system

Abstract

We provide the system. [Solution] A data collection method for collecting information on local traditions and culture, Data preprocessing means for shaping and formatting the collected data, A story generation means that constructs a story using a generative model based on the aforementioned formatted data, A content distribution method for distributing the aforementioned constructed story as digital content, A monetization means for managing revenue obtained from the aforementioned digital content, 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to depopulation and population decline in a region, there is a problem that the traditions and cultures unique to the region are being lost. If this continues, the historical culture of the region will not be passed on to the next generation, and there is a high possibility that the potential as an economic activity and tourism resource in the region will also be lost. Therefore, appropriate technical means for effectively preserving the culture of the region and transmitting it to the outside are necessary.

Means for Solving the Problems

[0005] This invention provides a data collection means for collecting information on local traditions and culture. This means allows local residents and fieldworkers to efficiently handle data acquired on-site. Furthermore, the invention includes a data preprocessing means for shaping and formatting the collected data, and in particular has the function of converting audio data into text data. A story generation means that constructs a narrative using a generative model based on the shaped data can be used to create digital content that reflects the history and culture of the region. This enables effective communication to tourists and future generations and also provides a means of monetizing through digital content.

[0006] "Data collection methods" refer to the methods and techniques used to collect information about local traditions and culture.

[0007] "Data preprocessing means" refers to a function or process for shaping collected data and converting it into a usable format.

[0008] "Story generation methods" refer to techniques or methods for constructing stories or narratives using generative models based on formatted data.

[0009] "Content distribution methods" refer to the methods and technologies for providing digital content to users through appropriate platforms.

[0010] "Monetization methods" refer to the techniques and technologies used to manage or maximize revenue generated from digital content and services.

[0011] A "generative model" is an algorithm or program that learns from input data and generates new information or narratives.

[0012] "Natural language processing technology" is a field of technology that enables computers to understand and process human language.

[0013] Keyword extraction is the process of identifying and extracting words and phrases that are considered important from text data. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of the present invention has a configuration for effectively preserving local traditions and culture and distributing them as digital content. This system begins with users (local residents or fieldworkers) collecting local cultural information. Users record local information as audio or text on their devices and upload it to a server.

[0036] Next, the server formats the uploaded information using data preprocessing tools and converts it into a usable format. This process includes transcribing audio data into text and removing noise. Then, based on the formatted data, the server uses a generative model with story generation tools to construct local stories and narratives. For example, it might incorporate the background and details of traditional festivals held in the region and shape them into a story that is accessible to tourists.

[0037] The generated stories will be published as digital content on the platform using content distribution methods. Users can access this content through their devices and become interested in the local culture by experiencing it. This is expected to increase tourism and revitalize the local economy.

[0038] Furthermore, this system has monetization mechanisms and manages revenue from digital content. Revenue is reinvested in local cultural activities and future content creation. This cycle ensures the continuous preservation and passing on of local culture to future generations.

[0039] As a concrete example, consider the case where this system is used to digitize a traditional festival in a certain region. After collecting anecdotes related to the festival and interviews with participants, the data is processed on a server to generate a story. This story is then published on an application, and the content is structured to encourage viewers to participate in the festival. As a result, the number of festival participants increases, which can lead to economic support for the region.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users collect local cultural information on their devices. Specifically, they conduct interviews with local residents and record audio data and notes.

[0043] Step 2:

[0044] The device formats the cultural information it collects and uploads it to the server. Formatting includes compressing audio files and organizing text data.

[0045] Step 3:

[0046] The server processes the received data using data preprocessing tools. Audio data is converted to text using speech recognition, and noise is removed.

[0047] Step 4:

[0048] The server uses a generative model to construct a story based on the formatted data. Natural language processing techniques are used to extract keywords and compile the narrative.

[0049] Step 5:

[0050] The server formats the generated story as digital content and delivers it to the device using a content distribution method. This allows users to view the content on a digital platform.

[0051] Step 6:

[0052] Users access digital content and experience local culture through their devices. Feedback from users is collected.

[0053] Step 7:

[0054] The server analyzes feedback and manages the revenue generated through monetization methods. This revenue is then used to reinvest in local cultural activities and improve content.

[0055] (Example 1)

[0056] 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."

[0057] Local traditions and cultures are important assets that should be passed down through generations, but these valuable pieces of information are being lost due to the rapid urbanization and globalization of modern society. Furthermore, the digitalization of local cultures has not progressed sufficiently, and the information is preserved in a fragmented and incomplete form, making it difficult to understand local cultures and traditions and to attract new participants.

[0058] 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.

[0059] In this invention, the server includes information acquisition means for obtaining information on local traditions and culture, information preprocessing means for formatting and formalizing the acquired information, and story generation means for constructing a story using a generative model based on the formatted information. This makes it possible to effectively preserve and disseminate local traditions and culture.

[0060] "Means of information acquisition" refers to means of collecting or acquiring information about local traditions and culture.

[0061] "Information preprocessing means" are means for shaping acquired information and converting it into a format suitable for analysis and use.

[0062] A "generative model" is a set of algorithms and technologies used to generate new stories or content from input information.

[0063] A "narrative generation method" is a means of constructing a narrative using a generative model based on formatted information.

[0064] A "content distribution method" is a means of widely distributing a constructed digital narrative and making it accessible to users.

[0065] "Profit management tools" are means for managing and efficiently processing revenue generated from digital formats.

[0066] This invention is a system for effectively preserving local traditions and culture and distributing them as digital content. The embodiments for carrying out the invention are described below.

[0067] First, users take on the role of collecting local cultural information. They use devices such as smartphones and tablets to record information about festivals and traditional events through voice recording and text input apps. For example, they might participate in a local festival, record interviews with local residents as audio, and take text notes about the origins and details of the festival.

[0068] When users upload collected data to the server via their devices, the server uses data preprocessing tools to format the data. Audio data is converted to text using the Google® Cloud Speech-to-Text API, and data quality is improved by removing noise using speech processing software such as Audacity. The text data is also formatted to a standardized and parseable form.

[0069] The server uses a story generation AI model to construct a narrative from the formatted data. Specifically, it uses open-source natural language processing technology to automatically generate stories about local traditions and culture based on the given information. An example of a prompt used here would be, "Please create a story about a traditional local summer festival, including its historical background and unique characteristics."

[0070] Ultimately, the generated stories are published as digital content on the platform via the server's content distribution system. Users can access this content and experience local culture using devices such as smartphones and tablets. In this way, the invention makes it possible to effectively preserve and widely disseminate local traditions and culture.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user collects local cultural information. In this process, the user uses a smartphone or tablet to launch voice recording and text input applications. Specific inputs include audio interviews with festival participants and notes about the festival's origins. The output consists of audio and text files.

[0074] Step 2:

[0075] The terminal sends audio and text files acquired by the user to the server using its data transmission function. This step involves uploading the data to cloud storage using a stable internet connection. The input is the data file on the terminal, and the output is the data file stored on the server.

[0076] Step 3:

[0077] The server processes the uploaded data using data preprocessing tools. In the case of audio data, it is converted to text using the Google Cloud Speech-to-Text API as input. Furthermore, noise is removed using processing software such as Audacity, and high-quality text data is obtained as output. For text data as well, formatting and standardization are performed as input, and data ready for analysis is obtained as output.

[0078] Step 4:

[0079] The server uses a generative AI model with formatted text data to generate a story using a story generation method. The input is formatted cultural information, and the story is constructed based on this using open-source natural language processing technology. Specifically, a prompt is set such as, "Create a story about a traditional local summer festival, incorporating its historical background and unique characteristics." The output is the completed story data.

[0080] Step 5:

[0081] The server publishes the generated stories as digital content to the platform using content distribution methods. The input is pre-generated story data, which is converted into a digital format and distributed via websites and mobile applications. The output is content publicly available online.

[0082] Step 6:

[0083] Users access digital content published through the platform and experience local culture. They use their devices to launch applications and learn about ancient cultures and traditions by browsing content that interests them. The input is the URL of the content accessed through the device or links within the app, and the output is the knowledge and experience provided through the user's sight and hearing.

[0084] (Application Example 1)

[0085] 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."

[0086] In many regions, the transmission of traditions and culture is a challenge. To pass on this cultural heritage to future generations, it is necessary to communicate its appeal in a modern way and allow a wide range of people to experience it. However, technical challenges remain in the processes of collecting, processing, and disseminating cultural information. It is essential to establish efficient methods for carrying out these processes and to monetize them.

[0087] 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.

[0088] In this invention, the server includes data collection means, data preprocessing means, story generation means, user interface means, and monetization means. This makes it possible to efficiently collect information on local traditions and culture, format it, generate stories using natural language processing technology and generative AI models, and distribute them as digital content. Furthermore, users can easily access and experience it through their terminals, and revenue is managed to support further cultural activities in the region.

[0089] "Data collection means" refers to methods and devices for efficiently collecting information on local traditions and culture.

[0090] "Data preprocessing means" refers to the techniques and processes used to shape and format the collected information.

[0091] A "story generation method" is a technique for constructing a narrative using a generative model based on formatted data.

[0092] "Content distribution means" refers to methods and devices for providing a constructed story to users as digital content.

[0093] "User interface means" refers to interactive methods and platforms that allow users to access and experience digital content.

[0094] "Monetization methods" refer to methods and systems for managing revenue generated from digital content and using it to support local cultural activities.

[0095] The system of this invention has a configuration that combines multiple means. The user's terminal collects local cultural information, and the server processes that information. Here, audio and text data are managed using Python, and Pandas and NLTK are used for data cleaning. Audio data is converted to text format using the Google Cloud Speech-to-Text API, and then noise reduction is performed using LibROSA.

[0096] Based on the formatted data, the server uses a generative AI model to construct a story. This process utilizes Hugging Face's Transformers library and generates a story using keywords obtained through natural language processing. For example, it might assemble the history of a festival in a specific region into a story and present it to the user.

[0097] The generated stories are delivered through an application developed with React Native. Through the user interface, users can view and experience this content on their own devices. This enables digital experiences of local culture, increasing opportunities for more users to engage with that culture.

[0098] Through monetization methods, revenue generated from digital content is effectively managed and ultimately reinvested in local cultural activities. Through this cycle, the system plays a role in supporting the preservation and transmission of local culture.

[0099] A concrete example is the experience of a traditional cultural festival in a certain region. The user records and films the festival and interviews with participants, and the server generates a story based on this. An example of a prompt might be, "Using interviews and historical background about the cultural festival in this region, create a story about how a family can participate." Based on this prompt, the generative model constructs a story and provides the user with a new cultural experience.

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] Users collect local cultural information using their devices. Specifically, they record this information as audio or text. This collected information becomes input data and is uploaded to the server.

[0103] Step 2:

[0104] The server converts the uploaded audio data into text data using the Google Cloud Speech-to-Text API. The converted text data is then used as input to remove noise using the LibROSA library. The output is the cleaned text data.

[0105] Step 3:

[0106] The server uses Pandas and NLTK to further format the cleaned text data and extract the necessary information. This step primarily involves formatting the data and filtering out important information. The output is the formatted data.

[0107] Step 4:

[0108] The server takes formatted data as input and uses the Hugging Face Transformers library to run a generative AI model, generating a story using prompt text. Specifically, it employs natural language processing techniques to compile the story's content. The output is the generated story.

[0109] Step 5:

[0110] The generated story is delivered to the user's device by the server through an application developed with React Native. The user can view and experience the story as digital content through the interface. In this step, the generated story becomes visible to the user.

