system

Abstract

We provide the system. [Solution] Information gathering means for collecting tourist information, A means of generating a story based on collected tourist information, A user interface providing means for providing the generated story to the user terminal, A means of regional collaboration that plans tourist routes and events based on the provided stories, A feedback collection method for gathering feedback from users after their sightseeing experience, The collected feedback is analyzed to improve the quality of story generation, and A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern tourism experiences, there is a problem that visitors tend to only stay at viewing tourist spots and lack opportunities to deeply understand the history and cultural background of the place. Furthermore, local governments and businesses lack effective means to attract customers and promote the characteristics of the region, and there is also a problem that it is difficult to improve tourism revenue. Therefore, it is required to provide a deeper experience for tourists and promote the attraction to the region.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system for collecting tourism information and generating stories based on it. The tourism information collection means gathers data on the history and culture of the target area, and the generation means generates a story from that data. Furthermore, the user interface provision means provides the story to tourists, and the regional collaboration means plans tourism routes and events based on the story in cooperation with local businesses, thereby guiding tourists to the local area. The feedback collection means collects opinions from tourists, and the improvement means improves the quality of story generation, thereby aiming to improve the tourism value of the region and visitor satisfaction.

[0006] "Tourism information" refers to data related to historical documents, anecdotes, and cultural background associated with tourist destinations.

[0007] "Information gathering means" refers to means that have the function of collecting information related to tourist destinations.

[0008] A "generative means" is a means that has the function of creating a story that follows a theme based on collected tourist information.

[0009] A "user interface provisioning method" is a means that has the function of providing the generated story through the user's terminal.

[0010] "Regional collaboration means" refers to methods that have the function of planning and implementing tourist routes and events in cooperation with local commercial and tourist facilities based on generated narratives.

[0011] A "feedback collection method" is a means of collecting opinions and evaluations from users after their tourist experience.

[0012] "Improvement methods" are means that have the function of improving the quality of the story based on collected feedback. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The present invention aims to enhance the tourist experience by automatically generating narratives based on the history and culture of tourist destinations and providing them to tourists. This system mainly consists of three components: a server, terminals, and users.

[0035] First, the server collects information related to the tourist destination. This information is obtained from online databases and local public records, and specifically includes data on historical documents and cultural heritage. Based on this collected data, the server uses natural language processing to extract key points and identify the themes and keywords that will form the basis of the story.

[0036] Next, the server generates a story using a generative AI model based on the collected information. This model has been pre-trained on a vast amount of historical literature and materials, and has the ability to create new stories from the input data. The generated story is checked for grammatical and stylistic consistency, and automatically corrected as needed. Once the corrections are complete, the story is formatted so that it can be displayed on the devices used by tourists.

[0037] Next, the generated story is delivered to the user through a device. This device can be a smartphone or tablet, and users can view the story through a dedicated application or webpage. The story is presented interactively, using not only text but also audio guides and visuals. This allows users to not only visit tourist destinations but also experience the stories behind them simultaneously.

[0038] Furthermore, this narrative will strengthen ties with the local community. The server will plan tourist routes and events based on the generated story, and work with local governments and businesses to attract visitors. Specifically, guided tours based on the story and events featuring local specialty products will be held.

[0039] Finally, users who have completed their sightseeing experience can submit their opinions and impressions as feedback through the application. The device sends this feedback to a server, where the collected feedback is analyzed and used to improve future story generation and plan new routes. In this way, the system is constantly evolving, aiming to provide a better sightseeing experience.

[0040] Thus, the system of the present invention comprehensively supports the entire tourism experience, from collecting tourism information and generating narratives to providing them to users, collaborating with local communities, and improving them through feedback. Specifically, historical narratives related to a particular tourist destination are generated, and events such as guided tours and sales of related products are planned, allowing tourists to enjoy an experience beyond mere sightseeing. This process also revitalizes the local economy and creates new value for tourism.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects information related to tourist destinations. This primarily involves retrieving data from online databases and local public records, gathering data on the historical sites and cultural heritage of tourist destinations.

[0044] Step 2:

[0045] The server performs natural language processing on the collected information, extracting key points and highly relevant keywords. This forms the basis for the themes and plot necessary for story generation.

[0046] Step 3:

[0047] The server uses a generative AI model to generate stories based on extracted keywords and themes. The stories are generated as text, and then language and grammar checks are performed. Automatic corrections are applied as needed.

[0048] Step 4:

[0049] The server sends the generated story to the terminal and formats it so that it can be accessed by the user. Specifically, it is converted into a text format that can be read, with added audio guides and visual displays.

[0050] Step 5:

[0051] The device delivers stories to users through dedicated applications and web pages. Users can access the stories using smartphones and tablets and enjoy an interactive experience that combines audio and visuals.

[0052] Step 6:

[0053] Server collaborates with local governments and businesses to plan narrative-based tourist routes and events. This includes planning guided tours and sales events for local products.

[0054] Step 7:

[0055] After their sightseeing experience, users provide feedback through the application. This feedback includes opinions on the story content and the overall experience.

[0056] Step 8:

[0057] The device sends the collected feedback to the server. The server analyzes the feedback and uses it to develop improvement plans to enhance the quality of the story and the content of the tourist experience.

[0058] (Example 1)

[0059] 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."

[0060] Experiences at tourist destinations tend to be fleeting if visitors don't fully understand the history and cultural background hidden within the place. To deeply experience the charm of a place, it's necessary to effectively convey the underlying stories and history, but this is difficult to do automatically. Furthermore, there's a need to improve the quality of tourist resources by leveraging post-visit experiences.

[0061] 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.

[0062] In this invention, the server includes server means for collecting information, natural language processing means for analyzing the collected information and extracting keywords, and generative AI model means for generating stories based on the keywords. This makes it possible to automatically generate stories based on the history and culture of tourist destinations and provide high-quality content to deepen the tourist experience. Furthermore, it is possible to improve the quality of tourist resources through a continuous improvement cycle by utilizing user feedback.

[0063] "Server means" refers to a computer system that has the equipment or role of collecting information and performing data analysis and model execution.

[0064] "Natural language processing methods" refer to processes and technologies that analyze text data and extract important information and keywords.

[0065] "Generative AI model means" refers to a machine learning model or its function that automatically generates new stories or content using collected data and prompts.

[0066] "Correction and formatting means" refers to the process of automatically correcting grammatical and stylistic errors in the generated story and preparing it for display on a device.

[0067] "Interface provision means" refers to technologies that function as applications or web pages for displaying or playing a generated story on a user's terminal.

[0068] "Regional collaboration means" refers to the processes and methods for cooperating with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0069] "Feedback collection methods" refer to the processes and systems for collecting opinions and feedback from users and accumulating them as data.

[0070] "Improvement methods" refer to processes and technologies used to analyze collected feedback and improve the quality of systems and content based on the analysis results.

[0071] This invention is a system that generates stories based on the history and culture of tourist destinations and provides them to users. This system consists of three elements: a server, a terminal, and a user.

[0072] First, the server collects information about tourist destinations. The server accesses public databases and local records via the internet, retrieving information about the history, culture, and other relevant details of the destinations. Specific examples include digital archives provided by local tourism associations and information on historical heritage sites. The server uses natural language processing techniques to process this information, extracting relevant keywords and themes.

[0073] Next, the server generates a story using a generative AI model based on the extracted keywords. This system's generative AI model has learned from a wide range of historical documents and past data, and has the ability to automatically create new stories that are in line with the background of tourist destinations. The generated stories are automatically corrected as needed to ensure consistency in grammar and expression. An example of a prompt sentence is the instruction, "Generate a story based on Arashiyama in the medieval period."

[0074] The generated story is then delivered to the user via a device. This device may be a smartphone or tablet, and the story is displayed to the user through a dedicated application or webpage. The story is provided not only in text format but can also be played as an audio guide, allowing for an interactive experience when combined with visuals.

