

[0037] Suggestions from the server are displayed on the terminal, allowing users to easily review and select this information through a visual interface. The terminal graphically displays emotional arcs, enabling users to intuitively compare and select suggested scenarios and character settings.

[0038] As a concrete example, when a user inputs a movie script aiming for international distribution in the science fiction genre, the server adjusts the emotional arc to emphasize feelings of wonder and fear towards futuristic technology, and reconstructs the main character's growth process in a way that resonates with teenagers worldwide. As a result, the user revises the scenario based on the suggestions and completes the final script.

[0039] Thus, the present invention enhances the quality of scenario writing and makes it possible to streamline the production of video works that resonate with audiences worldwide.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses their device to input text data for the scenario and sends it to the server. The user also inputs additional information such as the scenario's purpose and target audience.

[0043] Step 2:

[0044] The server sends the received text data to a natural language processing engine to analyze the scenario's content. The server extracts the emotional state of each scene and organizes that information.

[0045] Step 3:

[0046] The server generates an emotion arc based on the organized emotion information. The emotion arc is a graph that visually represents changes in emotion over time.

[0047] Step 4:

[0048] The server analyzes the generated emotion arc data and creates suggestions for optimizing the emotional flow of the scenario. The server also provides suggestions for revising the scene structure and dialogue.

[0049] Step 5:

[0050] The server analyzes the emotional expressions and actions of each character within the scenario. The server generates new setting proposals and improvement suggestions to deepen the relationships between the characters.

[0051] Step 6:

[0052] The server uses a cultural sentiment filter to adapt scenarios for different cultural backgrounds. The filter suggests dialogues and scenes that take into account differences in cultural expression of emotion.

[0053] Step 7:

[0054] The terminal receives scenario suggestion information from the server and displays it on the user interface. The terminal visualizes the suggested content in an easy-to-understand way, along with a graph of emotion arcs.

[0055] Step 8:

[0056] Users compare the suggestions displayed on their device and select revisions or make their own modifications as needed. If a suggestion is accepted, they review the revisions and resubmit them to the server.

[0057] Step 9:

[0058] The server receives the final modifications from the user, generates the updated scenario, and saves it. The server then outputs the scenario in the specified format as needed and provides it to the terminal.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] When creating screenplays for video productions, it is essential to maximize the emotional resonance with viewers and efficiently design a script structure that is adapted to different cultural spheres. However, traditional methods require individually visualizing emotional arcs and making cultural adjustments, which is time-consuming and labor-intensive, and does not adequately optimize expression for the international market.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states and generating an emotional arc, and means for applying a cultural emotional filter to adjust the input document based on different cultural backgrounds. This enables effective and efficient scenario adjustment to provide emotional resonance to diverse audiences.

[0064] "Text data" refers to electronic documents and information that are entered as strings of characters.

[0065] "Analysis" is the process of breaking down input data and understanding its structure and meaning.

[0066] "Emotional state" refers to the type and intensity of emotion inferred from the words and expressions contained in a document or content.

[0067] "Visualization" refers to the visual representation of data and information using charts and graphs.

[0068] An "emotion arc" refers to a graph-like representation that shows changes in emotions along a timeline within a scenario.

[0069] "Scenario structure" refers to the elements that make up the development and flow of a story in a film or other visual work.

[0070] "Character settings" are detailed descriptions of the characteristics and backgrounds of the characters appearing in a story.

[0071] A "cultural emotional filter" refers to methods and processes for adjusting content to take into account differences in how emotions are perceived across different cultures.

[0072] "User interface" refers to the screens and means of operation that allow a system and a user to interact with each other.

[0073] "Visual display" refers to providing information or data in a form that can be recognized visually.

[0074] "Optimization" refers to the process of adjusting or improving a system or process to maximize its performance according to a specific purpose.

[0075] To implement this invention, the user first uses a terminal to input text data for a scenario and sends it to the server. The user can create the scenario using a text editor via a touchscreen or keyboard. The server receives this input data and starts analyzing it using a natural language processing engine (e.g., SpaCy or NLTK).

[0076] During analysis, the server uses natural language processing to detect keywords and emotional expressions within the scenario, and extracts emotional states from the document based on these. Furthermore, it visualizes changes in emotional states as emotional arcs. This allows users to intuitively understand, through a graph, where emotional peaks occur in the scenario.

[0077] The system then analyzes the generated emotional arc and the entire scenario, and makes suggestions to make the story structure more effective. The server uses a generation AI model in this process, for example, suggesting modifications to deepen the characters' emotions and the relationships between those emotions. It also applies a cultural emotion filter to adjust the content to be more appropriate for audiences from different cultural backgrounds. This adjustment is customized to the user's target market.

[0078] For example, when a user inputs a science fiction movie script for international distribution, the server suggests emphasizing the emotional aspect of futuristic technology and adjusting the main characters to resonate more with teenagers worldwide. These suggestions are displayed on the device in a way that is easy for the user to understand and select.

[0079] An example of a prompt might be: "Analyze a science fiction movie script and make suggestions to maximize emotional resonance. In particular, emphasize the emotions surrounding futuristic technology and propose a coming-of-age story that will resonate with international teenagers."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user inputs text data for the scenario via their terminal. In this example, the input text data is created using a text editor and then sent to the server using the submit button. The input content is text data that requires sentiment analysis, such as a movie script.

[0083] Step 2:

[0084] The server retrieves the received scenario data and begins analysis using a natural language processing engine. Specifically, it uses natural language processing libraries such as SpaCy and NLTK. From the text data input, the server extracts keywords and phrases related to emotions and processes the data by continuously listing the emotional states of each scene. This results in the output of the emotional intensity for each scene.

[0085] Step 3:

[0086] The server generates an emotion arc based on the extracted emotional states. By plotting the emotional data obtained in the analysis step along a time axis, the overall flow of emotions is graphed. This generates visual data showing emotional fluctuations and peaks. The output is a visualized emotion arc.

[0087] Step 4:

[0088] The server combines emotional arc analysis with overall scenario analysis to generate suggestions for optimizing the emotional flow of the script. Here, a generative AI model is used to generate ideas for strengthening character emotions and the connections between scenes. Inputs are emotional arcs and scenario data, and output is a list of scenario revision suggestions.

[0089] Step 5:

[0090] The server applies a cultural emotional filter to accommodate different cultural backgrounds. At this stage, it adjusts the dialogue and scene structure so that the input scenario resonates emotionally across different cultural contexts. The output is a culturally adapted revised scenario.

[0091] Step 6:

[0092] The terminal visually displays the emotion arcs and suggested content provided by the server on the user interface. Specifically, the user can view the emotion arc graph and suggested changes on the terminal screen and make selections or edits. The input is suggested data from the server, and the output is the scenario confirmed and selected by the user.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In the development of video scripts, it has been difficult with conventional technologies to efficiently generate content that maximizes emotional resonance with viewers, adapts to international cultural contexts, and is acceptable to diverse audiences. This invention aims to solve these problems and provide a system that supports the distribution of emotionally rich and culturally relevant content.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for analyzing documents input as data and extracting emotional states; means for visualizing the extracted emotional states over time and generating an emotional arc; and means for internationally distributing emotionally rich content based on the generated emotional arc. This makes it possible to improve emotional resonance with viewers and efficiently provide scenarios that take into account different cultural backgrounds.

[0098] "Data" refers to a collection of information, such as text or numerical data, expressed in a specific format.

[0099] "Emotional state" refers to the expression of human feelings, analyzed from specific combinations of words or sentences within a document.

[0100] "The flow of time" refers to the continuity of events and processes that occur over time.

[0101] An "emotional arc" is a diagram that visually represents the changes and progression of emotions within a work of art.

[0102] "Structure" refers to the basic framework and arrangement of a work or system, as well as its overall form and shape.

[0103] "Culture" is a collection of lifestyles, values, customs, and other elements that are characteristic of a particular group or region.

[0104] "Background" refers to the underlying information and assumptions in a particular situation or environment.

[0105] "Correction" is the process of making changes to improve something.

[0106] "Means" refer to the methods or tools used to achieve an objective.

[0107] "Viewers" are people who observe and experience content.

[0108] "Content distribution" refers to the act of providing users with information and media in various forms, either online or offline.

[0109] To implement this invention, the user first sends a document as input data using a terminal. The server receives this document and extracts the emotional state through a data processing program. This process uses natural language processing software such as Python's spaCy and Transformers. The analyzed emotional state is visualized by the server along the flow of time, and an emotional arc is generated.

[0110] Furthermore, the server analyzes the generated emotional arc and uses data processing algorithms to propose internationally acceptable scenarios to provide the user with optimal structural modification suggestions. These suggestions adjust for differences in emotional expression across cultures and process the data to evoke empathy even when viewers have different backgrounds.

[0111] The device displays generated emotion arcs and suggestions to the user in a user-friendly format via a user interface. The user then edits the scenario based on these suggestions and delivers it as final content.

[0112] As a concrete example, when a user develops a movie script and inputs a futuristic scenario, the server helps to create a work that can be enjoyed without discomfort by adjusting the emotional arc that visually evokes fear. For example, by using a prompt such as, "Tell me a scenario that will excite and resonate with sci-fi fans from different cultures," the server will provide suggestions tailored to the emotions and situations of the characters.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user inputs text data for the scenario using a terminal and sends it to the server. The input data is a text file in document format and contains an abstract scenario.

[0116] Step 2:

[0117] The server analyzes the received text data using a natural language processing engine (e.g., spaCy, Transformers). At this stage, morphological analysis and dictionary-based sentiment analysis are performed to extract emotional states within the text. As a result, sentiment labels associated with each segment of the text are generated.

[0118] Step 3:

[0119] Based on the analysis results, the server generates an emotion arc along a timeline. Here, emotion labels are arranged chronologically for visualization, creating graph data that clearly shows the changes in emotion. This graph visually represents the flow of emotions throughout the entire scenario.

[0120] Step 4:

[0121] The server analyzes the generated emotional arc and proposes optimizations for the scenario structure. This process takes into account emotional receptivity in different cultures and generates scenario adjustments, including improvements to the placement of specific scenes and lines of dialogue. The suggestions are tailored to accommodate different cultural backgrounds.

[0122] Step 5:

[0123] The device displays suggested emotion arcs and scenario revisions to the user via a user interface. The user can intuitively manipulate this data and edit the scenario. Specifically, it provides an interface that allows revisions to be applied via drag-and-drop.

[0124] Step 6:

[0125] The user sends the edited scenario to the server, where the final scenario structure is saved. The server exports this structure as an optimized content format and prepares it for distribution. The output format is saved in a file format suitable for video production and distribution.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention provides a method for recognizing a user's emotional state and reflecting it in the scenario creation process by combining an emotion engine with a system that supports the creation of video scripts. The following describes specific embodiments of the invention.

[0128] The user inputs text data of the scenario into the server using a terminal. The server uses a natural language processing engine to analyze this text data and extract the emotional state in each scene. Based on this extraction, the server generates an emotion arc that visualizes the flow of emotions.

[0129] This system incorporates an emotion engine that monitors the user's emotional state in real time. The emotion engine analyzes user feedback and user reactions captured through the user interface to identify the user's emotional state. This user emotional information is then used to suggest scenario modifications. Based on this emotional state, the server presents appropriate scenario modifications and adjusts them to resonate with the user's emotions.

[0130] Furthermore, the emotion engine tracks the user's emotional changes in real time and dynamically adjusts the generation of emotion arcs. This enables scenario generation that reflects immediate feedback based on the user's reactions. The device also intuitively visualizes emotion arcs and suggested modifications, presenting them to the user on the user interface. Users can then refer to these suggestions and adjust the scenarios accordingly.

[0131] For example, if a user is creating a horror movie script, the emotion engine detects the user's peak fear and tension levels and suggests corresponding script adjustments. As a result, the server adjusts the emotion arc and constructs the optimal story structure to deliver the best possible horror experience to the audience.