[0111] Step 6:

[0112] The server manages the revenue generated from the story's content using monetization methods. This includes data such as page views and advertising revenue. The managed revenue is reinvested to support local cultural activities. The output is a detailed revenue report.

[0113] 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.

[0114] The present invention's system aims to provide a more effective cultural experience by recognizing and utilizing user emotions during the process of collecting information on local traditions and culture and providing it as digital content. This system includes multiple processes, starting from the stage where the user collects local information using a terminal, and continuing until the server processes and distributes it.

[0115] First, the user uses a device to collect audio and text information from local residents. At this stage, the data obtained through interviews and fieldwork is stored on the device. Next, the device uploads the data to a server, and the formatted data is then formatted using data preprocessing tools.

[0116] On the server, based on the formatted data, a story generation system uses a generative model to construct local narratives. This process incorporates natural language processing and an emotion engine, enabling it to recognize and reflect user emotions in the story. For example, it can highlight cultural elements that the user is interested in or construct narratives that evoke specific emotions.

[0117] The generated story is delivered to the device as digital content through a content distribution system. The device displays this content for the user to view and analyzes the user's emotions in real time. This analysis is sent back to the server as feedback and used to further optimize the content.

[0118] As a concrete example, consider the case of digitizing a traditional dance from a certain region. By collecting anecdotes and music related to this dance, it is possible to create a presentation that changes the visual effects in response to the user's emotional reactions. This allows users to not only watch a video but also to have an emotionally engaging experience.

[0119] Finally, the system maximizes revenue from digital content using monetization methods. Revenue is generated through targeted advertising utilizing emotional data, and a portion of this can be reinvested in local cultural activities. This process ensures the cyclical preservation of local culture and economic sustainability.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users collect local cultural information using their devices. Specifically, they record information in audio and text format through conversations with local residents. This record is saved on the device and later sent to a server.

[0123] Step 2:

[0124] The device uploads the collected cultural information to the server. During this process, the data format is formatted and compressed, optimizing it for easier processing on the server side.

[0125] Step 3:

[0126] The server analyzes and formats the uploaded data using data preprocessing tools. In particular, audio data is converted into text data using speech recognition technology, and noise is removed.

[0127] Step 4:

[0128] The server uses a generative model to construct a story based on formatted data. This process utilizes an emotion engine to analyze user emotions and reflect them in the content. It generates a personalized narrative, such as highlighting themes that the user has shown interest in.

[0129] Step 5:

[0130] The server formats the story it has created as digital content. This content is then delivered to the device via a content distribution method, allowing the user to experience it.

[0131] Step 6:

[0132] The user views digital content through a device. During this time, the device collects emotional data in real time from the user's facial expressions and vocalizations and transmits it to a server.

[0133] Step 7:

[0134] The server collects and analyzes sentiment data, and uses the results to improve and optimize content. Revenue is managed through monetization methods, delivering targeted ads based on user sentiment data. A portion of the revenue is reinvested in local cultural activities.

[0135] (Example 2)

[0136] 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 as the "terminal".

[0137] Traditional systems for collecting and distributing information on culture and customs often provided one-sided information without considering users' interests or feelings, making it difficult to offer users an effective cultural experience. Furthermore, the limited ways in which collected data could be utilized prevented the maximization of its value as diverse cultural content. This resulted in insufficient economic support for the protection and sustainable development of local cultures.

[0138] 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.

[0139] In this invention, the server includes information acquisition means, data processing means, narrative generation means, emotion analysis means, adaptation means, information distribution means, and profit management means. This makes it possible to generate and distribute stories about local culture and customs in a way that takes into account the user's emotions, enriching the user's experience, while simultaneously returning a portion of the profits obtained to local cultural activities.

[0140] "Information acquisition means" refers to devices and functions that collect information on local culture and customs, primarily through the collection of audio and text data.

[0141] "Data processing means" refers to a process that has the function of shaping and standardizing the format of collected information, and includes converting audio into text data.

[0142] A "narrative generation method" is a system that uses a generative model to create a story based on formatted information and utilizes natural language processing technology.

[0143] An "emotion analysis tool" is a process that has the function of recognizing and analyzing a user's emotions.

[0144] "Adaptation means" refers to a process that adjusts the content of the story based on the user's emotions obtained through emotion analysis means.

[0145] "Information distribution method" refers to the function of providing the created story to users as a digital medium.

[0146] A "profit management system" is a system that has the function of managing the profits obtained from digital media and appropriately returning them to local cultural activities.

[0147] This invention is a system for effectively processing information on local culture and customs through a series of processes from collection to distribution. This system is implemented by three main entities: users, terminals, and servers.

[0148] The user first uses a device to collect audio and text information from local residents. This device could be a smartphone or tablet, and data could be collected using voice recording and note-taking apps. For example, the user might conduct interviews about traditional local festivals, record the audio data on their smartphone, and save it as text data.

[0149] Next, the terminal uploads the collected data to the server. The server uses data processing tools to convert the audio data into text data and standardize the format. Here, speech recognition software and natural language processing tools are used to remove unnecessary information and maintain data integrity.

[0150] Subsequently, the server uses narrative generation tools to generate a story based on the formatted data. This process utilizes a generative AI model and constructs a meaningful story using prompts. For example, a prompt such as "Generate an inspiring story about the traditional dances of this region" can be input to the model.

[0151] Furthermore, the server recognizes the user's emotions in real time through emotion analysis means and adjusts the content of the generated story through adaptation means. By reflecting the user's emotions, a more personalized cultural experience can be provided. Then, using information distribution means, the adjusted story is delivered to the terminal as digital media, which the user can then view.

[0152] Finally, the server manages the profits generated from digital media using profit management mechanisms and reinvests them back into local cultural activities. This entire process creates a system that goes beyond mere digital content provision, aiming for the protection and sustainable development of local culture.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The user uses a device to collect information from local residents. During this process, the user conducts interviews using a smartphone voice recording app and saves the data as audio. The input is the audio obtained from conversations with local residents, and the output is an audio file saved on the device. This data forms the basis for subsequent processing.

[0156] Step 2:

[0157] The terminal uploads the collected audio data to the server. Here, the terminal uses an internet connection to send the audio files to the server. The input is the audio files stored on the terminal, and the output is the secure transfer of the audio data to the server. During this process, the integrity of the data is checked, and file names are changed or metadata is added as needed.

[0158] Step 3:

[0159] The server processes the received audio data. Using data processing tools, it converts the audio data into text and grammatically formats it. This step utilizes speech recognition technology to remove noise and standardize the text format. The input is audio data, and the output is formatted text data. This text data forms the basis for story generation.

[0160] Step 4:

[0161] The server generates a story from formatted text data using a generative AI model. This process utilizes pre-configured prompts to create a story aligned with the theme. For example, the model might be prompted with "Generate an inspiring story about the traditional dances of this region." The input consists of formatted text data and prompts, while the output is the generated story.

[0162] Step 5:

[0163] The server analyzes how the generated story evokes emotions in the user using emotion analysis tools. Here, emotion analysis algorithms are used to analyze the user's facial expressions and reaction data in real time. The input is the user's emotional data, and the output is a report based on their emotional response. This information is used for final adjustments to the story.

[0164] Step 6:

[0165] The server uses adaptive mechanisms to adjust the story content to reflect the results of sentiment analysis. Specifically, it emphasizes or modifies certain parts of the story to match the user's emotions. The input is the sentiment analysis report, and the output is the adjusted story. This adjustment provides a personalized user experience.

[0166] Step 7:

[0167] The server delivers the edited narrative as digital media to the terminal through an information distribution method. The terminal provides the user with the opportunity to receive and appreciate the content visually and aurally. The input is edited narrative data, and the output is digital content accessible to the user.

[0168] Step 8:

[0169] The server uses profit management mechanisms to manage the profits generated from distributed digital media and reinvest them back into local culture. Here, it monitors advertising revenue and user subscription models and distributes the results. The input is profit data obtained from digital content, and the output is that profits are appropriately managed and contributed to local culture.

[0170] (Application Example 2)

[0171] 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".

[0172] In recent years, there have been efforts to digitize and widely distribute local culture and traditions, but there is a challenge in providing effective cultural experiences that reflect the emotions of users. As a result, personalization of digital content and improvement of viewers' emotional satisfaction have not been fully achieved.

[0173] 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.

[0174] In this invention, the server includes information gathering means, data formatting means, and story generation means. This enables the generation and distribution of cultural content that takes into account the user's emotions.

[0175] "Information gathering means" refers to means that have the function of acquiring information about local traditions and culture and accumulating it as data.

[0176] A "data formatting tool" is a tool that has the function of arranging acquired information into a certain format and converting it into a form that is easy to process.

[0177] A "narrative generation method" is a means that has the function of constructing a narrative using a generative model based on formatted data.

[0178] An "information distribution method" is a means that has the function of delivering a constructed story to a user as digital information.

[0179] A "sentiment analysis tool" is a tool that has the function of analyzing the user's emotions when displaying information and collecting that data.

[0180] A "content adjustment method" is a means that dynamically changes the narrative based on collected emotional data, thereby personalizing the user's experience.

[0181] A "monetization tool" is a means that has the function of managing the income generated from digital information and returning it in an appropriate manner.

[0182] The system based on this invention collects information on local traditions and culture through various means and delivers stories based on that information to users, thereby providing a cultural experience that enriches emotions. Specific embodiments are described below.

[0183] First, the device is equipped with information gathering tools to collect audio and text information about local traditions and culture acquired by the user. This allows for the efficient collection of local information. This information is then formatted using data formatting tools and uploaded to the server.

[0184] The server uses a generative model based on formatted data and constructs a story using a narrative generation method. In this process, natural language processing techniques are used to interpret the text data and generate content that aligns with the story's theme. This results in the generation of relatable stories that can appeal to the user's emotions.

[0185] The generated stories are delivered to the device as digital information through an information distribution system. The device is equipped with sentiment analysis capabilities that analyze the user's emotions in real time as they view the information. The analysis results are sent to a server and used to adjust and personalize the next content.

[0186] For example, in a story that explains the history of music in a particular region in chapters, the visual effects are emphasized for chapters that particularly resonated with the user. Through this kind of emotional analysis, parts of the story are dynamically adjusted, providing a different experience for each user.

[0187] An example of a prompt message sent to the generation AI model might be: "Create a story based on the history of traditional local music. What additional effects should be added when the user's emotion is 'emotional'?" This results in a story structure that is tailored to the user experience.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] Users use their devices to collect audio and text information about local traditions and culture. The input is audio or text data, which is stored digitally on the device. The collected data serves as foundational data for subsequent processing.

[0191] Step 2:

[0192] The terminal formats the collected data through a data formatting mechanism. It receives audio and text information as input; audio data is converted to text data. The text data is then formatted and converted to a specified format. This output data is uploaded to the server.

[0193] Step 3:

[0194] The server receives formatted data and utilizes a generative AI model with a story generation mechanism. It receives formatted data as input and performs data calculations to construct the story using natural language processing techniques. After keyword extraction, template application, and theme setting, the story is generated.

[0195] Step 4:

[0196] The server transmits the generated story as digital information to the terminal via an information distribution system. The input is the generated story data, which is output in a format optimized for the terminal. This converts it into a format that the user can view on the terminal.