[0075] Finally, users who have completed their sightseeing experience can provide feedback on their thoughts and impressions through their device. The feedback provided by users is sent to a server via the device, which analyzes it and uses it to improve the quality of the sightseeing experience and the stories generated. In this way, the system can continuously improve and continue to provide users with more valuable sightseeing experiences.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects information related to tourist destinations.

[0079] The input consists of data sources such as online databases and local public records. Specifically, the server connects to these information sources via the internet and retrieves data on historical documents and cultural heritage related to tourist destinations. The obtained information is then stored as output.

[0080] Step 2:

[0081] The server performs natural language processing on the collected information to extract keywords.

[0082] The input is the raw data collected in Step 1. The server uses a natural language processing engine to analyze the data and extract highly relevant keywords and themes. This process outputs the elements that form the basis for story generation.

[0083] Step 3:

[0084] The server generates stories using a generative AI model.

[0085] The input consists of the keywords and prompt sentence obtained in Step 2. Specifically, the server inputs the prompt sentence and keywords into the generation AI model and executes a command such as, "Generate a story based on Arashiyama in the medieval period." The output is the generated story.

[0086] Step 4:

[0087] The server performs modifications and formatting of the generated story.

[0088] The input is the story generated in step 3. The server uses a grammar checking program to verify the story's consistency and correct any errors as needed. The completed story is then formatted so that it can be displayed appropriately on the user's terminal. The output is the formatted story.

[0089] Step 5:

[0090] The device delivers a shaped narrative to the user.

[0091] The input is the story completed in step 4. The device displays the story to the user via an application or web page. Furthermore, it is possible to play the story as an audio guide using the device's text-to-speech function. The output is the story that the user views or listens to.

[0092] Step 6:

[0093] The server plans tourism projects in collaboration with local communities, based on the story.

[0094] The input is the story and local resource data completed in Step 4. The server works with local organizations to develop events and tourist routes related to the story. This makes it possible to create new tourist experiences. The output is information on the planned events and routes.

[0095] Step 7:

[0096] Users provide feedback after their sightseeing experience.

[0097] The input consists of the user's experiences and opinions. Users use a terminal application to input their opinions on the content they have experienced and submit feedback. The output is data stored on the server as user feedback.

[0098] Step 8:

[0099] The server analyzes the collected feedback to improve the quality of story generation.

[0100] The input is the feedback data obtained in step 7. The server uses an analysis algorithm to analyze this data and utilize it for future story generation and new tourism planning. This enables continuous improvement of the system and allows for the provision of an optimized experience for users. The output is improvement measures based on the analysis results.

[0101] (Application Example 1)

[0102] 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."

[0103] Currently, the information tourists receive at tourist destinations relies heavily on limited visual and textual information. Furthermore, narratives used to convey the charm of these destinations are not fully utilized and are not effectively used to capture tourists' attention. As a result, there is a challenge in that understanding of the history and culture of tourist destinations does not deepen, and the quality of the tourist experience does not improve.

[0104] 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.

[0105] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story based on the collected tourist information, and location identification means for identifying the user's current location and providing the story visually and aurally. This allows tourists to interactively experience stories based on local history and culture through visual devices, deepening their understanding of the tourist destination.

[0106] "Information gathering means" refers to means that have the function of collecting diverse information related to tourist destinations from databases and public records.

[0107] "Generative methods" refer to the means of creating new narratives using generative AI models and natural language processing based on collected information.

[0108] "User interface provisioning means" refers to means that have the function of interactively displaying the generated story on the terminal used by the user.

[0109] "Regional collaboration methods" refer to means of planning events and tourist routes in cooperation with local commercial and tourist facilities, based on the generated narratives.

[0110] A "feedback collection method" is a means of collecting opinions and impressions from users who have completed a tourist experience.

[0111] "Improvement methods" refer to means of analyzing collected feedback and using that feedback to improve the quality of story generation.

[0112] A "location identification means" is a means that has the function of identifying the user's current geographical location and providing appropriate information accordingly.

[0113] The system of this invention aims to automatically generate and provide stories based on information about tourist destinations. The server is equipped with information gathering means that collect information about tourist destinations from online databases and local public records. The collected information is analyzed and summarized using natural language processing technology. This extracts themes and keywords that form the basis of the stories. Next, a generation AI model is used to generate stories based on the themes. The generated stories are checked for consistency and automatically corrected as needed. Finally, the formatted stories are provided to devices such as smartphones and tablets.

[0114] The device displays the story through a user interface. The story is delivered using location-based means that accurately determine the user's location and provide information and stories about the target tourist destination visually and aurally. Through this process, tourists can experience detailed information and episodes that they wouldn't normally have access to.

[0115] As a concrete example, when tourists visit historical sites, the device displays stories related to the location in streaming format, and an audio guide explains the historical background. For instance, a prompt such as, "Generate a story from the perspective of an Edo-period merchant about a historical episode related to a merchant house in an old alley in Kyoto," can be input into a generating AI model to create a story. The stories generated in this way help viewers gain a deeper understanding of history and culture.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server collects information related to tourist destinations from online databases and public records. It uses the name of the tourist destination and related keywords as input. The output is a set of detailed information about the history, culture, and geography of the tourist destination. This includes text and image data.

[0119] Step 2:

[0120] The server analyzes the collected information using natural language processing algorithms. Specifically, it extracts important themes and keywords from the text. The input is the information set obtained in step 1, and the output is a list of themes and keywords that form the basis of the story.

[0121] Step 3:

[0122] The server inputs prompt sentences into the generative AI model and generates a story. These prompt sentences are created based on the keywords and themes obtained in step 2. The input is the output list from step 2, and the output is the completed story text data to be provided to the user.

[0123] Step 4:

[0124] The server checks the generated story for consistency and expressiveness, and makes corrections as needed. An automated machine proofreading tool is used, and the output is the corrected story text. This text is then formatted into a format usable by the user interface.

[0125] Step 5:

[0126] The device obtains the user's location information using a GPS sensor. The input is the latitude and longitude of the current location, and the output is a list of appropriate tourist destination information based on the location.

[0127] Step 6:

[0128] The device selects the most suitable story based on the user's current location and delivers the story through visual and auditory means. Using the story text output in Step 4, it displays on-screen information, provides audio guidance, and plays videos as needed. The output is the audiovisual information the user experiences.

[0129] Step 7:

[0130] After the experience, users provide feedback through their device. This feedback consists of the user's impressions and opinions, and the device sends this data to the server. The output is the feedback data sent to the server.

[0131] Step 8:

[0132] The server analyzes the collected feedback and obtains data to improve the quality of story generation. The input is user feedback, and the output is analysis results that will help improve the quality of future stories.

[0133] 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.

[0134] This invention is a system for enhancing the tourism experience, and in particular aims to provide a more personalized tourism experience by combining it with an emotion engine that recognizes the user's emotions. This system aggregates tourism information, generates narratives, provides users with appropriate experiences, and has the function to adjust the content based on user feedback and emotional information.

[0135] First, the server collects a vast amount of information related to tourist destinations. This information is primarily collected from public records and local government documents, and aggregates data specifically focused on the history and culture of the tourist destinations. Natural language processing is then performed on this information, and the resulting data is used to form a narrative storyline.

[0136] Next, in generating the story, the server reflects the collected information and the user's emotional information. Through emotion recognition, the terminal analyzes emotional data from the user's voice and facial expressions, and this data is quantified by the emotion engine. Based on this data, the storytelling is customized to better suit the individual user. For example, if the user is feeling surprised or excited, the story will include more active developments that reflect those emotions, while if they are relaxed, a calmer development will be chosen.

[0137] After the story is generated, the device provides an interactive experience to the viewer through a dedicated application or web platform. Users can visit tourist destinations and learn about the history and culture of those places through the story. This allows visitors not only to visit tourist spots but also to experience the stories behind them with emotion.