[0132] Thus, this invention, which combines an emotion engine, enables the creation of more personal and emotionally resonant scenarios, streamlining the production of video works that evoke empathy from viewers.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user inputs the scenario text data into their device and sends it to the server. In addition, the user specifies their target emotion and intended genre.

[0136] Step 2:

[0137] The server analyzes the received scenario text data using a natural language processing engine and extracts the emotional state of each scene. Based on the analysis results, it generates an emotional profile for the entire document.

[0138] Step 3:

[0139] The server generates an emotion arc based on profiling. The emotion arc is used to visualize how emotions change over time in the story.

[0140] Step 4:

[0141] The server uses an emotion engine to recognize the user's real-time emotional state. This is done by collecting user responses on the user interface and analyzing their emotional state.

[0142] Step 5:

[0143] The server customizes scenario modification suggestions based on the user's emotional state. These suggestions include scenes that emphasize emotional peaks and dialogue changes to bridge emotional gaps.

[0144] Step 6:

[0145] The device visually displays the generated emotion arc and suggested revisions on the user interface, allowing users to easily review the proposed changes.

[0146] Step 7:

[0147] Users review the suggestions via their devices and adjust the scenarios. They can adopt the suggestions as needed, or make their own changes and send them back to the server as feedback.

[0148] Step 8:

[0149] The server receives user feedback in real time, dynamically adjusts emotional arcs and suggested scenario revisions, and then generates and saves the final scenario.

[0150] Step 9:

[0151] The terminal outputs the final generated scenario in a formatted format and provides it to the user. This allows the user to download or share the revised scenario.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0154] In contemporary video production, a challenge exists in creating scenarios that directly resonate with the user's emotions. In particular, there is a need to capture the user's physiological responses in real time and dynamically adjust the emotional arc and content of the scenario based on those responses. Achieving this will enable the creation of more personalized and emotionally resonant video works.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for analyzing material input as text information to extract emotions, means for visualizing the extracted emotions in a continuous time frame to generate a performance structure, and means for generating appropriate content structure modification suggestions using the performance structure. This makes it possible to reflect the user's emotions and physiological indicators in real time and dynamically adjust the scenario of the visual work.

[0157] "Text information" refers to character and document data entered through the user's device.

[0158] "Data" refers to the collection or content of text information that the system analyzes.

[0159] "Emotion" refers to the characteristics of a person's psychological state or feelings extracted from the input text information.

[0160] "Performance structure" refers to a format that visually represents changes and flows of emotions, generated from the analysis of text information.

[0161] "Content structure" refers to the flow and arrangement of scenarios proposed by the system.

[0162] A "user" refers to an individual who utilizes a system, provides input information, and receives suggestions from the system.

[0163] A "server" refers to a central computing device that performs data analysis and processing.

[0164] This system assists in scenario creation based on text information entered by users using their terminals. Users input text information about video works via their terminals, and this information is sent to the server. The server uses a natural language processing engine to analyze the input text information. Existing natural language processing libraries such as SpaCy and NLTK are used for this analysis.

[0165] From the analyzed text information, the server extracts emotions and generates a performance structure to visualize those emotions. This structure represents emotional fluctuations along a timeline. The server then generates suggestions for appropriate content structure revisions based on the performance structure. This incorporates an emotion engine to capture the user's psychological responses in real time, and may utilize IBM Watson® Tone Analyzer, an emotion analysis tool.

[0166] The device monitors the user's physiological indicators and feedback in real time and sends this as emotional data to the server. Based on the received emotional data, the server adjusts the scenario and immediately updates the suggested revisions and the presentation structure. This result is displayed on the device, and the user can improve the scenario based on the information presented.

[0167] For example, when a user is writing a romantic movie script, the server can use the emotion engine to suggest romantic scene settings that correspond to the user's heightened emotions. An example of this prompt would be: "The user is writing the end of a love scene. When the emotion engine detects that the user is feeling very happy, what scenario adjustments should it suggest?"

[0168] This system enables the creation of scenarios that resonate with users' emotions and provides concrete means for efficiently constructing emotionally rich video works.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses a terminal to input text information for the scenario. This input is then sent to the server via the network. This user action initiates the scenario creation process.

[0172] Step 2:

[0173] The server analyzes the received text information. Specifically, it uses a natural language processing engine to extract sentiment data from the text information. The input here is text information, and the output is the extracted sentiment data. Natural language processing libraries such as SpaCy and NLTK are used for this process.

[0174] Step 3:

[0175] The server generates a performance structure based on the extracted emotional data. It creates an emotional arc along a timeline as a way to visualize the transition of emotions. The input is emotional data, and the output is the performance structure. This process visually organizes the emotional flow of text data.

[0176] Step 4:

[0177] The device monitors the user's physiological indicators and feedback in real time and transmits this data to a server. The input is the user's physiological data, and the output is emotional data for analysis. This utilizes heart rate sensors and facial recognition technology using cameras.

[0178] Step 5:

[0179] The server generates appropriate revision suggestions for the content structure based on user sentiment data transmitted in real time. It utilizes a sentiment engine to create scenario adjustment proposals that reflect the user's emotional state. The input is user sentiment data, and the output is revision suggestions.

[0180] Step 6:

[0181] The server dynamically adjusts the scenario based on the suggested revisions and the production structure, and sends the results to the terminal. The input is the suggested revisions and the production structure, and the output is the visualization data of the adjusted scenario. The terminal presents this to the user, visually showing them options for revising the scenario in an easy-to-understand way. This allows the user to improve the scenario based on the information presented.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In screenwriting for video productions, accurately analyzing the flow of emotions contained within the script and adjusting the narrative structure based on that analysis is necessary to evoke emotional resonance from viewers. However, traditional methods rely heavily on the creator's subjectivity, making it difficult to effectively reflect the emotions of viewers. Furthermore, there is a lack of methods for incorporating viewer feedback into the script in real time, highlighting the need for efforts to improve the quality of the work.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states over a continuous period to generate an emotional arc, and means for generating appropriate revision suggestions for the narrative structure using the emotional arc. This enables creators to adjust scenarios based on the emotions of viewers, making it possible to produce video works with a higher emotional resonance.

[0187] "Text data" refers to structured written information in a format that computers can understand.

[0188] "Emotional state" refers to a person's feelings at a specific time, and includes emotional categories such as joy, sadness, and surprise.

[0189] "Visualization" refers to the process of representing data and extracted information in a way that is intuitively easy for humans to understand.

[0190] An "emotional arc" is a curve or diagram that visually represents changes in emotions within a story or scenario.

[0191] "Narrative structure" refers to the elements and sequence of events that make up a story, and it is through these elements that the flow and intent of the story are constructed.

[0192] A "revision proposal" refers to specific suggestions or instructions for making improvements or changes to the current structure or content.

[0193] "Real-time" refers to a state where data is acquired and processed instantly without delay.

[0194] "Feedback" refers to responses or opinions regarding specific actions or results, and includes providing information for future improvement and optimization.

[0195] "User interface" refers to the visual and operational means and screen design that allow users to interact with a system.

[0196] "Editing" refers to the process of modifying, adding to, or deleting existing content to optimize it.

[0197] "Viewer ratings" refer to the collective opinions and evaluations of viewers regarding a video work, and serve as an indicator for measuring the quality and emotional impact of the work.

[0198] The system required to realize this invention consists of a server and a user terminal. The server implements a natural language processing engine to analyze text data input from the user. Suitable engines include Python's Natural Language Toolkit (NLTK) and spaCy. Emotional states are extracted from the analyzed text data, and an emotion arc is generated based on the results. This emotion arc is displayed in the system's user interface to visually represent the emotional flow of the story.

[0199] To capture user emotions in real time, an emotion engine is installed on the server. This engine is built as a machine learning model using TENSORFLOW® and other technologies, and analyzes user feedback and reactions to identify emotional states in real time. Taking these emotional states into account, the server generates appropriate story revision suggestions and provides them to the user through the user interface.

[0200] Furthermore, viewer feedback is collected, and the server uses this as new training data to generate suggestions for improving the scenario. Through this process, users can emotionally optimize the structure of the story.

[0201] For example, if a viewer provides feedback such as, "I want more tension in this scene," the server analyzes this feedback and suggests revisions to the user to enhance the story's tension.

[0202] An example of a prompt using a generative AI model would be, "Please tell me how to emphasize the tension in the climax of the story." This type of prompt allows the AI ​​to generate specific suggestions for revisions.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives text data sent from the user's terminal. This input text data is a story scenario. To analyze the received text data, the server uses a natural language processing engine to perform syntactic analysis and extract the emotional state contained in each sentence. The output of the analysis includes emotional labels and scores for each sentence.

[0206] Step 2:

[0207] The server generates an emotion arc that visualizes the emotional flow of the entire story, based on the emotional states extracted in Step 1. The input is emotion labels and scores, which are visualized over a continuous period. For data processing, labels and scores are plotted on a time axis to create a curve that visualizes emotional changes. This curve is displayed as an emotion arc on the user interface.

[0208] Step 3:

[0209] The server uses the generated emotion arc to produce revision suggestions for the story structure. This process identifies parts of the emotion arc that should be emphasized and areas that could be improved, and outputs suggested revisions to the user. These suggestions may include adding scenes to increase the tension of the story or adjusting the intensity of emotions.

[0210] Step 4:

[0211] The user's device displays emotion arcs and revision suggestions sent from the server in the user interface. The user can then edit the story based on this information. Specifically, the user can accept suggested revisions or make their own changes on the interface, and the edits are sent to the server.

[0212] Step 5:

[0213] The server collects feedback from viewers and uses it as new training data. The input consists of viewer ratings and comments. The server analyzes this data and uses it to improve the accuracy of suggested revisions to the AI ​​model. Based on this data, it can regenerate prompts and suggest improvements to the user.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] This invention is a system for generating screenplays for video works that maximize emotional resonance with the viewer. Specific embodiments for carrying out the invention are described below.

[0231] First, the user inputs the scenario text data via their device and sends it to the server. Upon receiving this scenario data, the server uses a natural language processing engine to analyze the document and extract the emotional state in each scene. This visualizes the emotional fluctuations contained in the scenario and generates an emotional arc.

[0232] Next, the server analyzes the generated emotional arc and creates suggestions to optimize the emotional flow of the script. These suggestions may include ideas for rearranging specific scenes, adjusting dialogue, and strengthening character backgrounds. The server also analyzes the characters' emotional expressions and actions and provides additional information and suggestions for modifications to deepen the relationships between the characters.

[0233] Furthermore, the system has a function to apply cultural emotional filters, taking the international market into consideration. The server takes into account how emotions are perceived in different cultural spheres and adjusts dialogue and scenes so that the scenario content is suitable for each culture. This adjustment is optimized according to the viewer's cultural background.

[0234] Suggestions from the server are displayed on the terminal, allowing users to easily review and select this information through a visual interface. The terminal graphically displays emotional arcs, enabling users to intuitively compare and select suggested scenarios and character settings.

[0235] As a concrete example, when a user inputs a movie script aiming for international distribution in the science fiction genre, the server adjusts the emotional arc to emphasize feelings of wonder and fear towards futuristic technology, and reconstructs the main character's growth process in a way that resonates with teenagers worldwide. As a result, the user revises the scenario based on the suggestions and completes the final script.

[0236] Thus, the present invention enhances the quality of scenario writing and makes it possible to streamline the production of video works that resonate with audiences worldwide.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user uses their device to input text data for the scenario and sends it to the server. The user also inputs additional information such as the scenario's purpose and target audience.

[0240] Step 2:

[0241] The server sends the received text data to a natural language processing engine to analyze the scenario's content. The server extracts the emotional state of each scene and organizes that information.