[0197] Step 5:

[0198] The device displays information while analyzing the user's emotions using emotion analysis tools. It receives user reactions and viewing data as input and generates output data that measures the emotional state in real time. This data is sent to the server as feedback.

[0199] Step 6:

[0200] The server receives sentiment analysis data and uses it via a content adjustment mechanism during the next story generation. It uses sentiment data as input to perform data calculations that modify the story's themes and expressive techniques. This enables the generation of more personalized stories.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] The system of the present invention has a configuration for effectively preserving local traditions and culture and distributing them as digital content. This system begins with users (local residents or fieldworkers) collecting local cultural information. Users record local information as audio or text on their devices and upload it to a server.

[0218] Next, the server formats the uploaded information using data preprocessing tools and converts it into a usable format. This process includes transcribing audio data into text and removing noise. Then, based on the formatted data, the server uses a generative model with story generation tools to construct local stories and narratives. For example, it might incorporate the background and details of traditional festivals held in the region and shape them into a story that is accessible to tourists.

[0219] The generated stories will be published as digital content on the platform using content distribution methods. Users can access this content through their devices and become interested in the local culture by experiencing it. This is expected to increase tourism and revitalize the local economy.

[0220] Furthermore, this system has monetization mechanisms and manages revenue from digital content. Revenue is reinvested in local cultural activities and future content creation. This cycle ensures the continuous preservation and passing on of local culture to future generations.

[0221] As a concrete example, consider the case where this system is used to digitize a traditional festival in a certain region. After collecting anecdotes related to the festival and interviews with participants, the data is processed on a server to generate a story. This story is then published on an application, and the content is structured to encourage viewers to participate in the festival. As a result, the number of festival participants increases, which can lead to economic support for the region.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] Users collect local cultural information on their devices. Specifically, they conduct interviews with local residents and record audio data and notes.

[0225] Step 2:

[0226] The device formats the cultural information it collects and uploads it to the server. Formatting includes compressing audio files and organizing text data.

[0227] Step 3:

[0228] The server processes the received data using data preprocessing tools. Audio data is converted to text using speech recognition, and noise is removed.

[0229] Step 4:

[0230] The server uses a generative model to construct a story based on the formatted data. Natural language processing techniques are used to extract keywords and compile the narrative.

[0231] Step 5:

[0232] The server formats the generated story as digital content and delivers it to the device using a content distribution method. This allows users to view the content on a digital platform.

[0233] Step 6:

[0234] Users access digital content and experience local culture through their devices. Feedback from users is collected.

[0235] Step 7:

[0236] The server analyzes feedback and manages the revenue generated through monetization methods. This revenue is then used to reinvest in local cultural activities and improve content.

[0237] (Example 1)

[0238] 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."

[0239] Local traditions and cultures are important assets that should be passed down through generations, but these valuable pieces of information are being lost due to the rapid urbanization and globalization of modern society. Furthermore, the digitalization of local cultures has not progressed sufficiently, and the information is preserved in a fragmented and incomplete form, making it difficult to understand local cultures and traditions and to attract new participants.

[0240] 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.

[0241] In this invention, the server includes information acquisition means for obtaining information on local traditions and culture, information preprocessing means for formatting and formalizing the acquired information, and story generation means for constructing a story using a generative model based on the formatted information. This makes it possible to effectively preserve and disseminate local traditions and culture.

[0242] "Means of information acquisition" refers to means of collecting or acquiring information about local traditions and culture.

[0243] "Information preprocessing means" are means for shaping acquired information and converting it into a format suitable for analysis and use.

[0244] A "generative model" is a set of algorithms and technologies used to generate new stories or content from input information.

[0245] A "narrative generation method" is a means of constructing a narrative using a generative model based on formatted information.

[0246] A "content distribution method" is a means of widely distributing a constructed digital narrative and making it accessible to users.

[0247] "Profit management tools" are means for managing and efficiently processing revenue generated from digital formats.

[0248] This invention is a system for effectively preserving local traditions and culture and distributing them as digital content. The embodiments for carrying out the invention are described below.

[0249] First, users take on the role of collecting local cultural information. They use devices such as smartphones and tablets to record information about festivals and traditional events through voice recording and text input apps. For example, they might participate in a local festival, record interviews with local residents as audio, and take text notes about the origins and details of the festival.

[0250] When users upload collected data to the server via their devices, the server uses data preprocessing tools to format the data. Audio data is converted to text using the Google Cloud Speech-to-Text API, and data quality is improved by removing noise using speech processing software such as Audacity. The text data is also formatted to a standardized and parseable form.

[0251] The server uses a story generation AI model to construct a narrative from the formatted data. Specifically, it uses open-source natural language processing technology to automatically generate stories about local traditions and culture based on the given information. An example of a prompt used here would be, "Please create a story about a traditional local summer festival, including its historical background and unique characteristics."

[0252] Ultimately, the generated stories are published as digital content on the platform via the server's content distribution system. Users can access this content and experience local culture using devices such as smartphones and tablets. In this way, the invention makes it possible to effectively preserve and widely disseminate local traditions and culture.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user collects local cultural information. In this process, the user uses a smartphone or tablet to launch voice recording and text input applications. Specific inputs include audio interviews with festival participants and notes about the festival's origins. The output consists of audio and text files.

[0256] Step 2:

[0257] The terminal sends audio and text files acquired by the user to the server using its data transmission function. This step involves uploading the data to cloud storage using a stable internet connection. The input is the data file on the terminal, and the output is the data file stored on the server.

[0258] Step 3:

[0259] The server processes the uploaded data using data preprocessing tools. In the case of audio data, it is converted to text using the Google Cloud Speech-to-Text API as input. Furthermore, noise is removed using processing software such as Audacity, and high-quality text data is obtained as output. For text data as well, formatting and standardization are performed as input, and data ready for analysis is obtained as output.

[0260] Step 4:

[0261] The server uses a generative AI model with formatted text data to generate a story using a story generation method. The input is formatted cultural information, and the story is constructed based on this using open-source natural language processing technology. Specifically, a prompt is set such as, "Create a story about a traditional local summer festival, incorporating its historical background and unique characteristics." The output is the completed story data.

[0262] Step 5:

[0263] The server publishes the generated stories as digital content to the platform using content distribution methods. The input is pre-generated story data, which is converted into a digital format and distributed via websites and mobile applications. The output is content publicly available online.

[0264] Step 6:

[0265] Users access digital content published through the platform and experience local culture. They use their devices to launch applications and learn about ancient cultures and traditions by browsing content that interests them. The input is the URL of the content accessed through the device or links within the app, and the output is the knowledge and experience provided through the user's sight and hearing.

[0266] (Application Example 1)

[0267] 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."

[0268] In many regions, the transmission of traditions and culture is a challenge. To pass on this cultural heritage to future generations, it is necessary to communicate its appeal in a modern way and allow a wide range of people to experience it. However, technical challenges remain in the processes of collecting, processing, and disseminating cultural information. It is essential to establish efficient methods for carrying out these processes and to monetize them.

[0269] 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.

[0270] In this invention, the server includes data collection means, data preprocessing means, story generation means, user interface means, and monetization means. This makes it possible to efficiently collect information on local traditions and culture, format it, generate stories using natural language processing technology and generative AI models, and distribute them as digital content. Furthermore, users can easily access and experience it through their terminals, and revenue is managed to support further cultural activities in the region.

[0271] "Data collection means" refers to methods and devices for efficiently collecting information on local traditions and culture.

[0272] "Data preprocessing means" refers to the techniques and processes used to shape and format the collected information.

[0273] A "story generation method" is a technique for constructing a narrative using a generative model based on formatted data.

[0274] "Content distribution means" refers to methods and devices for providing a constructed story to users as digital content.

[0275] "User interface means" refers to interactive methods and platforms that allow users to access and experience digital content.

[0276] "Monetization methods" refer to methods and systems for managing revenue generated from digital content and using it to support local cultural activities.

[0277] The system of this invention has a configuration that combines multiple means. The user's terminal collects local cultural information, and the server processes that information. Here, audio and text data are managed using Python, and Pandas and NLTK are used for data cleaning. Audio data is converted to text format using the Google Cloud Speech-to-Text API, and then noise reduction is performed using LibROSA.

[0278] Based on the formatted data, the server uses a generative AI model to construct a story. This process utilizes Hugging Face's Transformers library and generates a story using keywords obtained through natural language processing. For example, it might assemble the history of a festival in a specific region into a story and present it to the user.

[0279] The generated stories are delivered through an application developed with React Native. Through the user interface, users can view and experience this content on their own devices. This enables digital experiences of local culture, increasing opportunities for more users to engage with that culture.

[0280] Through monetization methods, revenue generated from digital content is effectively managed and ultimately reinvested in local cultural activities. Through this cycle, the system plays a role in supporting the preservation and transmission of local culture.

[0281] As a specific example, there is the experience of a traditional cultural festival in a certain region. The user records the festival scene and interviews with participants, and based on this, the server generates a story. As an example of a prompt sentence, an instruction like "Please create a story about how a family can participate in the cultural festival in this region using interviews and historical backgrounds." can be considered. Based on this prompt sentence, the generation model constructs a story and provides a new cultural experience to the user.

[0282] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0283] Step 1:

[0284] The user collects cultural information of the region using a terminal. Specifically, the user records the information as voice or text. This collected information becomes input data and is uploaded to the server.

[0285] Step 2:

[0286] The server converts the uploaded voice data into text data using the Google Cloud Speech-to-Text API. Using the converted text data as input, noise is removed with the LibROSA library. The output is the cleaned text data.

[0287] Step 3:

[0288] The server further formats the cleaned text data and extracts necessary information using Pandas and NLTK. In this step, mainly data format arrangement and important information filtering are performed. The output is the formatted data.

[0289] Step 4:

[0290] The server takes formatted data as input and uses the Hugging Face Transformers library to run a generative AI model, generating a story using prompt text. Specifically, it employs natural language processing techniques to compile the story's content. The output is the generated story.

[0291] Step 5:

[0292] The generated story is delivered to the user's device by the server through an application developed with React Native. The user can view and experience the story as digital content through the interface. In this step, the generated story becomes visible to the user.

[0293] Step 6:

[0294] The server manages the revenue generated from the story's content using monetization methods. This includes data such as page views and advertising revenue. The managed revenue is reinvested to support local cultural activities. The output is a detailed revenue report.

[0295] 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.

[0296] The present invention's system aims to provide a more effective cultural experience by recognizing and utilizing user emotions during the process of collecting information on local traditions and culture and providing it as digital content. This system includes multiple processes, starting from the stage where the user collects local information using a terminal, and continuing until the server processes and distributes it.

[0297] First, the user uses a device to collect audio and text information from local residents. At this stage, the data obtained through interviews and fieldwork is stored on the device. Next, the device uploads the data to a server, and the formatted data is then formatted using data preprocessing tools.

[0298] On the server, based on the formatted data, a story generation system uses a generative model to construct local narratives. This process incorporates natural language processing and an emotion engine, enabling it to recognize and reflect user emotions in the story. For example, it can highlight cultural elements that the user is interested in or construct narratives that evoke specific emotions.

[0299] The generated story is delivered to the device as digital content through a content distribution system. The device displays this content for the user to view and analyzes the user's emotions in real time. This analysis is sent back to the server as feedback and used to further optimize the content.

[0300] As a concrete example, consider the case of digitizing a traditional dance from a certain region. By collecting anecdotes and music related to this dance, it is possible to create a presentation that changes the visual effects in response to the user's emotional reactions. This allows users to not only watch a video but also to have an emotionally engaging experience.