[0138] Furthermore, the server uses emotion recognition technology to collect data and collaborates with local communities to plan tourist routes and events. The planned events are narrative-based and may include elements that amplify users' emotions.

[0139] Finally, after the sightseeing experience, users can provide feedback through the application. The device sends this feedback along with emotional data to the server, where the system analyzes and improves. This allows for more appropriate and personalized content in future story generation and sightseeing route construction.

[0140] As a concrete example, when visiting a historical site, the narrative is tailored to enhance the user's excitement by including dramatic episodes related to the construction of the site. At the same time, events are prepared on-site, and photo spots are set up where participants can take pictures dressed in period clothing. Such experiences deeply satisfy the user's emotions and make them feel a sense of familiarity with even unfamiliar tourist destinations.

[0141] Thus, this invention is configured as a system that allows users to deeply learn about the culture and history of tourist destinations and have experiences that reflect their emotions. This enriches the tourist experience and enhances the value of local communities as tourism resources.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The server collects historical and cultural information related to tourist destinations from online databases and local government sources. This data includes the historical background, legends, and cultural significance of the tourist attractions.

[0145] Step 2:

[0146] The server performs natural language processing on the collected information. This extracts the key points and themes of the information, and identifies keywords that will serve as the basis for generating a story.

[0147] Step 3:

[0148] The device reads the user's voice and facial expressions using an emotion sensor, and the emotion engine analyzes the emotional data. This process quantifies the user's current emotional state.

[0149] Step 4:

[0150] The server uses a generative AI model to generate stories based on emotional data. The stories are optimized to match the user's emotions, taking into account extracted keywords and emotional data.

[0151] Step 5:

[0152] The server sends the generated story to the terminal and formats it in a user-accessible format. This includes text display, audio guides, and visual aids. The user then uses this to begin their sightseeing experience.

[0153] Step 6:

[0154] Users experience a story delivered through their device. While visiting tourist destinations, they can feel the historical background and narrative elements in real time. Throughout this process, the user's emotional responses are continuously monitored by emotion sensors.

[0155] Step 7:

[0156] The server collaborates with local commercial and tourist facilities to plan story-based tourist routes and events. This allows tourists to participate in local events and activities related to the story.

[0157] Step 8:

[0158] After their sightseeing experience, users submit feedback and comments through the application. The device then sends this data to the server.

[0159] Step 9:

[0160] The server analyzes the collected feedback and sentiment data to improve the story generation process and the content of the tourist routes. This ensures that the next tourist experience will be of even higher quality.

[0161] (Example 2)

[0162] 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".

[0163] Modern tourism experiences are often uniform for some users, lacking a deep understanding of the local culture and history. Furthermore, standardized tourist routes and events fail to adequately reflect the interests and emotions of individual users. As a result, tourists are unable to fully grasp the essence of their destinations, and the tourism resources of local communities are not being utilized to their fullest potential. Therefore, there is a need to provide tourism experiences that are more tailored to individual users and to generate narratives that take into account the emotions of the users.

[0164] 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.

[0165] In this invention, the server includes information acquisition means for collecting tourism-related data, generation means for analyzing the collected data and generating a story, and personalization means for analyzing the user's emotional information and customizing the story. This makes it possible for users to gain a deeper understanding of the local culture and history through their tourism experience and to receive a personalized experience tailored to their individual interests and emotions.

[0166] "Information acquisition methods" refer to the processes and technologies for collecting tourism-related data, including accessing data from public records and local sources.

[0167] "Generative means" refers to software or algorithms used to analyze collected tourism-related data and generate themed narratives.

[0168] "Personalization methods" refer to technologies that analyze users' emotional information and use that data to customize the story content to suit the user's needs.

[0169] "User interface provision means" refers to a mechanism that displays a customized story on a user's device, allowing the user to experience it interactively.

[0170] "Local cooperation methods" refer to the methods and processes for collaborating with local commercial and tourist facilities to plan events related to the generated narrative and to attract tourists.

[0171] "Feedback collection methods" refer to systems and techniques for gathering opinions and feedback from users after their travel experience.

[0172] "Improvement methods" refer to methods and algorithms for analyzing collected feedback and using the results to improve the quality of story generation.

[0173] This system is a complex system for personalizing the tourist experience, and it operates through the collaboration of three entities: servers, terminals, and users. The specific operations of each entity are described below.

[0174] The server plays a central role in collecting and analyzing tourism-related data. Through various data acquisition methods, the server accesses public records and local government databases to collect vast amounts of information about tourist destinations. This includes the history, culture, and current events of the destinations. The collected data is analyzed using NLTK, a Python natural language processing library, to generate narratives that form the basis of the tourism experience. Generative AI models are used to generate these narratives, proposing complex storylines. The generated narratives are then customized based on the user's emotional information.

[0175] The device functions as a tool for analyzing the user's emotions. It is equipped with a camera and microphone to capture the user's voice and facial expressions, and the acquired data is analyzed through software such as OpenCV and DeepFace. This quantifies the user's current emotional state and sends it to a server. Based on this data, the server optimizes the story content for the user.

[0176] Users experience the story through a dedicated application on their device. This application, with its interactive user interface, loads and displays the narrative, enabling a real-time, participatory experience through audio guides and AR technology. Users can experience stories related to the tourist destination they are visiting, gaining a deeper understanding of the local culture and history.

[0177] As a concrete example, when visiting a historical site, the server generates a dramatic story related to that site. Based on the user's emotional information, the story's progression is adjusted, incorporating events that evoke surprise and excitement in the user. Meanwhile, at the site, participatory events such as taking photos in period costumes are guided through the device's navigation system.

[0178] An example of a prompt for the generative AI model is: "Choose a historical adventure the user wants to experience and suggest a new travel experience based on their emotional information. For example, consider the places they want to visit and the points that excite them, and generate a story set in a specific era."

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The server collects tourism-related data. The input consists of tourist destination information from government and local authority databases, including historical, cultural, and event information. The server collects this input using public APIs and web scraping techniques and stores it in a database. The output is tourist destination data stored in a parseable format.

[0182] Step 2:

[0183] The server analyzes the collected data using a natural language processing engine. The input is tourist destination information data collected in step 1, which is then tokenized and categorized using the Python NLTK library. The output of the data analysis is storytelling elements that enable the generative AI model to generate a narrative.

[0184] Step 3:

[0185] The server generates a story using a generative AI model. The input is the storytelling elements obtained in step 2. The server combines these elements to generate prompt sentences, which are then input into the AI ​​model. The AI ​​model generates a story based on the theme and saves it as output.

[0186] Step 4:

[0187] The device analyzes the user's emotional information. Input consists of user voice and facial expression data, captured using a camera and microphone. The device uses libraries such as OpenCV and DeepFace to analyze this input and quantify the emotional data. The output is numerical data representing the user's current emotional state.

[0188] Step 5:

[0189] The server customizes the story based on the user's emotional information. The input consists of the emotional numerical data obtained in step 4 and the story generated in step 3. The server combines this data and adjusts the story content according to the user's emotions. The output is a personalized, customized story.

[0190] Step 6:

[0191] The device provides the user with a customized story. The input is the customized story obtained in step 5. The device provides a real-time interactive experience using voice guidance and AR technology through a dedicated application. The output is the story displayed visually and the user's interaction experience.

[0192] Step 7:

[0193] Users provide feedback after their experience. The input consists of the user's own opinions and impressions, which they enter into the application's feedback form. The device sends this data to the server, where it is stored as analyzable data. The output is the feedback data for analysis.

[0194] (Application Example 2)

[0195] 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".

[0196] Current tourist experiences are generally standardized and not personalized to suit the individual interests and emotions of tourists. As a result, tourist experiences are uniform and it is difficult to create a truly memorable experience. Furthermore, opportunities to learn about the history and culture of tourist destinations are limited, making it difficult to maintain visitors' interest over the long term.