[0242] Step 3:

[0243] The server generates an emotion arc based on the organized emotion information. The emotion arc is a graph that visually represents changes in emotion over time.

[0244] Step 4:

[0245] The server analyzes the generated emotion arc data and creates suggestions for optimizing the emotional flow of the scenario. The server also provides suggestions for revising the scene structure and dialogue.

[0246] Step 5:

[0247] The server analyzes the emotional expressions and actions of each character within the scenario. The server generates new setting proposals and improvement suggestions to deepen the relationships between the characters.

[0248] Step 6:

[0249] The server uses a cultural sentiment filter to adapt scenarios for different cultural backgrounds. The filter suggests dialogues and scenes that take into account differences in cultural expression of emotion.

[0250] Step 7:

[0251] The terminal receives scenario suggestion information from the server and displays it on the user interface. The terminal visualizes the suggested content in an easy-to-understand way, along with a graph of emotion arcs.

[0252] Step 8:

[0253] Users compare the suggestions displayed on their device and select revisions or make their own modifications as needed. If a suggestion is accepted, they review the revisions and resubmit them to the server.

[0254] Step 9:

[0255] The server receives the final modifications from the user, generates the updated scenario, and saves it. The server then outputs the scenario in the specified format as needed and provides it to the terminal.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] When creating screenplays for video productions, it is essential to maximize the emotional resonance with viewers and efficiently design a script structure that is adapted to different cultural spheres. However, traditional methods require individually visualizing emotional arcs and making cultural adjustments, which is time-consuming and labor-intensive, and does not adequately optimize expression for the international market.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states and generating an emotional arc, and means for applying a cultural emotional filter to adjust the input document based on different cultural backgrounds. This enables effective and efficient scenario adjustment to provide emotional resonance to diverse audiences.

[0261] "Text data" refers to electronic documents and information that are entered as strings of characters.

[0262] "Analysis" is the process of breaking down input data and understanding its structure and meaning.

[0263] "Emotional state" refers to the type and intensity of emotion inferred from the words and expressions contained in a document or content.

[0264] "Visualization" refers to the visual representation of data and information using charts and graphs.

[0265] An "emotion arc" refers to a graph-like representation that shows changes in emotions along a timeline within a scenario.

[0266] "Scenario structure" refers to the elements that make up the development and flow of a story in a film or other visual work.

[0267] "Character settings" are detailed descriptions of the characteristics and backgrounds of the characters appearing in a story.

[0268] A "cultural emotional filter" refers to methods and processes for adjusting content to take into account differences in how emotions are perceived across different cultures.

[0269] "User interface" refers to the screens and means of operation that allow a system and a user to interact with each other.

[0270] "Visual display" refers to providing information or data in a form that can be recognized visually.

[0271] "Optimization" refers to the process of adjusting or improving a system or process to maximize its performance according to a specific purpose.

[0272] To implement this invention, the user first uses a terminal to input text data for a scenario and sends it to the server. The user can create the scenario using a text editor via a touchscreen or keyboard. The server receives this input data and starts analyzing it using a natural language processing engine (e.g., SpaCy or NLTK).

[0273] During analysis, the server uses natural language processing to detect keywords and emotional expressions within the scenario, and extracts emotional states from the document based on these. Furthermore, it visualizes changes in emotional states as emotional arcs. This allows users to intuitively understand, through a graph, where emotional peaks occur in the scenario.

[0274] The system then analyzes the generated emotional arc and the entire scenario, and makes suggestions to make the story structure more effective. The server uses a generation AI model in this process, for example, suggesting modifications to deepen the characters' emotions and the relationships between those emotions. It also applies a cultural emotion filter to adjust the content to be more appropriate for audiences from different cultural backgrounds. This adjustment is customized to the user's target market.

[0275] For example, when a user inputs a science fiction movie script for international distribution, the server suggests emphasizing the emotional aspect of futuristic technology and adjusting the main characters to resonate more with teenagers worldwide. These suggestions are displayed on the device in a way that is easy for the user to understand and select.

[0276] An example of a prompt might be: "Analyze a science fiction movie script and make suggestions to maximize emotional resonance. In particular, emphasize the emotions surrounding futuristic technology and propose a coming-of-age story that will resonate with international teenagers."

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user inputs the text data of the scenario via the terminal. The input text data is created using a text editor in a specific example and then sent to the server using the submission button. The content of the input is text data that requires sentiment analysis, such as a movie scenario.

[0280] Step 2:

[0281] The server obtains the received scenario data and starts analysis using a natural language processing engine. Here, specifically, natural language processing libraries such as SpaCy or NLTK are used. From the input of the text data, the server extracts keywords and phrases related to sentiment and performs data processing by continuously listing the sentiment states of each scene. As a result, the sentiment intensity for each scene is output.

[0282] Step 3:

[0283] Based on the extracted sentiment states, the server generates a sentiment arc. By plotting the sentiment data obtained in the analysis step along the time axis, the overall flow of sentiment is graphed. As a result, visual data indicating sentiment fluctuations and peaks is generated. The output is a visualized sentiment arc.

[0284] Step 4:

[0285] The server combines the analysis of the sentiment arc and the analysis of the entire scenario to generate a proposal to optimize the emotional flow of the script. Here, a generative AI model is used to create ideas for strengthening the emotions of the characters and the connections between scenes. The input is the sentiment arc and scenario data, and the output is a list of scenario amendments.

[0286] Step 5:

[0287] The server applies a cultural emotional filter to accommodate different cultural backgrounds. At this stage, it adjusts the dialogue and scene structure so that the input scenario resonates emotionally across different cultural contexts. The output is a culturally adapted revised scenario.

[0288] Step 6:

[0289] The terminal visually displays the emotion arcs and suggested content provided by the server on the user interface. Specifically, the user can view the emotion arc graph and suggested changes on the terminal screen and make selections or edits. The input is suggested data from the server, and the output is the scenario confirmed and selected by the user.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] In the development of video scripts, it has been difficult with conventional technologies to efficiently generate content that maximizes emotional resonance with viewers, adapts to international cultural contexts, and is acceptable to diverse audiences. This invention aims to solve these problems and provide a system that supports the distribution of emotionally rich and culturally relevant content.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for analyzing documents input as data and extracting emotional states; means for visualizing the extracted emotional states over time and generating an emotional arc; and means for internationally distributing emotionally rich content based on the generated emotional arc. This makes it possible to improve emotional resonance with viewers and efficiently provide scenarios that take into account different cultural backgrounds.

[0295] "Data" refers to a collection of information, such as text or numerical data, expressed in a specific format.

[0296] "Emotional state" refers to the expression of human feelings, analyzed from specific combinations of words or sentences within a document.

[0297] "The flow of time" refers to the continuity of events and processes that occur over time.

[0298] An "emotional arc" is a diagram that visually represents the changes and progression of emotions within a work of art.

[0299] "Structure" refers to the basic framework and arrangement of a work or system, as well as its overall form and shape.

[0300] "Culture" is a collection of lifestyles, values, customs, and other elements that are characteristic of a particular group or region.

[0301] "Background" refers to the underlying information and assumptions in a particular situation or environment.

[0302] "Correction" is the process of making changes to improve something.

[0303] "Means" refer to the methods or tools used to achieve an objective.

[0304] "Viewers" are people who observe and experience content.

[0305] "Content distribution" refers to the act of providing users with information and media in various forms, either online or offline.

[0306] To implement this invention, first, the user uses a terminal to send a document serving as input data. The server receives this document and extracts the emotional state through a data processing program. In this process, spaCy or Transformers of Python, which are natural language processing software, are used. The analyzed emotional state is visualized by the server along with the passage of time, generating an emotional arc.

[0307] Furthermore, the server analyzes the generated emotional arc and uses a data calculation algorithm to propose scenarios considering international acceptance in order to provide the user with proposals for modifying the optimal structure. This proposal adjusts the differences in emotional expressions in different cultures and processes the data so as to induce empathy even when viewers have different backgrounds.

[0308] The terminal displays the generated emotional arc and the proposed content to the user in a highly convenient form via the user interface. The user edits the scenario based on these proposals and distributes it as the final content.

[0309] As a specific example, when the user develops a movie scenario and inputs a scenario themed on the future, the server supports turning it into a work that viewers worldwide can enjoy without discomfort by adjusting the emotional arc that visually induces horror. For example, by using a prompt sentence such as "Teach me a scenario that excites and is easy for fans of SF from different cultures to empathize with," proposals can be obtained according to the emotions and situations of the characters.

[0310] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user uses a terminal to input the text data of the scenario and sends it to the server. The input data is a text file in document format, describing an abstract scenario.

[0313] Step 2:

[0314] The server analyzes the received text data using a natural language processing engine (e.g., spaCy, Transformers). At this stage, morphological analysis and dictionary-based sentiment analysis are performed to extract emotional states within the text. As a result, sentiment labels associated with each segment of the text are generated.

[0315] Step 3:

[0316] Based on the analysis results, the server generates an emotion arc along a timeline. Here, emotion labels are arranged chronologically for visualization, creating graph data that clearly shows the changes in emotion. This graph visually represents the flow of emotions throughout the entire scenario.

[0317] Step 4:

[0318] The server analyzes the generated emotional arc and proposes optimizations for the scenario structure. This process takes into account emotional receptivity in different cultures and generates scenario adjustments, including improvements to the placement of specific scenes and lines of dialogue. The suggestions are tailored to accommodate different cultural backgrounds.

[0319] Step 5:

[0320] The device displays suggested emotion arcs and scenario revisions to the user via a user interface. The user can intuitively manipulate this data and edit the scenario. Specifically, it provides an interface that allows revisions to be applied via drag-and-drop.

[0321] Step 6:

[0322] The user sends the edited scenario to the server, where the final scenario structure is saved. The server exports this structure as an optimized content format and prepares it for distribution. The output format is saved in a file format suitable for video production and distribution.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] This invention provides a method for recognizing a user's emotional state and reflecting it in the scenario creation process by combining an emotion engine with a system that supports the creation of video scripts. The following describes specific embodiments of the invention.

[0325] The user inputs text data of the scenario into the server using a terminal. The server uses a natural language processing engine to analyze this text data and extract the emotional state in each scene. Based on this extraction, the server generates an emotion arc that visualizes the flow of emotions.

[0326] This system incorporates an emotion engine that monitors the user's emotional state in real time. The emotion engine analyzes user feedback and user reactions captured through the user interface to identify the user's emotional state. This user emotional information is then used to suggest scenario modifications. Based on this emotional state, the server presents appropriate scenario modifications and adjusts them to resonate with the user's emotions.

[0327] Furthermore, the emotion engine tracks the user's emotional changes in real time and dynamically adjusts the generation of emotion arcs. This enables scenario generation that reflects immediate feedback based on the user's reactions. The device also intuitively visualizes emotion arcs and suggested modifications, presenting them to the user on the user interface. Users can then refer to these suggestions and adjust the scenarios accordingly.

[0328] For example, if a user is creating a horror movie script, the emotion engine detects the user's peak fear and tension levels and suggests corresponding script adjustments. As a result, the server adjusts the emotion arc and constructs the optimal story structure to deliver the best possible horror experience to the audience.

[0329] Thus, this invention, which combines an emotion engine, enables the creation of more personal and emotionally resonant scenarios, streamlining the production of video works that evoke empathy from viewers.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user inputs the scenario text data into their device and sends it to the server. In addition, the user specifies their target emotion and intended genre.

[0333] Step 2:

[0334] The server analyzes the received scenario text data using a natural language processing engine and extracts the emotional state of each scene. Based on the analysis results, it generates an emotional profile for the entire document.

[0335] Step 3:

[0336] The server generates an emotion arc based on profiling. The emotion arc is used to visualize how emotions change over time in the story.

[0337] Step 4:

[0338] The server uses an emotion engine to recognize the user's real-time emotional state. This is done by collecting user responses on the user interface and analyzing their emotional state.