[0301] Finally, the system maximizes revenue from digital content using monetization methods. Revenue is generated through targeted advertising utilizing emotional data, and a portion of this can be reinvested in local cultural activities. This process ensures the cyclical preservation of local culture and economic sustainability.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user collects cultural information of the region using a terminal. Specifically, through interaction with local residents, information is recorded in the form of voice or text. This record is saved on the terminal as it will be sent to the server later.

[0305] Step 2:

[0306] The terminal uploads the collected cultural information to the server. At this time, the data format is formatted and compressed, and optimized to facilitate processing on the server side.

[0307] Step 3:

[0308] The server analyzes and formats the uploaded data using data preprocessing means. Especially for voice data, it is converted into text data using voice recognition technology and noise is removed.

[0309] Step 4:

[0310] Based on the formatted data from the server, a story is constructed using a generation model. In this process, an emotion engine is utilized to analyze the user's emotions and reflect them in the content. A personalized narrative is generated, such as emphasizing the themes the user is interested in.

[0311] Step 5:

[0312] The server formats the constructed story as digital content. Then, it is distributed to the terminal by content distribution means so that the user can experience it.

[0313] Step 6:

[0314] The user views the digital content through the terminal. During this time, the terminal collects emotion data in real-time from the user's expressions and voices and sends it to the server.

[0315] Step 7:

[0316] The server collects and analyzes sentiment data, and uses the results to improve and optimize content. Revenue is managed through monetization methods, delivering targeted ads based on user sentiment data. A portion of the revenue is reinvested in local cultural activities.

[0317] (Example 2)

[0318] 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".

[0319] Traditional systems for collecting and distributing information on culture and customs often provided one-sided information without considering users' interests or feelings, making it difficult to offer users an effective cultural experience. Furthermore, the limited ways in which collected data could be utilized prevented the maximization of its value as diverse cultural content. This resulted in insufficient economic support for the protection and sustainable development of local cultures.

[0320] 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.

[0321] In this invention, the server includes information acquisition means, data processing means, narrative generation means, emotion analysis means, adaptation means, information distribution means, and profit management means. This makes it possible to generate and distribute stories about local culture and customs in a way that takes into account the user's emotions, enriching the user's experience, while simultaneously returning a portion of the profits obtained to local cultural activities.

[0322] "Information acquisition means" refers to devices and functions that collect information on local culture and customs, primarily through the collection of audio and text data.

[0323] "Data processing means" refers to a process that has the function of shaping and standardizing the format of collected information, and includes converting audio into text data.

[0324] A "narrative generation method" is a system that uses a generative model to create a story based on formatted information and utilizes natural language processing technology.

[0325] An "emotion analysis tool" is a process that has the function of recognizing and analyzing a user's emotions.

[0326] "Adaptation means" refers to a process that adjusts the content of the story based on the user's emotions obtained through emotion analysis means.

[0327] "Information distribution method" refers to the function of providing the created story to users as a digital medium.

[0328] A "profit management system" is a system that has the function of managing the profits obtained from digital media and appropriately returning them to local cultural activities.

[0329] This invention is a system for effectively processing information on local culture and customs through a series of processes from collection to distribution. This system is implemented by three main entities: users, terminals, and servers.

[0330] The user first uses a device to collect audio and text information from local residents. This device could be a smartphone or tablet, and data could be collected using voice recording and note-taking apps. For example, the user might conduct interviews about traditional local festivals, record the audio data on their smartphone, and save it as text data.

[0331] Next, the terminal uploads the collected data to the server. The server uses data processing tools to convert the audio data into text data and standardize the format. Here, speech recognition software and natural language processing tools are used to remove unnecessary information and maintain data integrity.

[0332] Subsequently, the server uses narrative generation tools to generate a story based on the formatted data. This process utilizes a generative AI model and constructs a meaningful story using prompts. For example, a prompt such as "Generate an inspiring story about the traditional dances of this region" can be input to the model.

[0333] Furthermore, the server recognizes the user's emotions in real time through emotion analysis means and adjusts the content of the generated story through adaptation means. By reflecting the user's emotions, a more personalized cultural experience can be provided. Then, using information distribution means, the adjusted story is delivered to the terminal as digital media, which the user can then view.

[0334] Finally, the server manages the profits generated from digital media using profit management mechanisms and reinvests them back into local cultural activities. This entire process creates a system that goes beyond mere digital content provision, aiming for the protection and sustainable development of local culture.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The user uses a device to collect information from local residents. During this process, the user conducts interviews using a smartphone voice recording app and saves the data as audio. The input is the audio obtained from conversations with local residents, and the output is an audio file saved on the device. This data forms the basis for subsequent processing.

[0338] Step 2:

[0339] The terminal uploads the collected audio data to the server. Here, the terminal uses an internet connection to send the audio files to the server. The input is the audio files stored on the terminal, and the output is the secure transfer of the audio data to the server. During this process, the integrity of the data is checked, and file names are changed or metadata is added as needed.

[0340] Step 3:

[0341] The server processes the received audio data. Using data processing tools, it converts the audio data into text and grammatically formats it. This step utilizes speech recognition technology to remove noise and standardize the text format. The input is audio data, and the output is formatted text data. This text data forms the basis for story generation.

[0342] Step 4:

[0343] The server generates a story from formatted text data using a generative AI model. This process utilizes pre-configured prompts to create a story aligned with the theme. For example, the model might be prompted with "Generate an inspiring story about the traditional dances of this region." The input consists of formatted text data and prompts, while the output is the generated story.

[0344] Step 5:

[0345] The server analyzes how the generated story evokes emotions in the user using emotion analysis tools. Here, emotion analysis algorithms are used to analyze the user's facial expressions and reaction data in real time. The input is the user's emotional data, and the output is a report based on their emotional response. This information is used for final adjustments to the story.

[0346] Step 6:

[0347] The server uses adaptive mechanisms to adjust the story content to reflect the results of sentiment analysis. Specifically, it emphasizes or modifies certain parts of the story to match the user's emotions. The input is the sentiment analysis report, and the output is the adjusted story. This adjustment provides a personalized user experience.

[0348] Step 7:

[0349] The server delivers the edited narrative as digital media to the terminal through an information distribution method. The terminal provides the user with the opportunity to receive and appreciate the content visually and aurally. The input is edited narrative data, and the output is digital content accessible to the user.

[0350] Step 8:

[0351] The server uses profit management mechanisms to manage the profits generated from distributed digital media and reinvest them back into local culture. Here, it monitors advertising revenue and user subscription models and distributes the results. The input is profit data obtained from digital content, and the output is that profits are appropriately managed and contributed to local culture.

[0352] (Application Example 2)

[0353] 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."

[0354] In recent years, there have been efforts to digitize and widely distribute local culture and traditions, but there is a challenge in providing effective cultural experiences that reflect the emotions of users. As a result, personalization of digital content and improvement of viewers' emotional satisfaction have not been fully achieved.

[0355] 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.

[0356] In this invention, the server includes information gathering means, data formatting means, and story generation means. This enables the generation and distribution of cultural content that takes into account the user's emotions.

[0357] "Information gathering means" refers to means that have the function of acquiring information about local traditions and culture and accumulating it as data.

[0358] A "data formatting tool" is a tool that has the function of arranging acquired information into a certain format and converting it into a form that is easy to process.

[0359] A "narrative generation method" is a means that has the function of constructing a narrative using a generative model based on formatted data.

[0360] An "information distribution method" is a means that has the function of delivering a constructed story to a user as digital information.

[0361] A "sentiment analysis tool" is a tool that has the function of analyzing the user's emotions when displaying information and collecting that data.

[0362] A "content adjustment method" is a means that dynamically changes the narrative based on collected emotional data, thereby personalizing the user's experience.

[0363] A "monetization tool" is a means that has the function of managing the income generated from digital information and returning it in an appropriate manner.

[0364] The system based on this invention collects information on local traditions and culture through various means and delivers stories based on that information to users, thereby providing a cultural experience that enriches emotions. Specific embodiments are described below.

[0365] First, the device is equipped with information gathering tools to collect audio and text information about local traditions and culture acquired by the user. This allows for the efficient collection of local information. This information is then formatted using data formatting tools and uploaded to the server.

[0366] The server uses a generative model based on formatted data and constructs a story using a narrative generation method. In this process, natural language processing techniques are used to interpret the text data and generate content that aligns with the story's theme. This results in the generation of relatable stories that can appeal to the user's emotions.

[0367] The generated stories are delivered to the device as digital information through an information distribution system. The device is equipped with sentiment analysis capabilities that analyze the user's emotions in real time as they view the information. The analysis results are sent to a server and used to adjust and personalize the next content.

[0368] For example, in a story that explains the history of music in a particular region in chapters, the visual effects are emphasized for chapters that particularly resonated with the user. Through this kind of emotional analysis, parts of the story are dynamically adjusted, providing a different experience for each user.

[0369] An example of a prompt message sent to the generation AI model might be: "Create a story based on the history of traditional local music. What additional effects should be added when the user's emotion is 'emotional'?" This results in a story structure that is tailored to the user experience.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] Users use their devices to collect audio and text information about local traditions and culture. The input is audio or text data, which is stored digitally on the device. The collected data serves as foundational data for subsequent processing.

[0373] Step 2:

[0374] The terminal formats the collected data through a data formatting mechanism. It receives audio and text information as input; audio data is converted to text data. The text data is then formatted and converted to a specified format. This output data is uploaded to the server.

[0375] Step 3:

[0376] The server receives formatted data and utilizes a generative AI model with a story generation mechanism. It receives formatted data as input and performs data calculations to construct the story using natural language processing techniques. After keyword extraction, template application, and theme setting, the story is generated.

[0377] Step 4:

[0378] The server transmits the generated story as digital information to the terminal via an information distribution system. The input is the generated story data, which is output in a format optimized for the terminal. This converts it into a format that the user can view on the terminal.

[0379] Step 5:

[0380] The device displays information while analyzing the user's emotions using emotion analysis tools. It receives user reactions and viewing data as input and generates output data that measures the emotional state in real time. This data is sent to the server as feedback.

[0381] Step 6:

[0382] The server receives sentiment analysis data and uses it via a content adjustment mechanism during the next story generation. It uses sentiment data as input to perform data calculations that modify the story's themes and expressive techniques. This enables the generation of more personalized stories.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] The system of the present invention has a configuration for effectively preserving local traditions and culture and distributing them as digital content. This system begins with users (local residents or fieldworkers) collecting local cultural information. Users record local information as audio or text on their devices and upload it to a server.

[0400] Next, the server formats the uploaded information using data preprocessing tools and converts it into a usable format. This process includes transcribing audio data into text and removing noise. Then, based on the formatted data, the server uses a generative model with story generation tools to construct local stories and narratives. For example, it might incorporate the background and details of traditional festivals held in the region and shape them into a story that is accessible to tourists.

[0401] The generated stories will be published as digital content on the platform using content distribution methods. Users can access this content through their devices and become interested in the local culture by experiencing it. This is expected to increase tourism and revitalize the local economy.

[0402] Furthermore, this system has monetization mechanisms and manages revenue from digital content. Revenue is reinvested in local cultural activities and future content creation. This cycle ensures the continuous preservation and passing on of local culture to future generations.