[0197] 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.

[0198] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story using natural language processing based on the collected tourist information, and emotion analysis means for analyzing the user's emotions using an emotion recognition engine. This makes it possible to individually adjust the story based on the user's emotions and provide a personalized tourist experience.

[0199] "Information gathering means" refers to a device or function that collects information related to tourist destinations from public records and local government documents.

[0200] A "generative means" is a device or function that applies natural language processing to collected information to form a narrative.

[0201] "User interface provision means" refers to a device or function for providing the generated story to the user via a terminal.

[0202] A "regional collaboration tool" is a device or function that collaborates with local communities to plan tourist routes and events based on generated narratives.

[0203] "Emotional analysis means" refers to a device or function that analyzes a user's voice and facial expressions and quantifies them as emotional data.

[0204] "Personalization means" refers to a device or function that adjusts the development of a story based on data obtained from emotion analysis means, in order to provide an optimal experience for each individual user.

[0205] A "feedback collection method" refers to a device or function that collects opinions and impressions from users after their tourist experience.

[0206] "Improvement measures" refer to devices or functions that analyze collected feedback and use it to improve future story generation.

[0207] To implement this invention, the system is configured as follows: First, the server uses information gathering means to aggregate data related to tourist destinations. This data is collected from public records and local government documents, and comprehensively handles information about the history and culture of the tourist destinations. The server then applies natural language processing technology to the collected information and functions as a generation means for generating narratives. This forms a storyline about the tourist destinations.

[0208] The device is equipped with emotion analysis capabilities, acquiring emotional data by analyzing the user's voice and facial expressions. This emotional data is quantified by an emotion engine, and the story's progression is adjusted by server-side personalization. For example, if the user is excited, a dramatic story is provided, while a calm story is chosen when the user is relaxed. Smart glasses and similar devices are used for emotion analysis.

[0209] Users can experience the story through a user interface, which operates interactively on their device. Throughout the sightseeing flow, users can input opinions and impressions using feedback collection tools. This feedback is analyzed by improvement tools to inform future story generation. For example, if a user participates in a virtual tour of Venice and their emotions are excited, a story related to the carnival will be dynamically suggested.

[0210] An example of a prompt to input into a generative AI model would be: "The user is currently taking a virtual tour of Venice. His emotional state is one of excitement. Please suggest the best storyline for him in this situation."

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The server collects data related to tourist destinations using information gathering tools. It gathers data based on public records and local government documents and stores it in a database. This data covers a wide range of topics, including the history, culture, and geography of the tourist destination. The input is a specified tourist destination, and the output is a dataset of information related to that destination.

[0214] Step 2:

[0215] The server generates stories by performing natural language processing on the collected data. Specifically, it extracts keywords and performs contextual analysis to create storylines with a theme for each tourist destination. The input is the dataset of tourist destinations obtained in step 1, and the output is text data in the form of storylines.

[0216] Step 3:

[0217] Users visit tourist destinations using their devices and receive and play stories through the user interface. Smart glasses or mobile devices are used, and story data is displayed through these devices. The user's input is the selection of a device, and the output is the story as visual and auditory information.

[0218] Step 4:

[0219] The device uses emotion analysis technology to analyze the user's emotions in real time using their facial expressions and voice. Specifically, it acquires data using a camera and microphone, and then analyzes and quantifies it using an emotion engine. The input for this step is the user's facial expressions and voice data, and the output is numerical data representing the user's emotions.

[0220] Step 5:

[0221] The server uses personalization methods to dynamically adjust the story based on sentiment analysis data. It determines the user's emotional state and generates a story development that is appropriate for it. The input is the sentiment data obtained in step 4, and the output is the adjusted storyline.

[0222] Step 6:

[0223] After their sightseeing experience, users input their opinions and impressions based on their experience using feedback collection tools. This can be done through questionnaires or voice input, and the data is sent to the server. The input is user feedback information, and the output is feedback data.

[0224] Step 7:

[0225] As a means of improvement, the server analyzes the collected feedback data and uses it to improve the story generation algorithm. By analyzing the feedback using data analysis techniques and reflecting the new information in the stories and event planning for tourist destinations, the system aims to improve its accuracy. The input is feedback data, and the output is an improved algorithm or new ideas for the next story generation.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] The present invention aims to enhance the tourist experience by automatically generating narratives based on the history and culture of tourist destinations and providing them to tourists. This system mainly consists of three components: a server, terminals, and users.

[0243] First, the server collects information related to the tourist destination. This information is obtained from online databases and local public records, and specifically includes data on historical documents and cultural heritage. Based on this collected data, the server uses natural language processing to extract key points and identify the themes and keywords that will form the basis of the story.

[0244] Next, the server generates a story using a generative AI model based on the collected information. This model has been pre-trained on a vast amount of historical literature and materials, and has the ability to create new stories from the input data. The generated story is checked for grammatical and stylistic consistency, and automatically corrected as needed. Once the corrections are complete, the story is formatted so that it can be displayed on the devices used by tourists.

[0245] Next, the generated story is delivered to the user through a device. This device can be a smartphone or tablet, and users can view the story through a dedicated application or webpage. The story is presented interactively, using not only text but also audio guides and visuals. This allows users to not only visit tourist destinations but also experience the stories behind them simultaneously.

[0246] Furthermore, this narrative will strengthen ties with the local community. The server will plan tourist routes and events based on the generated story, and work with local governments and businesses to attract visitors. Specifically, guided tours based on the story and events featuring local specialty products will be held.

[0247] Finally, users who have completed their sightseeing experience can submit their opinions and impressions as feedback through the application. The device sends this feedback to a server, where the collected feedback is analyzed and used to improve future story generation and plan new routes. In this way, the system is constantly evolving, aiming to provide a better sightseeing experience.

[0248] Thus, the system of the present invention comprehensively supports the entire tourism experience, from collecting tourism information and generating narratives to providing them to users, collaborating with local communities, and improving them through feedback. Specifically, historical narratives related to a particular tourist destination are generated, and events such as guided tours and sales of related products are planned, allowing tourists to enjoy an experience beyond mere sightseeing. This process also revitalizes the local economy and creates new value for tourism.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server collects information related to tourist destinations. This primarily involves retrieving data from online databases and local public records, gathering data on the historical sites and cultural heritage of tourist destinations.

[0252] Step 2:

[0253] The server performs natural language processing on the collected information, extracting key points and highly relevant keywords. This forms the basis for the themes and plot necessary for story generation.

[0254] Step 3:

[0255] The server uses a generative AI model to generate stories based on extracted keywords and themes. The stories are generated as text, and then language and grammar checks are performed. Automatic corrections are applied as needed.

[0256] Step 4:

[0257] The server sends the generated story to the terminal and formats it so that it can be accessed by the user. Specifically, it is converted into a text format that can be read, with added audio guides and visual displays.

[0258] Step 5:

[0259] The device delivers stories to users through dedicated applications and web pages. Users can access the stories using smartphones and tablets and enjoy an interactive experience that combines audio and visuals.

[0260] Step 6:

[0261] Server collaborates with local governments and businesses to plan narrative-based tourist routes and events. This includes planning guided tours and sales events for local products.

[0262] Step 7:

[0263] After their sightseeing experience, users provide feedback through the application. This feedback includes opinions on the story content and the overall experience.

[0264] Step 8:

[0265] The device sends the collected feedback to the server. The server analyzes the feedback and uses it to develop improvement plans to enhance the quality of the story and the content of the tourist experience.

[0266] (Example 1)

[0267] 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."

[0268] Experiences at tourist destinations tend to be fleeting if visitors don't fully understand the history and cultural background hidden within the place. To deeply experience the charm of a place, it's necessary to effectively convey the underlying stories and history, but this is difficult to do automatically. Furthermore, there's a need to improve the quality of tourist resources by leveraging post-visit experiences.