[0339] Step 5:

[0340] The server customizes scenario modification suggestions based on the user's emotional state. These suggestions include scenes that emphasize emotional peaks and dialogue changes to bridge emotional gaps.

[0341] Step 6:

[0342] The device visually displays the generated emotion arc and suggested revisions on the user interface, allowing users to easily review the proposed changes.

[0343] Step 7:

[0344] Users review the suggestions via their devices and adjust the scenarios. They can adopt the suggestions as needed, or make their own changes and send them back to the server as feedback.

[0345] Step 8:

[0346] The server receives user feedback in real time, dynamically adjusts emotional arcs and suggested scenario revisions, and then generates and saves the final scenario.

[0347] Step 9:

[0348] The terminal outputs the final generated scenario in a formatted format and provides it to the user. This allows the user to download or share the revised scenario.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] In contemporary video production, a challenge exists in creating scenarios that directly resonate with the user's emotions. In particular, there is a need to capture the user's physiological responses in real time and dynamically adjust the emotional arc and content of the scenario based on those responses. Achieving this will enable the creation of more personalized and emotionally resonant video works.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for analyzing material input as text information to extract emotions, means for visualizing the extracted emotions in a continuous time frame to generate a performance structure, and means for generating appropriate content structure modification suggestions using the performance structure. This makes it possible to reflect the user's emotions and physiological indicators in real time and dynamically adjust the scenario of the visual work.

[0354] "Text information" refers to character and document data entered through the user's device.

[0355] "Data" refers to the collection or content of text information that the system analyzes.

[0356] "Emotion" refers to the characteristics of a person's psychological state or feelings extracted from the input text information.

[0357] "Performance structure" refers to a format that visually represents changes and flows of emotions, generated from the analysis of text information.

[0358] "Content structure" refers to the flow and arrangement of scenarios proposed by the system.

[0359] A "user" refers to an individual who utilizes a system, provides input information, and receives suggestions from the system.

[0360] A "server" refers to a central computing device that performs data analysis and processing.

[0361] This system assists in scenario creation based on text information entered by users using their terminals. Users input text information about video works via their terminals, and this information is sent to the server. The server uses a natural language processing engine to analyze the input text information. Existing natural language processing libraries such as SpaCy and NLTK are used for this analysis.

[0362] From the analyzed text information, the server extracts emotions and generates a performance structure to visualize those emotions. This structure represents emotional fluctuations along a timeline. The server then generates suggestions for appropriate content structure revisions based on the performance structure. This incorporates an emotion engine to capture the user's psychological responses in real time, and may utilize the IBM Watson Tone Analyzer, an emotion analysis tool.

[0363] The device monitors the user's physiological indicators and feedback in real time and sends this as emotional data to the server. Based on the received emotional data, the server adjusts the scenario and immediately updates the suggested revisions and the presentation structure. This result is displayed on the device, and the user can improve the scenario based on the information presented.

[0364] For example, when a user is writing a romantic movie script, the server can use the emotion engine to suggest romantic scene settings that correspond to the user's heightened emotions. An example of this prompt would be: "The user is writing the end of a love scene. When the emotion engine detects that the user is feeling very happy, what scenario adjustments should it suggest?"

[0365] This system enables the creation of scenarios that resonate with users' emotions and provides concrete means for efficiently constructing emotionally rich video works.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The user uses a terminal to input text information for the scenario. This input is then sent to the server via the network. This user action initiates the scenario creation process.

[0369] Step 2:

[0370] The server analyzes the received text information. Specifically, it uses a natural language processing engine to extract sentiment data from the text information. The input here is text information, and the output is the extracted sentiment data. Natural language processing libraries such as SpaCy and NLTK are used for this process.

[0371] Step 3:

[0372] The server generates a performance structure based on the extracted emotional data. It creates an emotional arc along a timeline as a way to visualize the transition of emotions. The input is emotional data, and the output is the performance structure. This process visually organizes the emotional flow of text data.

[0373] Step 4:

[0374] The device monitors the user's physiological indicators and feedback in real time and transmits this data to a server. The input is the user's physiological data, and the output is emotional data for analysis. This utilizes heart rate sensors and facial recognition technology using cameras.

[0375] Step 5:

[0376] The server generates appropriate revision suggestions for the content structure based on user sentiment data transmitted in real time. It utilizes a sentiment engine to create scenario adjustment proposals that reflect the user's emotional state. The input is user sentiment data, and the output is revision suggestions.

[0377] Step 6:

[0378] The server dynamically adjusts the scenario based on the suggested revisions and the production structure, and sends the results to the terminal. The input is the suggested revisions and the production structure, and the output is the visualization data of the adjusted scenario. The terminal presents this to the user, visually showing them options for revising the scenario in an easy-to-understand way. This allows the user to improve the scenario based on the information presented.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] In screenwriting for video productions, accurately analyzing the flow of emotions contained within the script and adjusting the narrative structure based on that analysis is necessary to evoke emotional resonance from viewers. However, traditional methods rely heavily on the creator's subjectivity, making it difficult to effectively reflect the emotions of viewers. Furthermore, there is a lack of methods for incorporating viewer feedback into the script in real time, highlighting the need for efforts to improve the quality of the work.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states over a continuous period to generate an emotional arc, and means for generating appropriate revision suggestions for the narrative structure using the emotional arc. This enables creators to adjust scenarios based on the emotions of viewers, making it possible to produce video works with a higher emotional resonance.

[0384] "Text data" refers to structured written information in a format that computers can understand.

[0385] "Emotional state" refers to a person's feelings at a specific time, and includes emotional categories such as joy, sadness, and surprise.

[0386] "Visualization" refers to the process of representing data and extracted information in a way that is intuitively easy for humans to understand.

[0387] An "emotional arc" is a curve or diagram that visually represents changes in emotions within a story or scenario.

[0388] "Narrative structure" refers to the elements and sequence of events that make up a story, and it is through these elements that the flow and intent of the story are constructed.

[0389] A "revision proposal" refers to specific suggestions or instructions for making improvements or changes to the current structure or content.

[0390] "Real-time" refers to a state where data is acquired and processed instantly without delay.

[0391] "Feedback" refers to responses or opinions regarding specific actions or results, and includes providing information for future improvement and optimization.

[0392] "User interface" refers to the visual and operational means and screen design that allow users to interact with a system.

[0393] "Editing" refers to the process of modifying, adding to, or deleting existing content to optimize it.

[0394] "Viewer ratings" refer to the collective opinions and evaluations of viewers regarding a video work, and serve as an indicator for measuring the quality and emotional impact of the work.

[0395] The system required to realize this invention consists of a server and a user terminal. The server implements a natural language processing engine to analyze text data input from the user. Suitable engines include Python's Natural Language Toolkit (NLTK) and spaCy. Emotional states are extracted from the analyzed text data, and an emotion arc is generated based on the results. This emotion arc is displayed in the system's user interface to visually represent the emotional flow of the story.

[0396] To capture user emotions in real time, an emotion engine is installed on the server. This engine is built as a machine learning model using TensorFlow and other tools, and it analyzes user feedback and reactions to identify emotional states in real time. Taking these emotional states into account, the server generates appropriate story revision suggestions and provides them to the user through the user interface.

[0397] Furthermore, viewer feedback is collected, and the server uses this as new training data to generate suggestions for improving the scenario. Through this process, users can emotionally optimize the structure of the story.

[0398] For example, if a viewer provides feedback such as, "I want more tension in this scene," the server analyzes this feedback and suggests revisions to the user to enhance the story's tension.

[0399] An example of a prompt using a generative AI model would be, "Please tell me how to emphasize the tension in the climax of the story." This type of prompt allows the AI ​​to generate specific suggestions for revisions.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The server receives text data sent from the user's terminal. This input text data is a story scenario. To analyze the received text data, the server uses a natural language processing engine to perform syntactic analysis and extract the emotional state contained in each sentence. The output of the analysis includes emotional labels and scores for each sentence.

[0403] Step 2:

[0404] The server generates an emotion arc that visualizes the emotional flow of the entire story, based on the emotional states extracted in Step 1. The input is emotion labels and scores, which are visualized over a continuous period. For data processing, labels and scores are plotted on a time axis to create a curve that visualizes emotional changes. This curve is displayed as an emotion arc on the user interface.

[0405] Step 3:

[0406] The server uses the generated emotion arc to produce revision suggestions for the story structure. This process identifies parts of the emotion arc that should be emphasized and areas that could be improved, and outputs suggested revisions to the user. These suggestions may include adding scenes to increase the tension of the story or adjusting the intensity of emotions.

[0407] Step 4:

[0408] The user's device displays emotion arcs and revision suggestions sent from the server in the user interface. The user can then edit the story based on this information. Specifically, the user can accept suggested revisions or make their own changes on the interface, and the edits are sent to the server.

[0409] Step 5:

[0410] The server collects feedback from viewers and uses it as new training data. The input consists of viewer ratings and comments. The server analyzes this data and uses it to improve the accuracy of suggested revisions to the AI ​​model. Based on this data, it can regenerate prompts and suggest improvements to the user.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] This invention is a system for generating screenplays for video works that maximize emotional resonance with the viewer. Specific embodiments for carrying out the invention are described below.

[0428] First, the user inputs the scenario text data via their device and sends it to the server. Upon receiving this scenario data, the server uses a natural language processing engine to analyze the document and extract the emotional state in each scene. This visualizes the emotional fluctuations contained in the scenario and generates an emotional arc.

[0429] Next, the server analyzes the generated emotional arc and creates suggestions to optimize the emotional flow of the script. These suggestions may include ideas for rearranging specific scenes, adjusting dialogue, and strengthening character backgrounds. The server also analyzes the characters' emotional expressions and actions and provides additional information and suggestions for modifications to deepen the relationships between the characters.

[0430] Furthermore, the system has a function to apply cultural emotional filters, taking the international market into consideration. The server takes into account how emotions are perceived in different cultural spheres and adjusts dialogue and scenes so that the scenario content is suitable for each culture. This adjustment is optimized according to the viewer's cultural background.

[0431] Suggestions from the server are displayed on the terminal, allowing users to easily review and select this information through a visual interface. The terminal graphically displays emotional arcs, enabling users to intuitively compare and select suggested scenarios and character settings.

[0432] As a concrete example, when a user inputs a movie script aiming for international distribution in the science fiction genre, the server adjusts the emotional arc to emphasize feelings of wonder and fear towards futuristic technology, and reconstructs the main character's growth process in a way that resonates with teenagers worldwide. As a result, the user revises the scenario based on the suggestions and completes the final script.

[0433] Thus, the present invention enhances the quality of scenario writing and makes it possible to streamline the production of video works that resonate with audiences worldwide.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The user uses their device to input text data for the scenario and sends it to the server. The user also inputs additional information such as the scenario's purpose and target audience.

[0437] Step 2:

[0438] The server sends the received text data to a natural language processing engine to analyze the scenario's content. The server extracts the emotional state of each scene and organizes that information.

[0439] Step 3:

[0440] The server generates an emotion arc based on the organized emotion information. The emotion arc is a graph that visually represents changes in emotion over time.

[0441] Step 4:

[0442] The server analyzes the generated emotion arc data and creates suggestions for optimizing the emotional flow of the scenario. The server also provides suggestions for revising the scene structure and dialogue.

[0443] Step 5:

[0444] The server analyzes the emotional expressions and actions of each character within the scenario. The server generates new setting proposals and improvement suggestions to deepen the relationships between the characters.

[0445] Step 6:

[0446] The server uses a cultural sentiment filter to adapt scenarios for different cultural backgrounds. The filter suggests dialogues and scenes that take into account differences in cultural expression of emotion.