[0403] As a concrete example, consider the case where this system is used to digitize a traditional festival in a certain region. After collecting anecdotes related to the festival and interviews with participants, the data is processed on a server to generate a story. This story is then published on an application, and the content is structured to encourage viewers to participate in the festival. As a result, the number of festival participants increases, which can lead to economic support for the region.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] Users collect local cultural information on their devices. Specifically, they conduct interviews with local residents and record audio data and notes.

[0407] Step 2:

[0408] The device formats the cultural information it collects and uploads it to the server. Formatting includes compressing audio files and organizing text data.

[0409] Step 3:

[0410] The server processes the received data using data preprocessing tools. Audio data is converted to text using speech recognition, and noise is removed.

[0411] Step 4:

[0412] The server uses a generative model to construct a story based on the formatted data. Natural language processing techniques are used to extract keywords and compile the narrative.

[0413] Step 5:

[0414] The server formats the generated story as digital content and delivers it to the device using a content distribution method. This allows users to view the content on a digital platform.

[0415] Step 6:

[0416] Users access digital content and experience local culture through their devices. Feedback from users is collected.

[0417] Step 7:

[0418] The server analyzes feedback and manages the revenue generated through monetization methods. This revenue is then used to reinvest in local cultural activities and improve content.

[0419] (Example 1)

[0420] 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."

[0421] Local traditions and cultures are important assets that should be passed down through generations, but these valuable pieces of information are being lost due to the rapid urbanization and globalization of modern society. Furthermore, the digitalization of local cultures has not progressed sufficiently, and the information is preserved in a fragmented and incomplete form, making it difficult to understand local cultures and traditions and to attract new participants.

[0422] 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.

[0423] In this invention, the server includes information acquisition means for obtaining information on local traditions and culture, information preprocessing means for formatting and formalizing the acquired information, and story generation means for constructing a story using a generative model based on the formatted information. This makes it possible to effectively preserve and disseminate local traditions and culture.

[0424] "Means of information acquisition" refers to means of collecting or acquiring information about local traditions and culture.

[0425] "Information preprocessing means" are means for shaping acquired information and converting it into a format suitable for analysis and use.

[0426] A "generative model" is a set of algorithms and technologies used to generate new stories or content from input information.

[0427] A "narrative generation method" is a means of constructing a narrative using a generative model based on formatted information.

[0428] A "content distribution method" is a means of widely distributing a constructed digital narrative and making it accessible to users.

[0429] "Profit management tools" are means for managing and efficiently processing revenue generated from digital formats.

[0430] This invention is a system for effectively preserving local traditions and culture and distributing them as digital content. The embodiments for carrying out the invention are described below.

[0431] First, users take on the role of collecting local cultural information. They use devices such as smartphones and tablets to record information about festivals and traditional events through voice recording and text input apps. For example, they might participate in a local festival, record interviews with local residents as audio, and take text notes about the origins and details of the festival.

[0432] When users upload collected data to the server via their devices, the server uses data preprocessing tools to format the data. Audio data is converted to text using the Google Cloud Speech-to-Text API, and data quality is improved by removing noise using speech processing software such as Audacity. The text data is also formatted to a standardized and parseable form.

[0433] The server uses a story generation AI model to construct a narrative from the formatted data. Specifically, it uses open-source natural language processing technology to automatically generate stories about local traditions and culture based on the given information. An example of a prompt used here would be, "Please create a story about a traditional local summer festival, including its historical background and unique characteristics."

[0434] Ultimately, the generated stories are published as digital content on the platform via the server's content distribution system. Users can access this content and experience local culture using devices such as smartphones and tablets. In this way, the invention makes it possible to effectively preserve and widely disseminate local traditions and culture.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The user collects local cultural information. In this process, the user uses a smartphone or tablet to launch voice recording and text input applications. Specific inputs include audio interviews with festival participants and notes about the festival's origins. The output consists of audio and text files.

[0438] Step 2:

[0439] The terminal sends audio and text files acquired by the user to the server using its data transmission function. This step involves uploading the data to cloud storage using a stable internet connection. The input is the data file on the terminal, and the output is the data file stored on the server.

[0440] Step 3:

[0441] The server processes the uploaded data using data preprocessing tools. In the case of audio data, it is converted to text using the Google Cloud Speech-to-Text API as input. Furthermore, noise is removed using processing software such as Audacity, and high-quality text data is obtained as output. For text data as well, formatting and standardization are performed as input, and data ready for analysis is obtained as output.

[0442] Step 4:

[0443] The server uses a generative AI model with formatted text data to generate a story using a story generation method. The input is formatted cultural information, and the story is constructed based on this using open-source natural language processing technology. Specifically, a prompt is set such as, "Create a story about a traditional local summer festival, incorporating its historical background and unique characteristics." The output is the completed story data.

[0444] Step 5:

[0445] The server publishes the generated stories as digital content to the platform using content distribution methods. The input is pre-generated story data, which is converted into a digital format and distributed via websites and mobile applications. The output is content publicly available online.

[0446] Step 6:

[0447] Users access digital content published through the platform and experience local culture. They use their devices to launch applications and learn about ancient cultures and traditions by browsing content that interests them. The input is the URL of the content accessed through the device or links within the app, and the output is the knowledge and experience provided through the user's sight and hearing.

[0448] (Application Example 1)

[0449] 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."

[0450] In many regions, the transmission of traditions and culture is a challenge. To pass on this cultural heritage to future generations, it is necessary to communicate its appeal in a modern way and allow a wide range of people to experience it. However, technical challenges remain in the processes of collecting, processing, and disseminating cultural information. It is essential to establish efficient methods for carrying out these processes and to monetize them.

[0451] 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.

[0452] In this invention, the server includes data collection means, data preprocessing means, story generation means, user interface means, and monetization means. This makes it possible to efficiently collect information on local traditions and culture, format it, generate stories using natural language processing technology and generative AI models, and distribute them as digital content. Furthermore, users can easily access and experience it through their terminals, and revenue is managed to support further cultural activities in the region.

[0453] "Data collection means" refers to methods and devices for efficiently collecting information on local traditions and culture.

[0454] "Data preprocessing means" refers to the techniques and processes used to shape and format the collected information.

[0455] A "story generation method" is a technique for constructing a narrative using a generative model based on formatted data.

[0456] "Content distribution means" refers to methods and devices for providing a constructed story to users as digital content.

[0457] "User interface means" refers to interactive methods and platforms that allow users to access and experience digital content.

[0458] "Monetization methods" refer to methods and systems for managing revenue generated from digital content and using it to support local cultural activities.

[0459] The system of this invention has a configuration that combines multiple means. The user's terminal collects local cultural information, and the server processes that information. Here, audio and text data are managed using Python, and Pandas and NLTK are used for data cleaning. Audio data is converted to text format using the Google Cloud Speech-to-Text API, and then noise reduction is performed using LibROSA.

[0460] Based on the formatted data, the server uses a generative AI model to construct a story. This process utilizes Hugging Face's Transformers library and generates a story using keywords obtained through natural language processing. For example, it might assemble the history of a festival in a specific region into a story and present it to the user.

[0461] The generated stories are delivered through an application developed with React Native. Through the user interface, users can view and experience this content on their own devices. This enables digital experiences of local culture, increasing opportunities for more users to engage with that culture.

[0462] Through monetization methods, revenue generated from digital content is effectively managed and ultimately reinvested in local cultural activities. Through this cycle, the system plays a role in supporting the preservation and transmission of local culture.

[0463] A concrete example is the experience of a traditional cultural festival in a certain region. The user records and films the festival and interviews with participants, and the server generates a story based on this. An example of a prompt might be, "Using interviews and historical background about the cultural festival in this region, create a story about how a family can participate." Based on this prompt, the generative model constructs a story and provides the user with a new cultural experience.

[0464] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0465] Step 1:

[0466] Users collect local cultural information using their devices. Specifically, they record this information as audio or text. This collected information becomes input data and is uploaded to the server.

[0467] Step 2:

[0468] The server converts the uploaded audio data into text data using the Google Cloud Speech-to-Text API. The converted text data is then used as input to remove noise using the LibROSA library. The output is the cleaned text data.

[0469] Step 3:

[0470] The server uses Pandas and NLTK to further format the cleaned text data and extract the necessary information. This step primarily involves formatting the data and filtering out important information. The output is the formatted data.

[0471] Step 4:

[0472] The server takes formatted data as input and uses the Hugging Face Transformers library to run a generative AI model, generating a story using prompt text. Specifically, it employs natural language processing techniques to compile the story's content. The output is the generated story.

[0473] Step 5:

[0474] The generated story is delivered to the user's device by the server through an application developed with React Native. The user can view and experience the story as digital content through the interface. In this step, the generated story becomes visible to the user.

[0475] Step 6:

[0476] The server manages the revenue generated from the story's content using monetization methods. This includes data such as page views and advertising revenue. The managed revenue is reinvested to support local cultural activities. The output is a detailed revenue report.

[0477] 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.

[0478] The present invention's system aims to provide a more effective cultural experience by recognizing and utilizing user emotions during the process of collecting information on local traditions and culture and providing it as digital content. This system includes multiple processes, starting from the stage where the user collects local information using a terminal, and continuing until the server processes and distributes it.

[0479] First, the user uses a device to collect audio and text information from local residents. At this stage, the data obtained through interviews and fieldwork is stored on the device. Next, the device uploads the data to a server, and the formatted data is then formatted using data preprocessing tools.

[0480] On the server, based on the formatted data, a story generation system uses a generative model to construct local narratives. This process incorporates natural language processing and an emotion engine, enabling it to recognize and reflect user emotions in the story. For example, it can highlight cultural elements that the user is interested in or construct narratives that evoke specific emotions.

[0481] The generated story is delivered to the device as digital content through a content distribution system. The device displays this content for the user to view and analyzes the user's emotions in real time. This analysis is sent back to the server as feedback and used to further optimize the content.

[0482] As a concrete example, consider the case of digitizing a traditional dance from a certain region. By collecting anecdotes and music related to this dance, it is possible to create a presentation that changes the visual effects in response to the user's emotional reactions. This allows users to not only watch a video but also to have an emotionally engaging experience.

[0483] Finally, the system maximizes revenue from digital content using monetization methods. Revenue is generated through targeted advertising utilizing emotional data, and a portion of this can be reinvested in local cultural activities. This process ensures the cyclical preservation of local culture and economic sustainability.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] Users collect local cultural information using their devices. Specifically, they record information in audio and text format through conversations with local residents. This record is saved on the device and later sent to a server.

[0487] Step 2:

[0488] The device uploads the collected cultural information to the server. During this process, the data format is formatted and compressed, optimizing it for easier processing on the server side.

[0489] Step 3:

[0490] The server analyzes and formats the uploaded data using data preprocessing tools. In particular, audio data is converted into text data using speech recognition technology, and noise is removed.

[0491] Step 4:

[0492] The server uses a generative model to construct a story based on formatted data. This process utilizes an emotion engine to analyze user emotions and reflect them in the content. It generates a personalized narrative, such as highlighting themes that the user has shown interest in.

[0493] Step 5:

[0494] The server formats the story it has created as digital content. This content is then delivered to the device via a content distribution method, allowing the user to experience it.

[0495] Step 6:

[0496] The user views digital content through a device. During this time, the device collects emotional data in real time from the user's facial expressions and vocalizations and transmits it to a server.

[0497] Step 7:

[0498] The server collects and analyzes sentiment data, and uses the results to improve and optimize content. Revenue is managed through monetization methods, delivering targeted ads based on user sentiment data. A portion of the revenue is reinvested in local cultural activities.