[0269] 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.

[0270] In this invention, the server includes server means for collecting information, natural language processing means for analyzing the collected information and extracting keywords, and generative AI model means for generating stories based on the keywords. This makes it possible to automatically generate stories based on the history and culture of tourist destinations and provide high-quality content to deepen the tourist experience. Furthermore, it is possible to improve the quality of tourist resources through a continuous improvement cycle by utilizing user feedback.

[0271] "Server means" refers to a computer system that has the equipment or role of collecting information and performing data analysis and model execution.

[0272] "Natural language processing methods" refer to processes and technologies that analyze text data and extract important information and keywords.

[0273] "Generative AI model means" refers to a machine learning model or its function that automatically generates new stories or content using collected data and prompts.

[0274] "Correction and formatting means" refers to the process of automatically correcting grammatical and stylistic errors in the generated story and preparing it for display on a device.

[0275] "Interface provision means" refers to technologies that function as applications or web pages for displaying or playing a generated story on a user's terminal.

[0276] "Regional collaboration means" refers to the processes and methods for cooperating with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0277] "Feedback collection methods" refer to the processes and systems for collecting opinions and feedback from users and accumulating them as data.

[0278] "Improvement methods" refer to processes and technologies used to analyze collected feedback and improve the quality of systems and content based on the analysis results.

[0279] This invention is a system that generates stories based on the history and culture of tourist destinations and provides them to users. This system consists of three elements: a server, a terminal, and a user.

[0280] First, the server collects information about tourist destinations. The server accesses public databases and local records via the internet, retrieving information about the history, culture, and other relevant details of the destinations. Specific examples include digital archives provided by local tourism associations and information on historical heritage sites. The server uses natural language processing techniques to process this information, extracting relevant keywords and themes.

[0281] Next, the server generates a story using a generative AI model based on the extracted keywords. This system's generative AI model has learned from a wide range of historical documents and past data, and has the ability to automatically create new stories that are in line with the background of tourist destinations. The generated stories are automatically corrected as needed to ensure consistency in grammar and expression. An example of a prompt sentence is the instruction, "Generate a story based on Arashiyama in the medieval period."

[0282] The generated story is then delivered to the user via a device. This device may be a smartphone or tablet, and the story is displayed to the user through a dedicated application or webpage. The story is provided not only in text format but can also be played as an audio guide, allowing for an interactive experience when combined with visuals.

[0283] Finally, users who have completed the tourism experience can provide their thoughts and feelings as feedback through the terminal. The feedback provided by the users is sent to the server via the terminal, and the server analyzes it to help improve the tourism experience and the quality of the generated stories. As a result, the system can continue to improve continuously and provide a more valuable tourism experience for users.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The server collects information related to the tourist destination.

[0287] The input is a data source such as an online database or regional public records. As a specific operation, the server connects to these information sources via the Internet and obtains data on historical materials and cultural heritages related to the tourist destination. The obtained information is accumulated as output.

[0288] Step 2:

[0289] The server performs natural language processing on the collected information and extracts keywords.

[0290] The input is the raw data collected in Step 1. The server utilizes a natural language processing engine to analyze the data and extract highly relevant keywords and themes. Through this process, elements that serve as the basis for story generation are output.

[0291] Step 3:

[0292] The server generates a story using a generation AI model.

[0293] The input consists of the keywords and prompt sentence obtained in Step 2. Specifically, the server inputs the prompt sentence and keywords into the generation AI model and executes a command such as, "Generate a story based on Arashiyama in the medieval period." The output is the generated story.

[0294] Step 4:

[0295] The server performs modifications and formatting of the generated story.

[0296] The input is the story generated in step 3. The server uses a grammar checking program to verify the story's consistency and correct any errors as needed. The completed story is then formatted so that it can be displayed appropriately on the user's terminal. The output is the formatted story.

[0297] Step 5:

[0298] The device delivers a shaped narrative to the user.

[0299] The input is the story completed in step 4. The device displays the story to the user via an application or web page. Furthermore, it is possible to play the story as an audio guide using the device's text-to-speech function. The output is the story that the user views or listens to.

[0300] Step 6:

[0301] The server plans tourism projects in collaboration with local communities, based on the story.

[0302] The input is the story and local resource data completed in Step 4. The server works with local organizations to develop events and tourist routes related to the story. This makes it possible to create new tourist experiences. The output is information on the planned events and routes.

[0303] Step 7:

[0304] The user provides feedback after the tourism experience.

[0305] The input is the user's experience and feelings. The user uses the terminal application to input opinions on the content experienced and send feedback. The output is the data accumulated on the server as user feedback.

[0306] Step 8:

[0307] The server analyzes the collected feedback to improve the quality of story generation.

[0308] The input is the feedback data obtained in Step 7. The server uses an analysis algorithm to analyze this data and utilize it for the next story generation or new tourism planning. As a result, continuous improvement of the system can be achieved, and it becomes possible to provide an optimized experience for the user. The output is the improvement measures based on the analysis results.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] Currently, the information that tourists experience at tourist destinations depends on limited visual and text information. Also, the stories for conveying the charm of tourist destinations are not fully utilized and are not well utilized to attract the interest of tourists. As a result, there is a problem that the understanding of the history and culture of tourist destinations does not deepen and the quality of the tourism experience does not improve.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story based on the collected tourist information, and location identification means for identifying the user's current location and providing the story visually and aurally. This allows tourists to interactively experience stories based on local history and culture through visual devices, deepening their understanding of the tourist destination.

[0314] "Information gathering means" refers to means that have the function of collecting diverse information related to tourist destinations from databases and public records.

[0315] "Generative methods" refer to the means of creating new narratives using generative AI models and natural language processing based on collected information.

[0316] "User interface provisioning means" refers to means that have the function of interactively displaying the generated story on the terminal used by the user.

[0317] "Regional collaboration methods" refer to means of planning events and tourist routes in cooperation with local commercial and tourist facilities, based on the generated narratives.

[0318] A "feedback collection method" is a means of collecting opinions and impressions from users who have completed a tourist experience.

[0319] "Improvement methods" refer to means of analyzing collected feedback and using that feedback to improve the quality of story generation.

[0320] A "location identification means" is a means that has the function of identifying the user's current geographical location and providing appropriate information accordingly.

[0321] The system of this invention aims to automatically generate and provide stories based on information about tourist destinations. The server is equipped with information gathering means that collect information about tourist destinations from online databases and local public records. The collected information is analyzed and summarized using natural language processing technology. This extracts themes and keywords that form the basis of the stories. Next, a generation AI model is used to generate stories based on the themes. The generated stories are checked for consistency and automatically corrected as needed. Finally, the formatted stories are provided to devices such as smartphones and tablets.

[0322] The device displays the story through a user interface. The story is delivered using location-based means that accurately determine the user's location and provide information and stories about the target tourist destination visually and aurally. Through this process, tourists can experience detailed information and episodes that they wouldn't normally have access to.

[0323] As a concrete example, when tourists visit historical sites, the device displays stories related to the location in streaming format, and an audio guide explains the historical background. For instance, a prompt such as, "Generate a story from the perspective of an Edo-period merchant about a historical episode related to a merchant house in an old alley in Kyoto," can be input into a generating AI model to create a story. The stories generated in this way help viewers gain a deeper understanding of history and culture.

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The server collects information related to tourist destinations from online databases and public records. It uses the name of the tourist destination and related keywords as input. The output is a set of detailed information about the history, culture, and geography of the tourist destination. This includes text and image data.

[0327] Step 2:

[0328] The server analyzes the collected information using natural language processing algorithms. Specifically, it extracts important themes and keywords from the text. The input is the information set obtained in step 1, and the output is a list of themes and keywords that form the basis of the story.

[0329] Step 3:

[0330] The server inputs prompt sentences into the generative AI model and generates a story. These prompt sentences are created based on the keywords and themes obtained in step 2. The input is the output list from step 2, and the output is the completed story text data to be provided to the user.