[0447] Step 7:

[0448] The terminal receives scenario suggestion information from the server and displays it on the user interface. The terminal visualizes the suggested content in an easy-to-understand way, along with a graph of emotion arcs.

[0449] Step 8:

[0450] Users compare the suggestions displayed on their device and select revisions or make their own modifications as needed. If a suggestion is accepted, they review the revisions and resubmit them to the server.

[0451] Step 9:

[0452] The server receives the final modifications from the user, generates the updated scenario, and saves it. The server then outputs the scenario in the specified format as needed and provides it to the terminal.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] When creating screenplays for video productions, it is essential to maximize the emotional resonance with viewers and efficiently design a script structure that is adapted to different cultural spheres. However, traditional methods require individually visualizing emotional arcs and making cultural adjustments, which is time-consuming and labor-intensive, and does not adequately optimize expression for the international market.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states and generating an emotional arc, and means for applying a cultural emotional filter to adjust the input document based on different cultural backgrounds. This enables effective and efficient scenario adjustment to provide emotional resonance to diverse audiences.

[0458] "Text data" refers to electronic documents and information that are entered as strings of characters.

[0459] "Analysis" is the process of breaking down input data and understanding its structure and meaning.

[0460] "Emotional state" refers to the type and intensity of emotion inferred from the words and expressions contained in a document or content.

[0461] "Visualization" refers to the visual representation of data and information using charts and graphs.

[0462] An "emotion arc" refers to a graph-like representation that shows changes in emotions along a timeline within a scenario.

[0463] "Scenario structure" refers to the elements that make up the development and flow of a story in a film or other visual work.

[0464] "Character settings" are detailed descriptions of the characteristics and backgrounds of the characters appearing in a story.

[0465] A "cultural emotional filter" refers to methods and processes for adjusting content to take into account differences in how emotions are perceived across different cultures.

[0466] "User interface" refers to the screens and means of operation that allow a system and a user to interact with each other.

[0467] "Visual display" refers to providing information or data in a form that can be recognized visually.

[0468] "Optimization" refers to the process of adjusting or improving a system or process to maximize its performance according to a specific purpose.

[0469] To implement this invention, the user first uses a terminal to input text data for a scenario and sends it to the server. The user can create the scenario using a text editor via a touchscreen or keyboard. The server receives this input data and starts analyzing it using a natural language processing engine (e.g., SpaCy or NLTK).

[0470] During analysis, the server uses natural language processing to detect keywords and emotional expressions within the scenario, and extracts emotional states from the document based on these. Furthermore, it visualizes changes in emotional states as emotional arcs. This allows users to intuitively understand, through a graph, where emotional peaks occur in the scenario.

[0471] The system then analyzes the generated emotional arc and the entire scenario, and makes suggestions to make the story structure more effective. The server uses a generation AI model in this process, for example, suggesting modifications to deepen the characters' emotions and the relationships between those emotions. It also applies a cultural emotion filter to adjust the content to be more appropriate for audiences from different cultural backgrounds. This adjustment is customized to the user's target market.

[0472] For example, when a user inputs a science fiction movie script for international distribution, the server suggests emphasizing the emotional aspect of futuristic technology and adjusting the main characters to resonate more with teenagers worldwide. These suggestions are displayed on the device in a way that is easy for the user to understand and select.

[0473] An example of a prompt might be: "Analyze a science fiction movie script and make suggestions to maximize emotional resonance. In particular, emphasize the emotions surrounding futuristic technology and propose a coming-of-age story that will resonate with international teenagers."

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The user inputs text data for the scenario via their terminal. In this example, the input text data is created using a text editor and then sent to the server using the submit button. The input content is text data that requires sentiment analysis, such as a movie script.

[0477] Step 2:

[0478] The server retrieves the received scenario data and begins analysis using a natural language processing engine. Specifically, it uses natural language processing libraries such as SpaCy and NLTK. From the text data input, the server extracts keywords and phrases related to emotions and processes the data by continuously listing the emotional states of each scene. This results in the output of the emotional intensity for each scene.

[0479] Step 3:

[0480] The server generates an emotion arc based on the extracted emotional states. By plotting the emotional data obtained in the analysis step along a time axis, the overall flow of emotions is graphed. This generates visual data showing emotional fluctuations and peaks. The output is a visualized emotion arc.

[0481] Step 4:

[0482] The server combines emotional arc analysis with overall scenario analysis to generate suggestions for optimizing the emotional flow of the script. Here, a generative AI model is used to generate ideas for strengthening character emotions and the connections between scenes. Inputs are emotional arcs and scenario data, and output is a list of scenario revision suggestions.

[0483] Step 5:

[0484] The server applies a cultural emotional filter to accommodate different cultural backgrounds. At this stage, it adjusts the dialogue and scene structure so that the input scenario resonates emotionally across different cultural contexts. The output is a culturally adapted revised scenario.

[0485] Step 6:

[0486] The terminal visually displays the emotion arcs and suggested content provided by the server on the user interface. Specifically, the user can view the emotion arc graph and suggested changes on the terminal screen and make selections or edits. The input is suggested data from the server, and the output is the scenario confirmed and selected by the user.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] In the development of video scripts, it has been difficult with conventional technologies to efficiently generate content that maximizes emotional resonance with viewers, adapts to international cultural contexts, and is acceptable to diverse audiences. This invention aims to solve these problems and provide a system that supports the distribution of emotionally rich and culturally relevant content.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for analyzing documents input as data and extracting emotional states; means for visualizing the extracted emotional states over time and generating an emotional arc; and means for internationally distributing emotionally rich content based on the generated emotional arc. This makes it possible to improve emotional resonance with viewers and efficiently provide scenarios that take into account different cultural backgrounds.

[0492] "Data" refers to a collection of information, such as text or numerical data, expressed in a specific format.

[0493] "Emotional state" refers to the expression of human feelings, analyzed from specific combinations of words or sentences within a document.

[0494] "The flow of time" refers to the continuity of events and processes that occur over time.

[0495] An "emotional arc" is a diagram that visually represents the changes and progression of emotions within a work of art.

[0496] "Structure" refers to the basic framework and arrangement of a work or system, as well as its overall form and shape.

[0497] "Culture" is a collection of lifestyles, values, customs, and other elements that are characteristic of a particular group or region.

[0498] "Background" refers to the underlying information and assumptions in a particular situation or environment.

[0499] "Correction" is the process of making changes to improve something.

[0500] "Means" refer to the methods or tools used to achieve an objective.

[0501] "Viewers" are people who observe and experience content.

[0502] "Content distribution" refers to the act of providing users with information and media in various forms, either online or offline.

[0503] To implement this invention, the user first sends a document as input data using a terminal. The server receives this document and extracts the emotional state through a data processing program. This process uses natural language processing software such as Python's spaCy and Transformers. The analyzed emotional state is visualized by the server along the flow of time, and an emotional arc is generated.

[0504] Furthermore, the server analyzes the generated emotional arc and uses data processing algorithms to propose internationally acceptable scenarios to provide the user with optimal structural modification suggestions. These suggestions adjust for differences in emotional expression across cultures and process the data to evoke empathy even when viewers have different backgrounds.

[0505] The device displays generated emotion arcs and suggestions to the user in a user-friendly format via a user interface. The user then edits the scenario based on these suggestions and delivers it as final content.

[0506] As a concrete example, when a user develops a movie script and inputs a futuristic scenario, the server helps to create a work that can be enjoyed without discomfort by adjusting the emotional arc that visually evokes fear. For example, by using a prompt such as, "Tell me a scenario that will excite and resonate with sci-fi fans from different cultures," the server will provide suggestions tailored to the emotions and situations of the characters.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user inputs text data for the scenario using a terminal and sends it to the server. The input data is a text file in document format and contains an abstract scenario.

[0510] Step 2:

[0511] The server analyzes the received text data using a natural language processing engine (e.g., spaCy, Transformers). At this stage, morphological analysis and dictionary-based sentiment analysis are performed to extract emotional states within the text. As a result, sentiment labels associated with each segment of the text are generated.

[0512] Step 3:

[0513] Based on the analysis results, the server generates an emotion arc along a timeline. Here, emotion labels are arranged chronologically for visualization, creating graph data that clearly shows the changes in emotion. This graph visually represents the flow of emotions throughout the entire scenario.

[0514] Step 4:

[0515] The server analyzes the generated emotional arc and proposes optimizations for the scenario structure. This process takes into account emotional receptivity in different cultures and generates scenario adjustments, including improvements to the placement of specific scenes and lines of dialogue. The suggestions are tailored to accommodate different cultural backgrounds.

[0516] Step 5:

[0517] The device displays suggested emotion arcs and scenario revisions to the user via a user interface. The user can intuitively manipulate this data and edit the scenario. Specifically, it provides an interface that allows revisions to be applied via drag-and-drop.

[0518] Step 6:

[0519] The user sends the edited scenario to the server, where the final scenario structure is saved. The server exports this structure as an optimized content format and prepares it for distribution. The output format is saved in a file format suitable for video production and distribution.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention provides a method for recognizing a user's emotional state and reflecting it in the scenario creation process by combining an emotion engine with a system that supports the creation of video scripts. The following describes specific embodiments of the invention.

[0522] The user inputs text data of the scenario into the server using a terminal. The server uses a natural language processing engine to analyze this text data and extract the emotional state in each scene. Based on this extraction, the server generates an emotion arc that visualizes the flow of emotions.

[0523] This system incorporates an emotion engine that monitors the user's emotional state in real time. The emotion engine analyzes user feedback and user reactions captured through the user interface to identify the user's emotional state. This user emotional information is then used to suggest scenario modifications. Based on this emotional state, the server presents appropriate scenario modifications and adjusts them to resonate with the user's emotions.

[0524] Furthermore, the emotion engine tracks the user's emotional changes in real time and dynamically adjusts the generation of emotion arcs. This enables scenario generation that reflects immediate feedback based on the user's reactions. The device also intuitively visualizes emotion arcs and suggested modifications, presenting them to the user on the user interface. Users can then refer to these suggestions and adjust the scenarios accordingly.

[0525] For example, if a user is creating a horror movie script, the emotion engine detects the user's peak fear and tension levels and suggests corresponding script adjustments. As a result, the server adjusts the emotion arc and constructs the optimal story structure to deliver the best possible horror experience to the audience.

[0526] Thus, this invention, which combines an emotion engine, enables the creation of more personal and emotionally resonant scenarios, streamlining the production of video works that evoke empathy from viewers.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user inputs the scenario text data into their device and sends it to the server. In addition, the user specifies their target emotion and intended genre.

[0530] Step 2:

[0531] The server analyzes the received scenario text data using a natural language processing engine and extracts the emotional state of each scene. Based on the analysis results, it generates an emotional profile for the entire document.

[0532] Step 3:

[0533] The server generates an emotion arc based on profiling. The emotion arc is used to visualize how emotions change over time in the story.

[0534] Step 4:

[0535] The server uses an emotion engine to recognize the user's real-time emotional state. This is done by collecting user responses on the user interface and analyzing their emotional state.

[0536] Step 5:

[0537] The server customizes scenario modification suggestions based on the user's emotional state. These suggestions include scenes that emphasize emotional peaks and dialogue changes to bridge emotional gaps.

[0538] Step 6:

[0539] The device visually displays the generated emotion arc and suggested revisions on the user interface, allowing users to easily review the proposed changes.

[0540] Step 7:

[0541] Users review the suggestions via their devices and adjust the scenarios. They can adopt the suggestions as needed, or make their own changes and send them back to the server as feedback.

[0542] Step 8:

[0543] The server receives user feedback in real time, dynamically adjusts emotional arcs and suggested scenario revisions, and then generates and saves the final scenario.