[0499] (Example 2)

[0500] 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."

[0501] Traditional systems for collecting and distributing information on culture and customs often provided one-sided information without considering users' interests or feelings, making it difficult to offer users an effective cultural experience. Furthermore, the limited ways in which collected data could be utilized prevented the maximization of its value as diverse cultural content. This resulted in insufficient economic support for the protection and sustainable development of local cultures.

[0502] 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.

[0503] In this invention, the server includes information acquisition means, data processing means, narrative generation means, emotion analysis means, adaptation means, information distribution means, and profit management means. This makes it possible to generate and distribute stories about local culture and customs in a way that takes into account the user's emotions, enriching the user's experience, while simultaneously returning a portion of the profits obtained to local cultural activities.

[0504] "Information acquisition means" refers to devices and functions that collect information on local culture and customs, primarily through the collection of audio and text data.

[0505] "Data processing means" refers to a process that has the function of shaping and standardizing the format of collected information, and includes converting audio into text data.

[0506] A "narrative generation method" is a system that uses a generative model to create a story based on formatted information and utilizes natural language processing technology.

[0507] An "emotion analysis tool" is a process that has the function of recognizing and analyzing a user's emotions.

[0508] "Adaptation means" refers to a process that adjusts the content of the story based on the user's emotions obtained through emotion analysis means.

[0509] "Information distribution method" refers to the function of providing the created story to users as a digital medium.

[0510] A "profit management system" is a system that has the function of managing the profits obtained from digital media and appropriately returning them to local cultural activities.

[0511] This invention is a system for effectively processing information on local culture and customs through a series of processes from collection to distribution. This system is implemented by three main entities: users, terminals, and servers.

[0512] The user first uses a device to collect audio and text information from local residents. This device could be a smartphone or tablet, and data could be collected using voice recording and note-taking apps. For example, the user might conduct interviews about traditional local festivals, record the audio data on their smartphone, and save it as text data.

[0513] Next, the terminal uploads the collected data to the server. The server uses data processing tools to convert the audio data into text data and standardize the format. Here, speech recognition software and natural language processing tools are used to remove unnecessary information and maintain data integrity.

[0514] Subsequently, the server uses narrative generation tools to generate a story based on the formatted data. This process utilizes a generative AI model and constructs a meaningful story using prompts. For example, a prompt such as "Generate an inspiring story about the traditional dances of this region" can be input to the model.

[0515] Furthermore, the server recognizes the user's emotions in real time through emotion analysis means and adjusts the content of the generated story through adaptation means. By reflecting the user's emotions, a more personalized cultural experience can be provided. Then, using information distribution means, the adjusted story is delivered to the terminal as digital media, which the user can then view.

[0516] Finally, the server manages the profits generated from digital media using profit management mechanisms and reinvests them back into local cultural activities. This entire process creates a system that goes beyond mere digital content provision, aiming for the protection and sustainable development of local culture.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The user uses a device to collect information from local residents. During this process, the user conducts interviews using a smartphone voice recording app and saves the data as audio. The input is the audio obtained from conversations with local residents, and the output is an audio file saved on the device. This data forms the basis for subsequent processing.

[0520] Step 2:

[0521] The terminal uploads the collected audio data to the server. Here, the terminal uses an internet connection to send the audio files to the server. The input is the audio files stored on the terminal, and the output is the secure transfer of the audio data to the server. During this process, the integrity of the data is checked, and file names are changed or metadata is added as needed.

[0522] Step 3:

[0523] The server processes the received audio data. Using data processing tools, it converts the audio data into text and grammatically formats it. This step utilizes speech recognition technology to remove noise and standardize the text format. The input is audio data, and the output is formatted text data. This text data forms the basis for story generation.

[0524] Step 4:

[0525] The server generates a story from formatted text data using a generative AI model. This process utilizes pre-configured prompts to create a story aligned with the theme. For example, the model might be prompted with "Generate an inspiring story about the traditional dances of this region." The input consists of formatted text data and prompts, while the output is the generated story.

[0526] Step 5:

[0527] The server analyzes how the generated story evokes emotions in the user using emotion analysis tools. Here, emotion analysis algorithms are used to analyze the user's facial expressions and reaction data in real time. The input is the user's emotional data, and the output is a report based on their emotional response. This information is used for final adjustments to the story.

[0528] Step 6:

[0529] The server uses adaptive mechanisms to adjust the story content to reflect the results of sentiment analysis. Specifically, it emphasizes or modifies certain parts of the story to match the user's emotions. The input is the sentiment analysis report, and the output is the adjusted story. This adjustment provides a personalized user experience.

[0530] Step 7:

[0531] The server delivers the edited narrative as digital media to the terminal through an information distribution method. The terminal provides the user with the opportunity to receive and appreciate the content visually and aurally. The input is edited narrative data, and the output is digital content accessible to the user.

[0532] Step 8:

[0533] The server uses profit management mechanisms to manage the profits generated from distributed digital media and reinvest them back into local culture. Here, it monitors advertising revenue and user subscription models and distributes the results. The input is profit data obtained from digital content, and the output is that profits are appropriately managed and contributed to local culture.

[0534] (Application Example 2)

[0535] 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."

[0536] In recent years, there have been efforts to digitize and widely distribute local culture and traditions, but there is a challenge in providing effective cultural experiences that reflect the emotions of users. As a result, personalization of digital content and improvement of viewers' emotional satisfaction have not been fully achieved.

[0537] 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.

[0538] In this invention, the server includes information gathering means, data formatting means, and story generation means. This enables the generation and distribution of cultural content that takes into account the user's emotions.

[0539] "Information gathering means" refers to means that have the function of acquiring information about local traditions and culture and accumulating it as data.

[0540] A "data formatting tool" is a tool that has the function of arranging acquired information into a certain format and converting it into a form that is easy to process.

[0541] A "narrative generation method" is a means that has the function of constructing a narrative using a generative model based on formatted data.

[0542] An "information distribution method" is a means that has the function of delivering a constructed story to a user as digital information.

[0543] A "sentiment analysis tool" is a tool that has the function of analyzing the user's emotions when displaying information and collecting that data.

[0544] A "content adjustment method" is a means that dynamically changes the narrative based on collected emotional data, thereby personalizing the user's experience.

[0545] A "monetization tool" is a means that has the function of managing the income generated from digital information and returning it in an appropriate manner.

[0546] The system based on this invention collects information on local traditions and culture through various means and delivers stories based on that information to users, thereby providing a cultural experience that enriches emotions. Specific embodiments are described below.

[0547] First, the device is equipped with information gathering tools to collect audio and text information about local traditions and culture acquired by the user. This allows for the efficient collection of local information. This information is then formatted using data formatting tools and uploaded to the server.

[0548] The server uses a generative model based on formatted data and constructs a story using a narrative generation method. In this process, natural language processing techniques are used to interpret the text data and generate content that aligns with the story's theme. This results in the generation of relatable stories that can appeal to the user's emotions.

[0549] The generated stories are delivered to the device as digital information through an information distribution system. The device is equipped with sentiment analysis capabilities that analyze the user's emotions in real time as they view the information. The analysis results are sent to a server and used to adjust and personalize the next content.

[0550] For example, in a story that explains the history of music in a particular region in chapters, the visual effects are emphasized for chapters that particularly resonated with the user. Through this kind of emotional analysis, parts of the story are dynamically adjusted, providing a different experience for each user.

[0551] An example of a prompt message sent to the generation AI model might be: "Create a story based on the history of traditional local music. What additional effects should be added when the user's emotion is 'emotional'?" This results in a story structure that is tailored to the user experience.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] Users use their devices to collect audio and text information about local traditions and culture. The input is audio or text data, which is stored digitally on the device. The collected data serves as foundational data for subsequent processing.

[0555] Step 2:

[0556] The terminal formats the collected data through a data formatting mechanism. It receives audio and text information as input; audio data is converted to text data. The text data is then formatted and converted to a specified format. This output data is uploaded to the server.

[0557] Step 3:

[0558] The server receives formatted data and utilizes a generative AI model with a story generation mechanism. It receives formatted data as input and performs data calculations to construct the story using natural language processing techniques. After keyword extraction, template application, and theme setting, the story is generated.

[0559] Step 4:

[0560] The server transmits the generated story as digital information to the terminal via an information distribution system. The input is the generated story data, which is output in a format optimized for the terminal. This converts it into a format that the user can view on the terminal.

[0561] Step 5:

[0562] The device displays information while analyzing the user's emotions using emotion analysis tools. It receives user reactions and viewing data as input and generates output data that measures the emotional state in real time. This data is sent to the server as feedback.

[0563] Step 6:

[0564] The server receives sentiment analysis data and uses it via a content adjustment mechanism during the next story generation. It uses sentiment data as input to perform data calculations that modify the story's themes and expressive techniques. This enables the generation of more personalized stories.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] The system of the present invention has a configuration for effectively preserving local traditions and culture and distributing them as digital content. This system begins with users (local residents or fieldworkers) collecting local cultural information. Users record local information as audio or text on their devices and upload it to a server.

[0583] Next, the server formats the uploaded information using data preprocessing tools and converts it into a usable format. This process includes transcribing audio data into text and removing noise. Then, based on the formatted data, the server uses a generative model with story generation tools to construct local stories and narratives. For example, it might incorporate the background and details of traditional festivals held in the region and shape them into a story that is accessible to tourists.

[0584] The generated stories will be published as digital content on the platform using content distribution methods. Users can access this content through their devices and become interested in the local culture by experiencing it. This is expected to increase tourism and revitalize the local economy.

[0585] Furthermore, this system has monetization mechanisms and manages revenue from digital content. Revenue is reinvested in local cultural activities and future content creation. This cycle ensures the continuous preservation and passing on of local culture to future generations.

[0586] As a concrete example, consider the case where this system is used to digitize a traditional festival in a certain region. After collecting anecdotes related to the festival and interviews with participants, the data is processed on a server to generate a story. This story is then published on an application, and the content is structured to encourage viewers to participate in the festival. As a result, the number of festival participants increases, which can lead to economic support for the region.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] Users collect local cultural information on their devices. Specifically, they conduct interviews with local residents and record audio data and notes.

[0590] Step 2:

[0591] The device formats the cultural information it collects and uploads it to the server. Formatting includes compressing audio files and organizing text data.

[0592] Step 3:

[0593] The server processes the received data using data preprocessing tools. Audio data is converted to text using speech recognition, and noise is removed.

[0594] Step 4:

[0595] The server uses a generative model to construct a story based on the formatted data. Natural language processing techniques are used to extract keywords and compile the narrative.

[0596] Step 5:

[0597] The server formats the generated story as digital content and delivers it to the device using a content distribution method. This allows users to view the content on a digital platform.

[0598] Step 6:

[0599] Users access digital content and experience local culture through their devices. Feedback from users is collected.

[0600] Step 7:

[0601] The server analyzes feedback and manages the revenue generated through monetization methods. This revenue is then used to reinvest in local cultural activities and improve content.

[0602] (Example 1)

[0603] 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".

[0604] Local traditions and cultures are important assets that should be passed down through generations, but these valuable pieces of information are being lost due to the rapid urbanization and globalization of modern society. Furthermore, the digitalization of local cultures has not progressed sufficiently, and the information is preserved in a fragmented and incomplete form, making it difficult to understand local cultures and traditions and to attract new participants.