[0331] Step 4:

[0332] The server checks the generated story for consistency and expressiveness, and makes corrections as needed. An automated machine proofreading tool is used, and the output is the corrected story text. This text is then formatted into a format usable by the user interface.

[0333] Step 5:

[0334] The device obtains the user's location information using a GPS sensor. The input is the latitude and longitude of the current location, and the output is a list of appropriate tourist destination information based on the location.

[0335] Step 6:

[0336] The device selects the most suitable story based on the user's current location and delivers the story through visual and auditory means. Using the story text output in Step 4, it displays on-screen information, provides audio guidance, and plays videos as needed. The output is the audiovisual information the user experiences.

[0337] Step 7:

[0338] After the experience, users provide feedback through their device. This feedback consists of the user's impressions and opinions, and the device sends this data to the server. The output is the feedback data sent to the server.

[0339] Step 8:

[0340] The server analyzes the collected feedback and obtains data to improve the quality of story generation. The input is user feedback, and the output is analysis results that will help improve the quality of future stories.

[0341] 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.

[0342] This invention is a system for enhancing the tourism experience, and in particular aims to provide a more personalized tourism experience by combining it with an emotion engine that recognizes the user's emotions. This system aggregates tourism information, generates narratives, provides users with appropriate experiences, and has the function to adjust the content based on user feedback and emotional information.

[0343] First, the server collects a vast amount of information related to tourist destinations. This information is primarily collected from public records and local government documents, and aggregates data specifically focused on the history and culture of the tourist destinations. Natural language processing is then performed on this information, and the resulting data is used to form a narrative storyline.

[0344] Next, in generating the story, the server reflects the collected information and the user's emotional information. Through emotion recognition, the terminal analyzes emotional data from the user's voice and facial expressions, and this data is quantified by the emotion engine. Based on this data, the storytelling is customized to better suit the individual user. For example, if the user is feeling surprised or excited, the story will include more active developments that reflect those emotions, while if they are relaxed, a calmer development will be chosen.

[0345] After the story is generated, the device provides an interactive experience to the viewer through a dedicated application or web platform. Users can visit tourist destinations and learn about the history and culture of those places through the story. This allows visitors not only to visit tourist spots but also to experience the stories behind them with emotion.

[0346] Furthermore, the server uses emotion recognition technology to collect data and collaborates with local communities to plan tourist routes and events. The planned events are narrative-based and may include elements that amplify users' emotions.

[0347] Finally, after the sightseeing experience, users can provide feedback through the application. The device sends this feedback along with emotional data to the server, where the system analyzes and improves. This allows for more appropriate and personalized content in future story generation and sightseeing route construction.

[0348] As a concrete example, when visiting a historical site, the narrative is tailored to enhance the user's excitement by including dramatic episodes related to the construction of the site. At the same time, events are prepared on-site, and photo spots are set up where participants can take pictures dressed in period clothing. Such experiences deeply satisfy the user's emotions and make them feel a sense of familiarity with even unfamiliar tourist destinations.

[0349] Thus, this invention is configured as a system that allows users to deeply learn about the culture and history of tourist destinations and have experiences that reflect their emotions. This enriches the tourist experience and enhances the value of local communities as tourism resources.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The server collects historical and cultural information related to tourist destinations from online databases and local government sources. This data includes the historical background, legends, and cultural significance of the tourist attractions.

[0353] Step 2:

[0354] The server performs natural language processing on the collected information. This extracts the key points and themes of the information, and identifies keywords that will serve as the basis for generating a story.

[0355] Step 3:

[0356] The device reads the user's voice and facial expressions using an emotion sensor, and the emotion engine analyzes the emotional data. This process quantifies the user's current emotional state.

[0357] Step 4:

[0358] The server uses a generative AI model to generate stories based on emotional data. The stories are optimized to match the user's emotions, taking into account extracted keywords and emotional data.

[0359] Step 5:

[0360] The server sends the generated story to the terminal and formats it in a user-accessible format. This includes text display, audio guides, and visual aids. The user then uses this to begin their sightseeing experience.

[0361] Step 6:

[0362] Users experience a story delivered through their device. While visiting tourist destinations, they can feel the historical background and narrative elements in real time. Throughout this process, the user's emotional responses are continuously monitored by emotion sensors.

[0363] Step 7:

[0364] The server collaborates with local commercial and tourist facilities to plan story-based tourist routes and events. This allows tourists to participate in local events and activities related to the story.

[0365] Step 8:

[0366] After their sightseeing experience, users submit feedback and comments through the application. The device then sends this data to the server.

[0367] Step 9:

[0368] The server analyzes the collected feedback and sentiment data to improve the story generation process and the content of the tourist routes. This ensures that the next tourist experience will be of even higher quality.

[0369] (Example 2)

[0370] 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".

[0371] Modern tourism experiences are often uniform for some users, lacking a deep understanding of the local culture and history. Furthermore, standardized tourist routes and events fail to adequately reflect the interests and emotions of individual users. As a result, tourists are unable to fully grasp the essence of their destinations, and the tourism resources of local communities are not being utilized to their fullest potential. Therefore, there is a need to provide tourism experiences that are more tailored to individual users and to generate narratives that take into account the emotions of the users.

[0372] 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.

[0373] In this invention, the server includes information acquisition means for collecting tourism-related data, generation means for analyzing the collected data and generating a story, and personalization means for analyzing the user's emotional information and customizing the story. This makes it possible for users to gain a deeper understanding of the local culture and history through their tourism experience and to receive a personalized experience tailored to their individual interests and emotions.

[0374] "Information acquisition methods" refer to the processes and technologies for collecting tourism-related data, including accessing data from public records and local sources.

[0375] "Generative means" refers to software or algorithms used to analyze collected tourism-related data and generate themed narratives.

[0376] "Personalization methods" refer to technologies that analyze users' emotional information and use that data to customize the story content to suit the user's needs.

[0377] "User interface provision means" refers to a mechanism that displays a customized story on a user's device, allowing the user to experience it interactively.

[0378] "Local cooperation methods" refer to the methods and processes for collaborating with local commercial and tourist facilities to plan events related to the generated narrative and to attract tourists.

[0379] "Feedback collection methods" refer to systems and techniques for gathering opinions and feedback from users after their travel experience.

[0380] "Improvement methods" refer to methods and algorithms for analyzing collected feedback and using the results to improve the quality of story generation.

[0381] This system is a complex system for personalizing the tourist experience, and it operates through the collaboration of three entities: servers, terminals, and users. The specific operations of each entity are described below.

[0382] The server plays a central role in collecting and analyzing tourism-related data. Through various data acquisition methods, the server accesses public records and local government databases to collect vast amounts of information about tourist destinations. This includes the history, culture, and current events of the destinations. The collected data is analyzed using NLTK, a Python natural language processing library, to generate narratives that form the basis of the tourism experience. Generative AI models are used to generate these narratives, proposing complex storylines. The generated narratives are then customized based on the user's emotional information.

[0383] The device functions as a tool for analyzing the user's emotions. It is equipped with a camera and microphone to capture the user's voice and facial expressions, and the acquired data is analyzed through software such as OpenCV and DeepFace. This quantifies the user's current emotional state and sends it to a server. Based on this data, the server optimizes the story content for the user.

[0384] Users experience the story through a dedicated application on their device. This application, with its interactive user interface, loads and displays the narrative, enabling a real-time, participatory experience through audio guides and AR technology. Users can experience stories related to the tourist destination they are visiting, gaining a deeper understanding of the local culture and history.

[0385] As a concrete example, when visiting a historical site, the server generates a dramatic story related to that site. Based on the user's emotional information, the story's progression is adjusted, incorporating events that evoke surprise and excitement in the user. Meanwhile, at the site, participatory events such as taking photos in period costumes are guided through the device's navigation system.