[0544] Step 9:

[0545] The terminal outputs the final generated scenario in a formatted format and provides it to the user. This allows the user to download or share the revised scenario.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] In contemporary video production, a challenge exists in creating scenarios that directly resonate with the user's emotions. In particular, there is a need to capture the user's physiological responses in real time and dynamically adjust the emotional arc and content of the scenario based on those responses. Achieving this will enable the creation of more personalized and emotionally resonant video works.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes means for analyzing material input as text information to extract emotions, means for visualizing the extracted emotions in a continuous time frame to generate a performance structure, and means for generating appropriate content structure modification suggestions using the performance structure. This makes it possible to reflect the user's emotions and physiological indicators in real time and dynamically adjust the scenario of the visual work.

[0551] "Text information" refers to character and document data entered through the user's device.

[0552] "Data" refers to the collection or content of text information that the system analyzes.

[0553] "Emotion" refers to the characteristics of a person's psychological state or feelings extracted from the input text information.

[0554] "Performance structure" refers to a format that visually represents changes and flows of emotions, generated from the analysis of text information.

[0555] "Content structure" refers to the flow and arrangement of scenarios proposed by the system.

[0556] A "user" refers to an individual who utilizes a system, provides input information, and receives suggestions from the system.

[0557] A "server" refers to a central computing device that performs data analysis and processing.

[0558] This system assists in scenario creation based on text information entered by users using their terminals. Users input text information about video works via their terminals, and this information is sent to the server. The server uses a natural language processing engine to analyze the input text information. Existing natural language processing libraries such as SpaCy and NLTK are used for this analysis.

[0559] From the analyzed text information, the server extracts emotions and generates a performance structure to visualize those emotions. This structure represents emotional fluctuations along a timeline. The server then generates suggestions for appropriate content structure revisions based on the performance structure. This incorporates an emotion engine to capture the user's psychological responses in real time, and may utilize the IBM Watson Tone Analyzer, an emotion analysis tool.

[0560] The device monitors the user's physiological indicators and feedback in real time and sends this as emotional data to the server. Based on the received emotional data, the server adjusts the scenario and immediately updates the suggested revisions and the presentation structure. This result is displayed on the device, and the user can improve the scenario based on the information presented.

[0561] For example, when a user is writing a romantic movie script, the server can use the emotion engine to suggest romantic scene settings that correspond to the user's heightened emotions. An example of this prompt would be: "The user is writing the end of a love scene. When the emotion engine detects that the user is feeling very happy, what scenario adjustments should it suggest?"

[0562] This system enables the creation of scenarios that resonate with users' emotions and provides concrete means for efficiently constructing emotionally rich video works.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The user uses a terminal to input text information for the scenario. This input is then sent to the server via the network. This user action initiates the scenario creation process.

[0566] Step 2:

[0567] The server analyzes the received text information. Specifically, it uses a natural language processing engine to extract sentiment data from the text information. The input here is text information, and the output is the extracted sentiment data. Natural language processing libraries such as SpaCy and NLTK are used for this process.

[0568] Step 3:

[0569] The server generates a performance structure based on the extracted emotional data. It creates an emotional arc along a timeline as a way to visualize the transition of emotions. The input is emotional data, and the output is the performance structure. This process visually organizes the emotional flow of text data.

[0570] Step 4:

[0571] The device monitors the user's physiological indicators and feedback in real time and transmits this data to a server. The input is the user's physiological data, and the output is emotional data for analysis. This utilizes heart rate sensors and facial recognition technology using cameras.

[0572] Step 5:

[0573] The server generates appropriate revision suggestions for the content structure based on user sentiment data transmitted in real time. It utilizes a sentiment engine to create scenario adjustment proposals that reflect the user's emotional state. The input is user sentiment data, and the output is revision suggestions.

[0574] Step 6:

[0575] The server dynamically adjusts the scenario based on the suggested revisions and the production structure, and sends the results to the terminal. The input is the suggested revisions and the production structure, and the output is the visualization data of the adjusted scenario. The terminal presents this to the user, visually showing them options for revising the scenario in an easy-to-understand way. This allows the user to improve the scenario based on the information presented.

[0576] (Application Example 2)

[0577] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] In screenwriting for video productions, accurately analyzing the flow of emotions contained within the script and adjusting the narrative structure based on that analysis is necessary to evoke emotional resonance from viewers. However, traditional methods rely heavily on the creator's subjectivity, making it difficult to effectively reflect the emotions of viewers. Furthermore, there is a lack of methods for incorporating viewer feedback into the script in real time, highlighting the need for efforts to improve the quality of the work.

[0579] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0580] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states over a continuous period to generate an emotional arc, and means for generating appropriate revision suggestions for the narrative structure using the emotional arc. This enables creators to adjust scenarios based on the emotions of viewers, making it possible to produce video works with a higher emotional resonance.

[0581] "Text data" refers to structured written information in a format that computers can understand.

[0582] "Emotional state" refers to a person's feelings at a specific time, and includes emotional categories such as joy, sadness, and surprise.

[0583] "Visualization" refers to the process of representing data and extracted information in a way that is intuitively easy for humans to understand.

[0584] An "emotional arc" is a curve or diagram that visually represents changes in emotions within a story or scenario.

[0585] "Narrative structure" refers to the elements and sequence of events that make up a story, and it is through these elements that the flow and intent of the story are constructed.

[0586] A "revision proposal" refers to specific suggestions or instructions for making improvements or changes to the current structure or content.

[0587] "Real-time" refers to a state where data is acquired and processed instantly without delay.

[0588] "Feedback" refers to responses or opinions regarding specific actions or results, and includes providing information for future improvement and optimization.

[0589] "User interface" refers to the visual and operational means and screen design that allow users to interact with a system.

[0590] "Editing" refers to the process of modifying, adding to, or deleting existing content to optimize it.

[0591] "Viewer ratings" refer to the collective opinions and evaluations of viewers regarding a video work, and serve as an indicator for measuring the quality and emotional impact of the work.

[0592] The system required to realize this invention consists of a server and a user terminal. The server implements a natural language processing engine to analyze text data input from the user. Suitable engines include Python's Natural Language Toolkit (NLTK) and spaCy. Emotional states are extracted from the analyzed text data, and an emotion arc is generated based on the results. This emotion arc is displayed in the system's user interface to visually represent the emotional flow of the story.

[0593] To capture user emotions in real time, an emotion engine is installed on the server. This engine is built as a machine learning model using TensorFlow and other tools, and it analyzes user feedback and reactions to identify emotional states in real time. Taking these emotional states into account, the server generates appropriate story revision suggestions and provides them to the user through the user interface.

[0594] Furthermore, viewer feedback is collected, and the server uses this as new training data to generate suggestions for improving the scenario. Through this process, users can emotionally optimize the structure of the story.

[0595] For example, if a viewer provides feedback such as, "I want more tension in this scene," the server analyzes this feedback and suggests revisions to the user to enhance the story's tension.

[0596] An example of a prompt using a generative AI model would be, "Please tell me how to emphasize the tension in the climax of the story." This type of prompt allows the AI ​​to generate specific suggestions for revisions.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The server receives text data sent from the user's terminal. This input text data is a story scenario. To analyze the received text data, the server uses a natural language processing engine to perform syntactic analysis and extract the emotional state contained in each sentence. The output of the analysis includes emotional labels and scores for each sentence.

[0600] Step 2:

[0601] The server generates an emotion arc that visualizes the emotional flow of the entire story, based on the emotional states extracted in Step 1. The input is emotion labels and scores, which are visualized over a continuous period. For data processing, labels and scores are plotted on a time axis to create a curve that visualizes emotional changes. This curve is displayed as an emotion arc on the user interface.

[0602] Step 3:

[0603] The server uses the generated emotion arc to produce revision suggestions for the story structure. This process identifies parts of the emotion arc that should be emphasized and areas that could be improved, and outputs suggested revisions to the user. These suggestions may include adding scenes to increase the tension of the story or adjusting the intensity of emotions.

[0604] Step 4:

[0605] The user's device displays emotion arcs and revision suggestions sent from the server in the user interface. The user can then edit the story based on this information. Specifically, the user can accept suggested revisions or make their own changes on the interface, and the edits are sent to the server.

[0606] Step 5:

[0607] The server collects feedback from viewers and uses it as new training data. The input consists of viewer ratings and comments. The server analyzes this data and uses it to improve the accuracy of suggested revisions to the AI ​​model. Based on this data, it can regenerate prompts and suggest improvements to the user.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] This invention is a system for generating screenplays for video works that maximize emotional resonance with the viewer. Specific embodiments for carrying out the invention are described below.

[0626] First, the user inputs the scenario text data via their device and sends it to the server. Upon receiving this scenario data, the server uses a natural language processing engine to analyze the document and extract the emotional state in each scene. This visualizes the emotional fluctuations contained in the scenario and generates an emotional arc.

[0627] Next, the server analyzes the generated emotional arc and creates suggestions to optimize the emotional flow of the script. These suggestions may include ideas for rearranging specific scenes, adjusting dialogue, and strengthening character backgrounds. The server also analyzes the characters' emotional expressions and actions and provides additional information and suggestions for modifications to deepen the relationships between the characters.

[0628] Furthermore, the system has a function to apply cultural emotional filters, taking the international market into consideration. The server takes into account how emotions are perceived in different cultural spheres and adjusts dialogue and scenes so that the scenario content is suitable for each culture. This adjustment is optimized according to the viewer's cultural background.

[0629] Suggestions from the server are displayed on the terminal, allowing users to easily review and select this information through a visual interface. The terminal graphically displays emotional arcs, enabling users to intuitively compare and select suggested scenarios and character settings.

[0630] As a concrete example, when a user inputs a movie script aiming for international distribution in the science fiction genre, the server adjusts the emotional arc to emphasize feelings of wonder and fear towards futuristic technology, and reconstructs the main character's growth process in a way that resonates with teenagers worldwide. As a result, the user revises the scenario based on the suggestions and completes the final script.

[0631] Thus, the present invention enhances the quality of scenario writing and makes it possible to streamline the production of video works that resonate with audiences worldwide.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The user uses their device to input text data for the scenario and sends it to the server. The user also inputs additional information such as the scenario's purpose and target audience.

[0635] Step 2:

[0636] The server sends the received text data to a natural language processing engine to analyze the scenario's content. The server extracts the emotional state of each scene and organizes that information.

[0637] Step 3:

[0638] The server generates an emotion arc based on the organized emotion information. The emotion arc is a graph that visually represents changes in emotion over time.

[0639] Step 4:

[0640] The server analyzes the generated emotion arc data and creates suggestions for optimizing the emotional flow of the scenario. The server also provides suggestions for revising the scene structure and dialogue.

[0641] Step 5:

[0642] The server analyzes the emotional expressions and actions of each character within the scenario. The server generates new setting proposals and improvement suggestions to deepen the relationships between the characters.

[0643] Step 6:

[0644] The server uses a cultural sentiment filter to adapt scenarios for different cultural backgrounds. The filter suggests dialogues and scenes that take into account differences in cultural expression of emotion.

[0645] Step 7:

[0646] The terminal receives scenario suggestion information from the server and displays it on the user interface. The terminal visualizes the suggested content in an easy-to-understand way, along with a graph of emotion arcs.

[0647] Step 8:

[0648] Users compare the suggestions displayed on their device and select revisions or make their own modifications as needed. If a suggestion is accepted, they review the revisions and resubmit them to the server.

[0649] Step 9:

[0650] The server receives the final modifications from the user, generates the updated scenario, and saves it. The server then outputs the scenario in the specified format as needed and provides it to the terminal.

[0651] (Example 1)

[0652] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] When creating screenplays for video productions, it is essential to maximize the emotional resonance with viewers and efficiently design a script structure that is adapted to different cultural spheres. However, traditional methods require individually visualizing emotional arcs and making cultural adjustments, which is time-consuming and labor-intensive, and does not adequately optimize expression for the international market.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0655] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states and generating an emotional arc, and means for applying a cultural emotional filter to adjust the input document based on different cultural backgrounds. This enables effective and efficient scenario adjustment to provide emotional resonance to diverse audiences.