[0605] 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.

[0606] In this invention, the server includes information acquisition means for obtaining information on local traditions and culture, information preprocessing means for formatting and formalizing the acquired information, and story generation means for constructing a story using a generative model based on the formatted information. This makes it possible to effectively preserve and disseminate local traditions and culture.

[0607] "Means of information acquisition" refers to means of collecting or acquiring information about local traditions and culture.

[0608] "Information preprocessing means" are means for shaping acquired information and converting it into a format suitable for analysis and use.

[0609] A "generative model" is a set of algorithms and technologies used to generate new stories or content from input information.

[0610] A "narrative generation method" is a means of constructing a narrative using a generative model based on formatted information.

[0611] A "content distribution method" is a means of widely distributing a constructed digital narrative and making it accessible to users.

[0612] "Profit management tools" are means for managing and efficiently processing revenue generated from digital formats.

[0613] This invention is a system for effectively preserving local traditions and culture and distributing them as digital content. The embodiments for carrying out the invention are described below.

[0614] First, users take on the role of collecting local cultural information. They use devices such as smartphones and tablets to record information about festivals and traditional events through voice recording and text input apps. For example, they might participate in a local festival, record interviews with local residents as audio, and take text notes about the origins and details of the festival.

[0615] When users upload collected data to the server via their devices, the server uses data preprocessing tools to format the data. Audio data is converted to text using the Google Cloud Speech-to-Text API, and data quality is improved by removing noise using speech processing software such as Audacity. The text data is also formatted to a standardized and parseable form.

[0616] The server uses a story generation AI model to construct a narrative from the formatted data. Specifically, it uses open-source natural language processing technology to automatically generate stories about local traditions and culture based on the given information. An example of a prompt used here would be, "Please create a story about a traditional local summer festival, including its historical background and unique characteristics."

[0617] Ultimately, the generated stories are published as digital content on the platform via the server's content distribution system. Users can access this content and experience local culture using devices such as smartphones and tablets. In this way, the invention makes it possible to effectively preserve and widely disseminate local traditions and culture.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The user collects local cultural information. In this process, the user uses a smartphone or tablet to launch voice recording and text input applications. Specific inputs include audio interviews with festival participants and notes about the festival's origins. The output consists of audio and text files.

[0621] Step 2:

[0622] The terminal sends audio and text files acquired by the user to the server using its data transmission function. This step involves uploading the data to cloud storage using a stable internet connection. The input is the data file on the terminal, and the output is the data file stored on the server.

[0623] Step 3:

[0624] The server processes the uploaded data using data preprocessing tools. In the case of audio data, it is converted to text using the Google Cloud Speech-to-Text API as input. Furthermore, noise is removed using processing software such as Audacity, and high-quality text data is obtained as output. For text data as well, formatting and standardization are performed as input, and data ready for analysis is obtained as output.

[0625] Step 4:

[0626] The server uses a generative AI model with formatted text data to generate a story using a story generation method. The input is formatted cultural information, and the story is constructed based on this using open-source natural language processing technology. Specifically, a prompt is set such as, "Create a story about a traditional local summer festival, incorporating its historical background and unique characteristics." The output is the completed story data.

[0627] Step 5:

[0628] The server publishes the generated stories as digital content to the platform using content distribution methods. The input is pre-generated story data, which is converted into a digital format and distributed via websites and mobile applications. The output is content publicly available online.

[0629] Step 6:

[0630] Users access digital content published through the platform and experience local culture. They use their devices to launch applications and learn about ancient cultures and traditions by browsing content that interests them. The input is the URL of the content accessed through the device or links within the app, and the output is the knowledge and experience provided through the user's sight and hearing.

[0631] (Application Example 1)

[0632] 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".

[0633] In many regions, the transmission of traditions and culture is a challenge. To pass on this cultural heritage to future generations, it is necessary to communicate its appeal in a modern way and allow a wide range of people to experience it. However, technical challenges remain in the processes of collecting, processing, and disseminating cultural information. It is essential to establish efficient methods for carrying out these processes and to monetize them.

[0634] 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.

[0635] In this invention, the server includes data collection means, data preprocessing means, story generation means, user interface means, and monetization means. This makes it possible to efficiently collect information on local traditions and culture, format it, generate stories using natural language processing technology and generative AI models, and distribute them as digital content. Furthermore, users can easily access and experience it through their terminals, and revenue is managed to support further cultural activities in the region.

[0636] "Data collection means" refers to methods and devices for efficiently collecting information on local traditions and culture.

[0637] "Data preprocessing means" refers to the techniques and processes used to shape and format the collected information.

[0638] A "story generation method" is a technique for constructing a narrative using a generative model based on formatted data.

[0639] "Content distribution means" refers to methods and devices for providing a constructed story to users as digital content.

[0640] "User interface means" refers to interactive methods and platforms that allow users to access and experience digital content.

[0641] "Monetization methods" refer to methods and systems for managing revenue generated from digital content and using it to support local cultural activities.

[0642] The system of this invention has a configuration that combines multiple means. The user's terminal collects local cultural information, and the server processes that information. Here, audio and text data are managed using Python, and Pandas and NLTK are used for data cleaning. Audio data is converted to text format using the Google Cloud Speech-to-Text API, and then noise reduction is performed using LibROSA.

[0643] Based on the formatted data, the server uses a generative AI model to construct a story. This process utilizes Hugging Face's Transformers library and generates a story using keywords obtained through natural language processing. For example, it might assemble the history of a festival in a specific region into a story and present it to the user.

[0644] The generated stories are delivered through an application developed with React Native. Through the user interface, users can view and experience this content on their own devices. This enables digital experiences of local culture, increasing opportunities for more users to engage with that culture.

[0645] Through monetization methods, revenue generated from digital content is effectively managed and ultimately reinvested in local cultural activities. Through this cycle, the system plays a role in supporting the preservation and transmission of local culture.

[0646] A concrete example is the experience of a traditional cultural festival in a certain region. The user records and films the festival and interviews with participants, and the server generates a story based on this. An example of a prompt might be, "Using interviews and historical background about the cultural festival in this region, create a story about how a family can participate." Based on this prompt, the generative model constructs a story and provides the user with a new cultural experience.

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] Users collect local cultural information using their devices. Specifically, they record this information as audio or text. This collected information becomes input data and is uploaded to the server.

[0650] Step 2:

[0651] The server converts the uploaded audio data into text data using the Google Cloud Speech-to-Text API. The converted text data is then used as input to remove noise using the LibROSA library. The output is the cleaned text data.

[0652] Step 3:

[0653] The server uses Pandas and NLTK to further format the cleaned text data and extract the necessary information. This step primarily involves formatting the data and filtering out important information. The output is the formatted data.

[0654] Step 4:

[0655] The server takes formatted data as input and uses the Hugging Face Transformers library to run a generative AI model, generating a story using prompt text. Specifically, it employs natural language processing techniques to compile the story's content. The output is the generated story.

[0656] Step 5:

[0657] The generated story is delivered to the user's device by the server through an application developed with React Native. The user can view and experience the story as digital content through the interface. In this step, the generated story becomes visible to the user.

[0658] Step 6:

[0659] The server manages the revenue generated from the story's content using monetization methods. This includes data such as page views and advertising revenue. The managed revenue is reinvested to support local cultural activities. The output is a detailed revenue report.

[0660] 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.

[0661] The present invention's system aims to provide a more effective cultural experience by recognizing and utilizing user emotions during the process of collecting information on local traditions and culture and providing it as digital content. This system includes multiple processes, starting from the stage where the user collects local information using a terminal, and continuing until the server processes and distributes it.

[0662] First, the user uses a device to collect audio and text information from local residents. At this stage, the data obtained through interviews and fieldwork is stored on the device. Next, the device uploads the data to a server, and the formatted data is then formatted using data preprocessing tools.

[0663] On the server, based on the formatted data, a story generation system uses a generative model to construct local narratives. This process incorporates natural language processing and an emotion engine, enabling it to recognize and reflect user emotions in the story. For example, it can highlight cultural elements that the user is interested in or construct narratives that evoke specific emotions.

[0664] The generated story is delivered to the device as digital content through a content distribution system. The device displays this content for the user to view and analyzes the user's emotions in real time. This analysis is sent back to the server as feedback and used to further optimize the content.

[0665] As a concrete example, consider the case of digitizing a traditional dance from a certain region. By collecting anecdotes and music related to this dance, it is possible to create a presentation that changes the visual effects in response to the user's emotional reactions. This allows users to not only watch a video but also to have an emotionally engaging experience.

[0666] Finally, the system maximizes revenue from digital content using monetization methods. Revenue is generated through targeted advertising utilizing emotional data, and a portion of this can be reinvested in local cultural activities. This process ensures the cyclical preservation of local culture and economic sustainability.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users collect local cultural information using their devices. Specifically, they record information in audio and text format through conversations with local residents. This record is saved on the device and later sent to a server.

[0670] Step 2:

[0671] The device uploads the collected cultural information to the server. During this process, the data format is formatted and compressed, optimizing it for easier processing on the server side.

[0672] Step 3:

[0673] The server analyzes and formats the uploaded data using data preprocessing tools. In particular, audio data is converted into text data using speech recognition technology, and noise is removed.

[0674] Step 4:

[0675] The server uses a generative model to construct a story based on formatted data. This process utilizes an emotion engine to analyze user emotions and reflect them in the content. It generates a personalized narrative, such as highlighting themes that the user has shown interest in.

[0676] Step 5:

[0677] The server formats the story it has created as digital content. This content is then delivered to the device via a content distribution method, allowing the user to experience it.

[0678] Step 6:

[0679] The user views digital content through a device. During this time, the device collects emotional data in real time from the user's facial expressions and vocalizations and transmits it to a server.

[0680] Step 7:

[0681] The server collects and analyzes sentiment data, and uses the results to improve and optimize content. Revenue is managed through monetization methods, delivering targeted ads based on user sentiment data. A portion of the revenue is reinvested in local cultural activities.

[0682] (Example 2)

[0683] 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".

[0684] Traditional systems for collecting and distributing information on culture and customs often provided one-sided information without considering users' interests or feelings, making it difficult to offer users an effective cultural experience. Furthermore, the limited ways in which collected data could be utilized prevented the maximization of its value as diverse cultural content. This resulted in insufficient economic support for the protection and sustainable development of local cultures.

[0685] 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.

[0686] In this invention, the server includes information acquisition means, data processing means, narrative generation means, emotion analysis means, adaptation means, information distribution means, and profit management means. This makes it possible to generate and distribute stories about local culture and customs in a way that takes into account the user's emotions, enriching the user's experience, while simultaneously returning a portion of the profits obtained to local cultural activities.

[0687] "Information acquisition means" refers to devices and functions that collect information on local culture and customs, primarily through the collection of audio and text data.

[0688] "Data processing means" refers to a process that has the function of shaping and standardizing the format of collected information, and includes converting audio into text data.

[0689] A "narrative generation method" is a system that uses a generative model to create a story based on formatted information and utilizes natural language processing technology.

[0690] An "emotion analysis tool" is a process that has the function of recognizing and analyzing a user's emotions.

[0691] "Adaptation means" refers to a process that adjusts the content of the story based on the user's emotions obtained through emotion analysis means.

[0692] "Information distribution method" refers to the function of providing the created story to users as a digital medium.

[0693] A "profit management system" is a system that has the function of managing the profits obtained from digital media and appropriately returning them to local cultural activities.