[0386] An example of a prompt for the generative AI model is: "Choose a historical adventure the user wants to experience and suggest a new travel experience based on their emotional information. For example, consider the places they want to visit and the points that excite them, and generate a story set in a specific era."

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The server collects tourism-related data. The input consists of tourist destination information from government and local authority databases, including historical, cultural, and event information. The server collects this input using public APIs and web scraping techniques and stores it in a database. The output is tourist destination data stored in a parseable format.

[0390] Step 2:

[0391] The server analyzes the collected data using a natural language processing engine. The input is tourist destination information data collected in step 1, which is then tokenized and categorized using the Python NLTK library. The output of the data analysis is storytelling elements that enable the generative AI model to generate a narrative.

[0392] Step 3:

[0393] The server generates a story using a generative AI model. The input is the storytelling elements obtained in step 2. The server combines these elements to generate prompt sentences, which are then input into the AI ​​model. The AI ​​model generates a story based on the theme and saves it as output.

[0394] Step 4:

[0395] The device analyzes the user's emotional information. Input consists of user voice and facial expression data, captured using a camera and microphone. The device uses libraries such as OpenCV and DeepFace to analyze this input and quantify the emotional data. The output is numerical data representing the user's current emotional state.

[0396] Step 5:

[0397] The server customizes the story based on the user's emotional information. The input consists of the emotional numerical data obtained in step 4 and the story generated in step 3. The server combines this data and adjusts the story content according to the user's emotions. The output is a personalized, customized story.

[0398] Step 6:

[0399] The device provides the user with a customized story. The input is the customized story obtained in step 5. The device provides a real-time interactive experience using voice guidance and AR technology through a dedicated application. The output is the story displayed visually and the user's interaction experience.

[0400] Step 7:

[0401] Users provide feedback after their experience. The input consists of the user's own opinions and impressions, which they enter into the application's feedback form. The device sends this data to the server, where it is stored as analyzable data. The output is the feedback data for analysis.

[0402] (Application Example 2)

[0403] 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."

[0404] Current tourist experiences are generally standardized and not personalized to suit the individual interests and emotions of tourists. As a result, tourist experiences are uniform and it is difficult to create a truly memorable experience. Furthermore, opportunities to learn about the history and culture of tourist destinations are limited, making it difficult to maintain visitors' interest over the long term.

[0405] 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.

[0406] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story using natural language processing based on the collected tourist information, and emotion analysis means for analyzing the user's emotions using an emotion recognition engine. This makes it possible to individually adjust the story based on the user's emotions and provide a personalized tourist experience.

[0407] "Information gathering means" refers to a device or function that collects information related to tourist destinations from public records and local government documents.

[0408] A "generative means" is a device or function that applies natural language processing to collected information to form a narrative.

[0409] "User interface provision means" refers to a device or function for providing the generated story to the user via a terminal.

[0410] A "regional collaboration tool" is a device or function that collaborates with local communities to plan tourist routes and events based on generated narratives.

[0411] "Emotional analysis means" refers to a device or function that analyzes a user's voice and facial expressions and quantifies them as emotional data.

[0412] "Personalization means" refers to a device or function that adjusts the development of a story based on data obtained from emotion analysis means, in order to provide an optimal experience for each individual user.

[0413] A "feedback collection method" refers to a device or function that collects opinions and impressions from users after their tourist experience.

[0414] "Improvement measures" refer to devices or functions that analyze collected feedback and use it to improve future story generation.

[0415] To implement this invention, the system is configured as follows: First, the server uses information gathering means to aggregate data related to tourist destinations. This data is collected from public records and local government documents, and comprehensively handles information about the history and culture of the tourist destinations. The server then applies natural language processing technology to the collected information and functions as a generation means for generating narratives. This forms a storyline about the tourist destinations.

[0416] The device is equipped with emotion analysis capabilities, acquiring emotional data by analyzing the user's voice and facial expressions. This emotional data is quantified by an emotion engine, and the story's progression is adjusted by server-side personalization. For example, if the user is excited, a dramatic story is provided, while a calm story is chosen when the user is relaxed. Smart glasses and similar devices are used for emotion analysis.

[0417] Users can experience the story through a user interface, which operates interactively on their device. Throughout the sightseeing flow, users can input opinions and impressions using feedback collection tools. This feedback is analyzed by improvement tools to inform future story generation. For example, if a user participates in a virtual tour of Venice and their emotions are excited, a story related to the carnival will be dynamically suggested.

[0418] An example of a prompt to input into a generative AI model would be: "The user is currently taking a virtual tour of Venice. His emotional state is one of excitement. Please suggest the best storyline for him in this situation."

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The server collects data related to tourist destinations using information gathering tools. It gathers data based on public records and local government documents and stores it in a database. This data covers a wide range of topics, including the history, culture, and geography of the tourist destination. The input is a specified tourist destination, and the output is a dataset of information related to that destination.

[0422] Step 2:

[0423] The server generates stories by performing natural language processing on the collected data. Specifically, it extracts keywords and performs contextual analysis to create storylines with a theme for each tourist destination. The input is the dataset of tourist destinations obtained in step 1, and the output is text data in the form of storylines.

[0424] Step 3:

[0425] Users visit tourist destinations using their devices and receive and play stories through the user interface. Smart glasses or mobile devices are used, and story data is displayed through these devices. The user's input is the selection of a device, and the output is the story as visual and auditory information.

[0426] Step 4:

[0427] The device uses emotion analysis technology to analyze the user's emotions in real time using their facial expressions and voice. Specifically, it acquires data using a camera and microphone, and then analyzes and quantifies it using an emotion engine. The input for this step is the user's facial expressions and voice data, and the output is numerical data representing the user's emotions.

[0428] Step 5:

[0429] The server uses personalization methods to dynamically adjust the story based on sentiment analysis data. It determines the user's emotional state and generates a story development that is appropriate for it. The input is the sentiment data obtained in step 4, and the output is the adjusted storyline.

[0430] Step 6:

[0431] After their sightseeing experience, users input their opinions and impressions based on their experience using feedback collection tools. This can be done through questionnaires or voice input, and the data is sent to the server. The input is user feedback information, and the output is feedback data.

[0432] Step 7:

[0433] As a means of improvement, the server analyzes the collected feedback data and uses it to improve the story generation algorithm. By analyzing the feedback using data analysis techniques and reflecting the new information in the stories and event planning for tourist destinations, the system aims to improve its accuracy. The input is feedback data, and the output is an improved algorithm or new ideas for the next story generation.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] The present invention aims to enhance the tourist experience by automatically generating narratives based on the history and culture of tourist destinations and providing them to tourists. This system mainly consists of three components: a server, terminals, and users.

[0451] First, the server collects information related to the tourist destination. This information is obtained from online databases and local public records, and specifically includes data on historical documents and cultural heritage. Based on this collected data, the server uses natural language processing to extract key points and identify the themes and keywords that will form the basis of the story.

[0452] Next, the server generates a story using a generative AI model based on the collected information. This model has been pre-trained on a vast amount of historical literature and materials, and has the ability to create new stories from the input data. The generated story is checked for grammatical and stylistic consistency, and automatically corrected as needed. Once the corrections are complete, the story is formatted so that it can be displayed on the devices used by tourists.

[0453] Next, the generated story is delivered to the user through a device. This device can be a smartphone or tablet, and users can view the story through a dedicated application or webpage. The story is presented interactively, using not only text but also audio guides and visuals. This allows users to not only visit tourist destinations but also experience the stories behind them simultaneously.

[0454] Furthermore, this narrative will strengthen ties with the local community. The server will plan tourist routes and events based on the generated story, and work with local governments and businesses to attract visitors. Specifically, guided tours based on the story and events featuring local specialty products will be held.