[0656] "Text data" refers to electronic documents and information that are entered as strings of characters.

[0657] "Analysis" is the process of breaking down input data and understanding its structure and meaning.

[0658] "Emotional state" refers to the type and intensity of emotion inferred from the words and expressions contained in a document or content.

[0659] "Visualization" refers to the visual representation of data and information using charts and graphs.

[0660] An "emotion arc" refers to a graph-like representation that shows changes in emotions along a timeline within a scenario.

[0661] "Scenario structure" refers to the elements that make up the development and flow of a story in a film or other visual work.

[0662] "Character settings" are detailed descriptions of the characteristics and backgrounds of the characters appearing in a story.

[0663] A "cultural emotional filter" refers to methods and processes for adjusting content to take into account differences in how emotions are perceived across different cultures.

[0664] "User interface" refers to the screens and means of operation that allow a system and a user to interact with each other.

[0665] "Visual display" refers to providing information or data in a form that can be recognized visually.

[0666] "Optimization" refers to the process of adjusting or improving a system or process to maximize its performance according to a specific purpose.

[0667] To implement this invention, the user first uses a terminal to input text data for a scenario and sends it to the server. The user can create the scenario using a text editor via a touchscreen or keyboard. The server receives this input data and starts analyzing it using a natural language processing engine (e.g., SpaCy or NLTK).

[0668] During analysis, the server uses natural language processing to detect keywords and emotional expressions within the scenario, and extracts emotional states from the document based on these. Furthermore, it visualizes changes in emotional states as emotional arcs. This allows users to intuitively understand, through a graph, where emotional peaks occur in the scenario.

[0669] The system then analyzes the generated emotional arc and the entire scenario, and makes suggestions to make the story structure more effective. The server uses a generation AI model in this process, for example, suggesting modifications to deepen the characters' emotions and the relationships between those emotions. It also applies a cultural emotion filter to adjust the content to be more appropriate for audiences from different cultural backgrounds. This adjustment is customized to the user's target market.

[0670] For example, when a user inputs a science fiction movie script for international distribution, the server suggests emphasizing the emotional aspect of futuristic technology and adjusting the main characters to resonate more with teenagers worldwide. These suggestions are displayed on the device in a way that is easy for the user to understand and select.

[0671] An example of a prompt might be: "Analyze a science fiction movie script and make suggestions to maximize emotional resonance. In particular, emphasize the emotions surrounding futuristic technology and propose a coming-of-age story that will resonate with international teenagers."

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The user inputs text data for the scenario via their terminal. In this example, the input text data is created using a text editor and then sent to the server using the submit button. The input content is text data that requires sentiment analysis, such as a movie script.

[0675] Step 2:

[0676] The server retrieves the received scenario data and begins analysis using a natural language processing engine. Specifically, it uses natural language processing libraries such as SpaCy and NLTK. From the text data input, the server extracts keywords and phrases related to emotions and processes the data by continuously listing the emotional states of each scene. This results in the output of the emotional intensity for each scene.

[0677] Step 3:

[0678] The server generates an emotion arc based on the extracted emotional states. By plotting the emotional data obtained in the analysis step along a time axis, the overall flow of emotions is graphed. This generates visual data showing emotional fluctuations and peaks. The output is a visualized emotion arc.

[0679] Step 4:

[0680] The server combines emotional arc analysis with overall scenario analysis to generate suggestions for optimizing the emotional flow of the script. Here, a generative AI model is used to generate ideas for strengthening character emotions and the connections between scenes. Inputs are emotional arcs and scenario data, and output is a list of scenario revision suggestions.

[0681] Step 5:

[0682] The server applies a cultural emotional filter to accommodate different cultural backgrounds. At this stage, it adjusts the dialogue and scene structure so that the input scenario resonates emotionally across different cultural contexts. The output is a culturally adapted revised scenario.

[0683] Step 6:

[0684] The terminal visually displays the emotion arcs and suggested content provided by the server on the user interface. Specifically, the user can view the emotion arc graph and suggested changes on the terminal screen and make selections or edits. The input is suggested data from the server, and the output is the scenario confirmed and selected by the user.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In the development of video scripts, it has been difficult with conventional technologies to efficiently generate content that maximizes emotional resonance with viewers, adapts to international cultural contexts, and is acceptable to diverse audiences. This invention aims to solve these problems and provide a system that supports the distribution of emotionally rich and culturally relevant content.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for analyzing documents input as data and extracting emotional states; means for visualizing the extracted emotional states over time and generating an emotional arc; and means for internationally distributing emotionally rich content based on the generated emotional arc. This makes it possible to improve emotional resonance with viewers and efficiently provide scenarios that take into account different cultural backgrounds.

[0690] "Data" refers to a collection of information, such as text or numerical data, expressed in a specific format.

[0691] "Emotional state" refers to the expression of human feelings, analyzed from specific combinations of words or sentences within a document.

[0692] "The flow of time" refers to the continuity of events and processes that occur over time.

[0693] An "emotional arc" is a diagram that visually represents the changes and progression of emotions within a work of art.

[0694] "Structure" refers to the basic framework and arrangement of a work or system, as well as its overall form and shape.

[0695] "Culture" is a collection of lifestyles, values, customs, and other elements that are characteristic of a particular group or region.

[0696] "Background" refers to the underlying information and assumptions in a particular situation or environment.

[0697] "Correction" is the process of making changes to improve something.

[0698] "Means" refer to the methods or tools used to achieve an objective.

[0699] "Viewers" are people who observe and experience content.

[0700] "Content distribution" refers to the act of providing users with information and media in various forms, either online or offline.

[0701] To implement this invention, the user first sends a document as input data using a terminal. The server receives this document and extracts the emotional state through a data processing program. This process uses natural language processing software such as Python's spaCy and Transformers. The analyzed emotional state is visualized by the server along the flow of time, and an emotional arc is generated.

[0702] Furthermore, the server analyzes the generated emotional arc and uses data processing algorithms to propose internationally acceptable scenarios to provide the user with optimal structural modification suggestions. These suggestions adjust for differences in emotional expression across cultures and process the data to evoke empathy even when viewers have different backgrounds.

[0703] The device displays generated emotion arcs and suggestions to the user in a user-friendly format via a user interface. The user then edits the scenario based on these suggestions and delivers it as final content.

[0704] As a concrete example, when a user develops a movie script and inputs a futuristic scenario, the server helps to create a work that can be enjoyed without discomfort by adjusting the emotional arc that visually evokes fear. For example, by using a prompt such as, "Tell me a scenario that will excite and resonate with sci-fi fans from different cultures," the server will provide suggestions tailored to the emotions and situations of the characters.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The user inputs text data for the scenario using a terminal and sends it to the server. The input data is a text file in document format and contains an abstract scenario.

[0708] Step 2:

[0709] The server analyzes the received text data using a natural language processing engine (e.g., spaCy, Transformers). At this stage, morphological analysis and dictionary-based sentiment analysis are performed to extract emotional states within the text. As a result, sentiment labels associated with each segment of the text are generated.

[0710] Step 3:

[0711] Based on the analysis results, the server generates an emotion arc along a timeline. Here, emotion labels are arranged chronologically for visualization, creating graph data that clearly shows the changes in emotion. This graph visually represents the flow of emotions throughout the entire scenario.

[0712] Step 4:

[0713] The server analyzes the generated emotional arc and proposes optimizations for the scenario structure. This process takes into account emotional receptivity in different cultures and generates scenario adjustments, including improvements to the placement of specific scenes and lines of dialogue. The suggestions are tailored to accommodate different cultural backgrounds.

[0714] Step 5:

[0715] The device displays suggested emotion arcs and scenario revisions to the user via a user interface. The user can intuitively manipulate this data and edit the scenario. Specifically, it provides an interface that allows revisions to be applied via drag-and-drop.

[0716] Step 6:

[0717] The user sends the edited scenario to the server, where the final scenario structure is saved. The server exports this structure as an optimized content format and prepares it for distribution. The output format is saved in a file format suitable for video production and distribution.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] This invention provides a method for recognizing a user's emotional state and reflecting it in the scenario creation process by combining an emotion engine with a system that supports the creation of video scripts. The following describes specific embodiments of the invention.

[0720] The user inputs text data of the scenario into the server using a terminal. The server uses a natural language processing engine to analyze this text data and extract the emotional state in each scene. Based on this extraction, the server generates an emotion arc that visualizes the flow of emotions.

[0721] This system incorporates an emotion engine that monitors the user's emotional state in real time. The emotion engine analyzes user feedback and user reactions captured through the user interface to identify the user's emotional state. This user emotional information is then used to suggest scenario modifications. Based on this emotional state, the server presents appropriate scenario modifications and adjusts them to resonate with the user's emotions.

[0722] Furthermore, the emotion engine tracks the user's emotional changes in real time and dynamically adjusts the generation of emotion arcs. This enables scenario generation that reflects immediate feedback based on the user's reactions. The device also intuitively visualizes emotion arcs and suggested modifications, presenting them to the user on the user interface. Users can then refer to these suggestions and adjust the scenarios accordingly.

[0723] For example, if a user is creating a horror movie script, the emotion engine detects the user's peak fear and tension levels and suggests corresponding script adjustments. As a result, the server adjusts the emotion arc and constructs the optimal story structure to deliver the best possible horror experience to the audience.

[0724] Thus, this invention, which combines an emotion engine, enables the creation of more personal and emotionally resonant scenarios, streamlining the production of video works that evoke empathy from viewers.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The user inputs the scenario text data into their device and sends it to the server. In addition, the user specifies their target emotion and intended genre.

[0728] Step 2:

[0729] The server analyzes the received scenario text data using a natural language processing engine and extracts the emotional state of each scene. Based on the analysis results, it generates an emotional profile for the entire document.

[0730] Step 3:

[0731] The server generates an emotion arc based on profiling. The emotion arc is used to visualize how emotions change over time in the story.

[0732] Step 4:

[0733] The server uses an emotion engine to recognize the user's real-time emotional state. This is done by collecting user responses on the user interface and analyzing their emotional state.

[0734] Step 5:

[0735] The server customizes scenario modification suggestions based on the user's emotional state. These suggestions include scenes that emphasize emotional peaks and dialogue changes to bridge emotional gaps.

[0736] Step 6:

[0737] The device visually displays the generated emotion arc and suggested revisions on the user interface, allowing users to easily review the proposed changes.

[0738] Step 7:

[0739] Users review the suggestions via their devices and adjust the scenarios. They can adopt the suggestions as needed, or make their own changes and send them back to the server as feedback.

[0740] Step 8:

[0741] The server receives user feedback in real time, dynamically adjusts emotional arcs and suggested scenario revisions, and then generates and saves the final scenario.

[0742] Step 9:

[0743] The terminal outputs the final generated scenario in a formatted format and provides it to the user. This allows the user to download or share the revised scenario.

[0744] (Example 2)

[0745] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In contemporary video production, a challenge exists in creating scenarios that directly resonate with the user's emotions. In particular, there is a need to capture the user's physiological responses in real time and dynamically adjust the emotional arc and content of the scenario based on those responses. Achieving this will enable the creation of more personalized and emotionally resonant video works.

[0747] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0748] In this invention, the server includes means for analyzing material input as text information to extract emotions, means for visualizing the extracted emotions in a continuous time frame to generate a performance structure, and means for generating appropriate content structure modification suggestions using the performance structure. This makes it possible to reflect the user's emotions and physiological indicators in real time and dynamically adjust the scenario of the visual work.

[0749] "Text information" refers to character and document data entered through the user's device.

[0750] "Data" refers to the collection or content of text information that the system analyzes.

[0751] "Emotion" refers to the characteristics of a person's psychological state or feelings extracted from the input text information.