[0694] This invention is a system for effectively processing information on local culture and customs through a series of processes from collection to distribution. This system is implemented by three main entities: users, terminals, and servers.

[0695] The user first uses a device to collect audio and text information from local residents. This device could be a smartphone or tablet, and data could be collected using voice recording and note-taking apps. For example, the user might conduct interviews about traditional local festivals, record the audio data on their smartphone, and save it as text data.

[0696] Next, the terminal uploads the collected data to the server. The server uses data processing tools to convert the audio data into text data and standardize the format. Here, speech recognition software and natural language processing tools are used to remove unnecessary information and maintain data integrity.

[0697] Subsequently, the server uses narrative generation tools to generate a story based on the formatted data. This process utilizes a generative AI model and constructs a meaningful story using prompts. For example, a prompt such as "Generate an inspiring story about the traditional dances of this region" can be input to the model.

[0698] Furthermore, the server recognizes the user's emotions in real time through emotion analysis means and adjusts the content of the generated story through adaptation means. By reflecting the user's emotions, a more personalized cultural experience can be provided. Then, using information distribution means, the adjusted story is delivered to the terminal as digital media, which the user can then view.

[0699] Finally, the server manages the profits generated from digital media using profit management mechanisms and reinvests them back into local cultural activities. This entire process creates a system that goes beyond mere digital content provision, aiming for the protection and sustainable development of local culture.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The user uses a device to collect information from local residents. During this process, the user conducts interviews using a smartphone voice recording app and saves the data as audio. The input is the audio obtained from conversations with local residents, and the output is an audio file saved on the device. This data forms the basis for subsequent processing.

[0703] Step 2:

[0704] The terminal uploads the collected audio data to the server. Here, the terminal uses an internet connection to send the audio files to the server. The input is the audio files stored on the terminal, and the output is the secure transfer of the audio data to the server. During this process, the integrity of the data is checked, and file names are changed or metadata is added as needed.

[0705] Step 3:

[0706] The server processes the received audio data. Using data processing tools, it converts the audio data into text and grammatically formats it. This step utilizes speech recognition technology to remove noise and standardize the text format. The input is audio data, and the output is formatted text data. This text data forms the basis for story generation.

[0707] Step 4:

[0708] The server generates a story from formatted text data using a generative AI model. This process utilizes pre-configured prompts to create a story aligned with the theme. For example, the model might be prompted with "Generate an inspiring story about the traditional dances of this region." The input consists of formatted text data and prompts, while the output is the generated story.

[0709] Step 5:

[0710] The server analyzes how the generated story evokes emotions in the user using emotion analysis tools. Here, emotion analysis algorithms are used to analyze the user's facial expressions and reaction data in real time. The input is the user's emotional data, and the output is a report based on their emotional response. This information is used for final adjustments to the story.

[0711] Step 6:

[0712] The server uses adaptive mechanisms to adjust the story content to reflect the results of sentiment analysis. Specifically, it emphasizes or modifies certain parts of the story to match the user's emotions. The input is the sentiment analysis report, and the output is the adjusted story. This adjustment provides a personalized user experience.

[0713] Step 7:

[0714] The server delivers the edited narrative as digital media to the terminal through an information distribution method. The terminal provides the user with the opportunity to receive and appreciate the content visually and aurally. The input is edited narrative data, and the output is digital content accessible to the user.

[0715] Step 8:

[0716] The server uses profit management mechanisms to manage the profits generated from distributed digital media and reinvest them back into local culture. Here, it monitors advertising revenue and user subscription models and distributes the results. The input is profit data obtained from digital content, and the output is that profits are appropriately managed and contributed to local culture.

[0717] (Application Example 2)

[0718] 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".

[0719] In recent years, there have been efforts to digitize and widely distribute local culture and traditions, but there is a challenge in providing effective cultural experiences that reflect the emotions of users. As a result, personalization of digital content and improvement of viewers' emotional satisfaction have not been fully achieved.

[0720] 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.

[0721] In this invention, the server includes information gathering means, data formatting means, and story generation means. This enables the generation and distribution of cultural content that takes into account the user's emotions.

[0722] "Information gathering means" refers to means that have the function of acquiring information about local traditions and culture and accumulating it as data.

[0723] A "data formatting tool" is a tool that has the function of arranging acquired information into a certain format and converting it into a form that is easy to process.

[0724] A "narrative generation method" is a means that has the function of constructing a narrative using a generative model based on formatted data.

[0725] An "information distribution method" is a means that has the function of delivering a constructed story to a user as digital information.

[0726] A "sentiment analysis tool" is a tool that has the function of analyzing the user's emotions when displaying information and collecting that data.

[0727] A "content adjustment method" is a means that dynamically changes the narrative based on collected emotional data, thereby personalizing the user's experience.

[0728] A "monetization tool" is a means that has the function of managing the income generated from digital information and returning it in an appropriate manner.

[0729] The system based on this invention collects information on local traditions and culture through various means and delivers stories based on that information to users, thereby providing a cultural experience that enriches emotions. Specific embodiments are described below.

[0730] First, the device is equipped with information gathering tools to collect audio and text information about local traditions and culture acquired by the user. This allows for the efficient collection of local information. This information is then formatted using data formatting tools and uploaded to the server.

[0731] The server uses a generative model based on formatted data and constructs a story using a narrative generation method. In this process, natural language processing techniques are used to interpret the text data and generate content that aligns with the story's theme. This results in the generation of relatable stories that can appeal to the user's emotions.

[0732] The generated stories are delivered to the device as digital information through an information distribution system. The device is equipped with sentiment analysis capabilities that analyze the user's emotions in real time as they view the information. The analysis results are sent to a server and used to adjust and personalize the next content.

[0733] For example, in a story that explains the history of music in a particular region in chapters, the visual effects are emphasized for chapters that particularly resonated with the user. Through this kind of emotional analysis, parts of the story are dynamically adjusted, providing a different experience for each user.

[0734] An example of a prompt message sent to the generation AI model might be: "Create a story based on the history of traditional local music. What additional effects should be added when the user's emotion is 'emotional'?" This results in a story structure that is tailored to the user experience.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] Users use their devices to collect audio and text information about local traditions and culture. The input is audio or text data, which is stored digitally on the device. The collected data serves as foundational data for subsequent processing.

[0738] Step 2:

[0739] The terminal formats the collected data through a data formatting mechanism. It receives audio and text information as input; audio data is converted to text data. The text data is then formatted and converted to a specified format. This output data is uploaded to the server.

[0740] Step 3:

[0741] The server receives formatted data and utilizes a generative AI model with a story generation mechanism. It receives formatted data as input and performs data calculations to construct the story using natural language processing techniques. After keyword extraction, template application, and theme setting, the story is generated.

[0742] Step 4:

[0743] The server transmits the generated story as digital information to the terminal via an information distribution system. The input is the generated story data, which is output in a format optimized for the terminal. This converts it into a format that the user can view on the terminal.

[0744] Step 5:

[0745] The device displays information while analyzing the user's emotions using emotion analysis tools. It receives user reactions and viewing data as input and generates output data that measures the emotional state in real time. This data is sent to the server as feedback.

[0746] Step 6:

[0747] The server receives sentiment analysis data and uses it via a content adjustment mechanism during the next story generation. It uses sentiment data as input to perform data calculations that modify the story's themes and expressive techniques. This enables the generation of more personalized stories.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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."

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] A data collection method for collecting information on local traditions and culture,

[0772] Data preprocessing means for shaping and formatting the collected data,

[0773] A story generation means that constructs a story using a generative model based on the aforementioned formatted data,

[0774] A content distribution method for distributing the aforementioned constructed story as digital content,

[0775] A monetization means for managing revenue obtained from the aforementioned digital content,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, characterized in that the data preprocessing means has a function of converting audio data into text data.

[0779] (Claim 3)

[0780] The system according to claim 1, characterized in that the story generation means extracts keywords using natural language processing technology and constructs a story.

[0781] "Example 1"

[0782] (Claim 1)

[0783] Information acquisition methods for obtaining information on local traditions and culture,

[0784] Information preprocessing means for formatting and shaping the acquired information,

[0785] A story generation means that constructs a story using a generative model based on the aforementioned formatted information,

[0786] A content distribution method for distributing the aforementioned constructed story in digital format,

[0787] A profit management means for processing profits obtained from the aforementioned digital format,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, characterized in that the information preprocessing means has a function of converting audio information into text information.

[0791] (Claim 3)

[0792] The system according to claim 1, characterized in that the story generation means extracts important words using natural language processing technology and constructs a story.

[0793] "Application Example 1"

[0794] (Claim 1)

[0795] A data collection method for collecting information on local traditions and culture,

[0796] Data preprocessing means for shaping and formatting the collected data,

[0797] A story generation means that constructs a story using a generative model based on the aforementioned formatted data,

[0798] A content distribution method for distributing the aforementioned constructed story as digital content,

[0799] A user interface means that enables the user to access and experience the aforementioned digital content through a terminal,

[0800] A monetization means for managing revenue obtained from the aforementioned digital content,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized in that the data preprocessing means has a function to convert audio data into text data and a function to remove noise using audio analysis technology.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized in that the story generation means extracts keywords using natural language processing technology, constructs a story, and further enriches the story by generating additional information based on prompt sentences using a generative AI model.

[0806] "Example 2 of combining an emotion engine"

[0807] (Claim 1)

[0808] Information acquisition means for collecting information on local culture and customs,

[0809] A data processing means for formatting and standardizing the format of the collected information,

[0810] A narrative generation means that creates a story using a generative model based on the formatted information,

[0811] A means of sentiment analysis that recognizes and analyzes the user's emotions,

[0812] An adaptive means that adjusts the content of the story based on the user's emotions obtained by the emotion analysis means,

[0813] An information distribution means for distributing the aforementioned created story as digital media,

[0814] A profit management means for managing the profits obtained from the aforementioned digital media,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, characterized in that the data processing means has a function to convert audio data into text data.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the narrative generation means extracts a theme and creates a story using natural language processing technology.

[0820] "Application example 2 of combining emotional engines"

[0821] (Claim 1)

[0822] Information gathering means for collecting information on local traditions and culture,

[0823] A data formatting means for formatting and shaping the collected data,

[0824] A story generation means that constructs a story using a generative model based on the aforementioned formatted data,

[0825] An information distribution means for distributing the aforementioned constructed story as digital information,

[0826] An emotion analysis means for analyzing the user's emotions when displaying the aforementioned digital information,

[0827] Content adjustment means that adjusts the story based on the emotions of the user,

[0828] A means of monetizing the revenue obtained from the aforementioned digital information,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, characterized in that the data formatting means has a function of converting audio information into text information.

[0832] (Claim 3)

[0833] The system according to claim 1, characterized in that the story generation means extracts specific terms using natural language processing technology, constructs a story, and adds an effect that evokes emotions based on the terms. [Explanation of symbols]

[0834] 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 data collection method for collecting information on local traditions and culture, Data preprocessing means for shaping and formatting the collected data, A story generation means that constructs a story using a generative model based on the aforementioned formatted data, A content distribution method for distributing the aforementioned constructed story as digital content, A monetization means for managing revenue obtained from the aforementioned digital content, A system that includes this.

2. The system according to claim 1, characterized in that the data preprocessing means has a function of converting audio data into text data.

3. The system according to claim 1, characterized in that the story generation means extracts keywords using natural language processing technology and constructs a story.

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