[0752] "Performance structure" refers to a format that visually represents changes and flows of emotions, generated from the analysis of text information.

[0753] "Content structure" refers to the flow and arrangement of scenarios proposed by the system.

[0754] A "user" refers to an individual who utilizes a system, provides input information, and receives suggestions from the system.

[0755] A "server" refers to a central computing device that performs data analysis and processing.

[0756] This system assists in scenario creation based on text information entered by users using their terminals. Users input text information about video works via their terminals, and this information is sent to the server. The server uses a natural language processing engine to analyze the input text information. Existing natural language processing libraries such as SpaCy and NLTK are used for this analysis.

[0757] From the analyzed text information, the server extracts emotions and generates a performance structure to visualize those emotions. This structure represents emotional fluctuations along a timeline. The server then generates suggestions for appropriate content structure revisions based on the performance structure. This incorporates an emotion engine to capture the user's psychological responses in real time, and may utilize the IBM Watson Tone Analyzer, an emotion analysis tool.

[0758] The device monitors the user's physiological indicators and feedback in real time and sends this as emotional data to the server. Based on the received emotional data, the server adjusts the scenario and immediately updates the suggested revisions and the presentation structure. This result is displayed on the device, and the user can improve the scenario based on the information presented.

[0759] For example, when a user is writing a romantic movie script, the server can use the emotion engine to suggest romantic scene settings that correspond to the user's heightened emotions. An example of this prompt would be: "The user is writing the end of a love scene. When the emotion engine detects that the user is feeling very happy, what scenario adjustments should it suggest?"

[0760] This system enables the creation of scenarios that resonate with users' emotions and provides concrete means for efficiently constructing emotionally rich video works.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] The user uses a terminal to input text information for the scenario. This input is then sent to the server via the network. This user action initiates the scenario creation process.

[0764] Step 2:

[0765] The server analyzes the received text information. Specifically, it uses a natural language processing engine to extract sentiment data from the text information. The input here is text information, and the output is the extracted sentiment data. Natural language processing libraries such as SpaCy and NLTK are used for this process.

[0766] Step 3:

[0767] The server generates a performance structure based on the extracted emotional data. It creates an emotional arc along a timeline as a way to visualize the transition of emotions. The input is emotional data, and the output is the performance structure. This process visually organizes the emotional flow of text data.

[0768] Step 4:

[0769] The device monitors the user's physiological indicators and feedback in real time and transmits this data to a server. The input is the user's physiological data, and the output is emotional data for analysis. This utilizes heart rate sensors and facial recognition technology using cameras.

[0770] Step 5:

[0771] The server generates appropriate revision suggestions for the content structure based on user sentiment data transmitted in real time. It utilizes a sentiment engine to create scenario adjustment proposals that reflect the user's emotional state. The input is user sentiment data, and the output is revision suggestions.

[0772] Step 6:

[0773] The server dynamically adjusts the scenario based on the suggested revisions and the production structure, and sends the results to the terminal. The input is the suggested revisions and the production structure, and the output is the visualization data of the adjusted scenario. The terminal presents this to the user, visually showing them options for revising the scenario in an easy-to-understand way. This allows the user to improve the scenario based on the information presented.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In screenwriting for video productions, accurately analyzing the flow of emotions contained within the script and adjusting the narrative structure based on that analysis is necessary to evoke emotional resonance from viewers. However, traditional methods rely heavily on the creator's subjectivity, making it difficult to effectively reflect the emotions of viewers. Furthermore, there is a lack of methods for incorporating viewer feedback into the script in real time, highlighting the need for efforts to improve the quality of the work.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for analyzing a document input as text data to extract emotional states, means for visualizing the extracted emotional states over a continuous period to generate an emotional arc, and means for generating appropriate revision suggestions for the narrative structure using the emotional arc. This enables creators to adjust scenarios based on the emotions of viewers, making it possible to produce video works with a higher emotional resonance.

[0779] "Text data" refers to structured written information in a format that computers can understand.

[0780] "Emotional state" refers to a person's feelings at a specific time, and includes emotional categories such as joy, sadness, and surprise.

[0781] "Visualization" refers to the process of representing data and extracted information in a way that is intuitively easy for humans to understand.

[0782] An "emotional arc" is a curve or diagram that visually represents changes in emotions within a story or scenario.

[0783] "Narrative structure" refers to the elements and sequence of events that make up a story, and it is through these elements that the flow and intent of the story are constructed.

[0784] A "revision proposal" refers to specific suggestions or instructions for making improvements or changes to the current structure or content.

[0785] "Real-time" refers to a state where data is acquired and processed instantly without delay.

[0786] "Feedback" refers to responses or opinions regarding specific actions or results, and includes providing information for future improvement and optimization.

[0787] "User interface" refers to the visual and operational means and screen design that allow users to interact with a system.

[0788] "Editing" refers to the process of modifying, adding to, or deleting existing content to optimize it.

[0789] "Viewer ratings" refer to the collective opinions and evaluations of viewers regarding a video work, and serve as an indicator for measuring the quality and emotional impact of the work.

[0790] The system required to realize this invention consists of a server and a user terminal. The server implements a natural language processing engine to analyze text data input from the user. Suitable engines include Python's Natural Language Toolkit (NLTK) and spaCy. Emotional states are extracted from the analyzed text data, and an emotion arc is generated based on the results. This emotion arc is displayed in the system's user interface to visually represent the emotional flow of the story.

[0791] To capture user emotions in real time, an emotion engine is installed on the server. This engine is built as a machine learning model using TensorFlow and other tools, and it analyzes user feedback and reactions to identify emotional states in real time. Taking these emotional states into account, the server generates appropriate story revision suggestions and provides them to the user through the user interface.

[0792] Furthermore, viewer feedback is collected, and the server uses this as new training data to generate suggestions for improving the scenario. Through this process, users can emotionally optimize the structure of the story.

[0793] For example, if a viewer provides feedback such as, "I want more tension in this scene," the server analyzes this feedback and suggests revisions to the user to enhance the story's tension.

[0794] An example of a prompt using a generative AI model would be, "Please tell me how to emphasize the tension in the climax of the story." This type of prompt allows the AI ​​to generate specific suggestions for revisions.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server receives text data sent from the user's terminal. This input text data is a story scenario. To analyze the received text data, the server uses a natural language processing engine to perform syntactic analysis and extract the emotional state contained in each sentence. The output of the analysis includes emotional labels and scores for each sentence.

[0798] Step 2:

[0799] The server generates an emotion arc that visualizes the emotional flow of the entire story, based on the emotional states extracted in Step 1. The input is emotion labels and scores, which are visualized over a continuous period. For data processing, labels and scores are plotted on a time axis to create a curve that visualizes emotional changes. This curve is displayed as an emotion arc on the user interface.

[0800] Step 3:

[0801] The server uses the generated emotion arc to produce revision suggestions for the story structure. This process identifies parts of the emotion arc that should be emphasized and areas that could be improved, and outputs suggested revisions to the user. These suggestions may include adding scenes to increase the tension of the story or adjusting the intensity of emotions.

[0802] Step 4:

[0803] The user's device displays emotion arcs and revision suggestions sent from the server in the user interface. The user can then edit the story based on this information. Specifically, the user can accept suggested revisions or make their own changes on the interface, and the edits are sent to the server.

[0804] Step 5:

[0805] The server collects feedback from viewers and uses it as new training data. The input consists of viewer ratings and comments. The server analyzes this data and uses it to improve the accuracy of suggested revisions to the AI ​​model. Based on this data, it can regenerate prompts and suggest improvements to the user.

[0806] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0807] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0810] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0811] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0812] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0813] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0814] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0816] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0817] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0818] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0819] 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.

[0820] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0821] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0822] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0823] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0824] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0825] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A method for analyzing documents input as text data and extracting emotional states,

[0830] A means for visualizing the extracted emotional states over a continuous time frame and generating an emotional arc,

[0831] A means for generating appropriate scenario structure modification suggestions using the aforementioned emotional arc,

[0832] A method for analyzing characters' emotions and relationships, and based on that, proposing character settings and deepening relationships,

[0833] A means of adjusting input documents based on different cultural backgrounds, taking into account differences in emotional expression across cultures,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for providing a user interface, displaying generated emotion arcs and proposed scenario revisions, and allowing the user to select and edit them.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for saving the modified scenario structure and character settings and outputting them in a formatted form.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A method for analyzing documents input as text data and extracting emotional states,

[0842] A means for visualizing the extracted emotional states over a continuous time frame and generating an emotional arc,

[0843] A means for generating appropriate scenario structure modification suggestions using the aforementioned emotional arc,

[0844] A method for analyzing characters' emotions and relationships, and based on that, proposing character settings and deepening relationships,

[0845] A means of applying a cultural sentiment filter to adjust input documents based on different cultural backgrounds,

[0846] A means of providing an interface on a user terminal that visually displays emotion arcs and suggestions, and allows the user to select and edit them.

[0847] A means of generating suggestions to optimize the emotional flow of a script according to the target field,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising means for providing a visual interface that allows the user to intuitively view, select, and edit the details of a modified scenario.

[0851] (Claim 3)

[0852] The system according to claim 1, comprising means for saving optimized scenario structures and character settings and outputting information in a predetermined format.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means of analyzing documents entered as data and extracting emotional states,

[0856] A means for visualizing the extracted emotional state over time and generating an emotional arc,

[0857] A means of proposing a suitable structural modification by utilizing the aforementioned emotional arc,

[0858] A method for analyzing the emotions and relationships of the characters and proposing ways to deepen the setting and relationships based on that analysis,

[0859] A means to adjust input documents to different backgrounds, taking into account differences in emotional expression across cultures,

[0860] A means of internationally distributing emotionally rich content based on the generated emotional arc,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising a user interface that displays generated emotion arcs and suggested modifications, and means by which the user can select and edit them.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for saving the modified structure and settings and outputting them in a formalized form.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A method for analyzing data entered as text information to extract emotions,

[0869] A means for visualizing the extracted emotions within a continuous time frame and generating a performance structure,

[0870] A means for generating appropriate content structure revision proposals using the aforementioned production structure,

[0871] A means of monitoring emotions based on users' physiological indicators and feedback, and adjusting the content to reflect those emotions,

[0872] A means for immediately updating and visually presenting the adjusted content,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for providing information input, displaying generated production structures and proposed content revisions, and enabling users to select and edit them.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising means for saving modified content structure and configuration information and outputting it in a rated format.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] A method for analyzing documents input as text data and extracting emotional states,

[0881] A means for visualizing the extracted emotional states over a continuous period and generating an emotional arc,

[0882] A means for generating appropriate revisions to the narrative structure using the aforementioned emotional arc,

[0883] A means of capturing the user's emotional state in real time and suggesting adjustments to the story based on that emotional information,

[0884] A method for analyzing viewer ratings and opinions and generating suggestions for improving the story based on them,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for providing the user interface, displaying generated emotion arcs and proposed revisions to the story, and allowing the user to select and edit them.

[0888] (Claim 3)

[0889] The system according to claim 1, comprising means for saving the revised story structure and character settings and outputting them in a formatted format. [Explanation of symbols]

[0890] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for analyzing documents input as text data and extracting emotional states, A means for visualizing the extracted emotional states over a continuous time frame and generating an emotional arc, A means for generating appropriate scenario structure modification suggestions using the aforementioned emotional arc, A method for analyzing characters' emotions and relationships, and based on that, proposing character settings and deepening relationships, A means of adjusting input documents based on different cultural backgrounds, taking into account differences in emotional expression across cultures, A system that includes this.

2. The system according to claim 1, further comprising means for providing a user interface, displaying generated emotion arcs and proposed scenario revisions, and allowing the user to select and edit them.

3. The system according to claim 1, comprising means for saving the modified scenario structure and character settings and outputting them in a formatted form.

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