[0455] Finally, users who have completed their sightseeing experience can submit their opinions and impressions as feedback through the application. The device sends this feedback to a server, where the collected feedback is analyzed and used to improve future story generation and plan new routes. In this way, the system is constantly evolving, aiming to provide a better sightseeing experience.

[0456] Thus, the system of the present invention comprehensively supports the entire tourism experience, from collecting tourism information and generating narratives to providing them to users, collaborating with local communities, and improving them through feedback. Specifically, historical narratives related to a particular tourist destination are generated, and events such as guided tours and sales of related products are planned, allowing tourists to enjoy an experience beyond mere sightseeing. This process also revitalizes the local economy and creates new value for tourism.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The server collects information related to tourist destinations. This primarily involves retrieving data from online databases and local public records, gathering data on the historical sites and cultural heritage of tourist destinations.

[0460] Step 2:

[0461] The server performs natural language processing on the collected information, extracting key points and highly relevant keywords. This forms the basis for the themes and plot necessary for story generation.

[0462] Step 3:

[0463] The server uses a generative AI model to generate stories based on extracted keywords and themes. The stories are generated as text, and then language and grammar checks are performed. Automatic corrections are applied as needed.

[0464] Step 4:

[0465] The server sends the generated story to the terminal and formats it so that it can be accessed by the user. Specifically, it is converted into a text format that can be read, with added audio guides and visual displays.

[0466] Step 5:

[0467] The device delivers stories to users through dedicated applications and web pages. Users can access the stories using smartphones and tablets and enjoy an interactive experience that combines audio and visuals.

[0468] Step 6:

[0469] Server collaborates with local governments and businesses to plan narrative-based tourist routes and events. This includes planning guided tours and sales events for local products.

[0470] Step 7:

[0471] After their sightseeing experience, users provide feedback through the application. This feedback includes opinions on the story content and the overall experience.

[0472] Step 8:

[0473] The device sends the collected feedback to the server. The server analyzes the feedback and uses it to develop improvement plans to enhance the quality of the story and the content of the tourist experience.

[0474] (Example 1)

[0475] 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."

[0476] Experiences at tourist destinations tend to be fleeting if visitors don't fully understand the history and cultural background hidden within the place. To deeply experience the charm of a place, it's necessary to effectively convey the underlying stories and history, but this is difficult to do automatically. Furthermore, there's a need to improve the quality of tourist resources by leveraging post-visit experiences.

[0477] 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.

[0478] In this invention, the server includes server means for collecting information, natural language processing means for analyzing the collected information and extracting keywords, and generative AI model means for generating stories based on the keywords. This makes it possible to automatically generate stories based on the history and culture of tourist destinations and provide high-quality content to deepen the tourist experience. Furthermore, it is possible to improve the quality of tourist resources through a continuous improvement cycle by utilizing user feedback.

[0479] "Server means" refers to a computer system that has the equipment or role of collecting information and performing data analysis and model execution.

[0480] "Natural language processing methods" refer to processes and technologies that analyze text data and extract important information and keywords.

[0481] "Generative AI model means" refers to a machine learning model or its function that automatically generates new stories or content using collected data and prompts.

[0482] "Correction and formatting means" refers to the process of automatically correcting grammatical and stylistic errors in the generated story and preparing it for display on a device.

[0483] "Interface provision means" refers to technologies that function as applications or web pages for displaying or playing a generated story on a user's terminal.

[0484] "Regional collaboration means" refers to the processes and methods for cooperating with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0485] "Feedback collection methods" refer to the processes and systems for collecting opinions and feedback from users and accumulating them as data.

[0486] "Improvement methods" refer to processes and technologies used to analyze collected feedback and improve the quality of systems and content based on the analysis results.

[0487] This invention is a system that generates stories based on the history and culture of tourist destinations and provides them to users. This system consists of three elements: a server, a terminal, and a user.

[0488] First, the server collects information about tourist destinations. The server accesses public databases and local records via the internet, retrieving information about the history, culture, and other relevant details of the destinations. Specific examples include digital archives provided by local tourism associations and information on historical heritage sites. The server uses natural language processing techniques to process this information, extracting relevant keywords and themes.

[0489] Next, the server generates a story using a generative AI model based on the extracted keywords. This system's generative AI model has learned from a wide range of historical documents and past data, and has the ability to automatically create new stories that are in line with the background of tourist destinations. The generated stories are automatically corrected as needed to ensure consistency in grammar and expression. An example of a prompt sentence is the instruction, "Generate a story based on Arashiyama in the medieval period."

[0490] The generated story is then delivered to the user via a device. This device may be a smartphone or tablet, and the story is displayed to the user through a dedicated application or webpage. The story is provided not only in text format but can also be played as an audio guide, allowing for an interactive experience when combined with visuals.

[0491] Finally, users who have completed their sightseeing experience can provide feedback on their thoughts and impressions through their device. The feedback provided by users is sent to a server via the device, which analyzes it and uses it to improve the quality of the sightseeing experience and the stories generated. In this way, the system can continuously improve and continue to provide users with more valuable sightseeing experiences.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] The server collects information related to tourist destinations.

[0495] The input consists of data sources such as online databases and local public records. Specifically, the server connects to these information sources via the internet and retrieves data on historical documents and cultural heritage related to tourist destinations. The obtained information is then stored as output.

[0496] Step 2:

[0497] The server performs natural language processing on the collected information to extract keywords.

[0498] The input is the raw data collected in Step 1. The server uses a natural language processing engine to analyze the data and extract highly relevant keywords and themes. This process outputs the elements that form the basis for story generation.

[0499] Step 3:

[0500] The server generates stories using a generative AI model.

[0501] The input consists of the keywords and prompt sentence obtained in Step 2. Specifically, the server inputs the prompt sentence and keywords into the generation AI model and executes a command such as, "Generate a story based on Arashiyama in the medieval period." The output is the generated story.

[0502] Step 4:

[0503] The server performs modifications and formatting of the generated story.

[0504] The input is the story generated in step 3. The server uses a grammar checking program to verify the story's consistency and correct any errors as needed. The completed story is then formatted so that it can be displayed appropriately on the user's terminal. The output is the formatted story.

[0505] Step 5:

[0506] The device delivers a shaped narrative to the user.

[0507] The input is the story completed in step 4. The device displays the story to the user via an application or web page. Furthermore, it is possible to play the story as an audio guide using the device's text-to-speech function. The output is the story that the user views or listens to.

[0508] Step 6:

[0509] The server plans tourism projects in collaboration with local communities, based on the story.

[0510] The input is the story and local resource data completed in Step 4. The server works with local organizations to develop events and tourist routes related to the story. This makes it possible to create new tourist experiences. The output is information on the planned events and routes.

[0511] Step 7:

[0512] Users provide feedback after their sightseeing experience.

[0513] The input consists of the user's experiences and opinions. Users use a terminal application to input their opinions on the content they have experienced and submit feedback. The output is data stored on the server as user feedback.

[0514] Step 8:

[0515] The server analyzes the collected feedback to improve the quality of story generation.

[0516] The input is the feedback data obtained in step 7. The server uses an analysis algorithm to analyze this data and utilize it for future story generation and new tourism planning. This enables continuous improvement of the system and allows for the provision of an optimized experience for users. The output is improvement measures based on the analysis results.

[0517] (Application Example 1)

[0518] 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."

[0519] Currently, the information tourists receive at tourist destinations relies heavily on limited visual and textual information. Furthermore, narratives used to convey the charm of these destinations are not fully utilized and are not effectively used to capture tourists' attention. As a result, there is a challenge in that understanding of the history and culture of tourist destinations does not deepen, and the quality of the tourist experience does not improve.

[0520] 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.

[0521] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story based on the collected tourist information, and location identification means for identifying the user's current location and providing the story visually and aurally. This allows tourists to interactively experience stories based on local history and culture through visual devices, deepening their understanding of the tourist destination.

[0522] "Information gathering means" refers to means that have the function of collecting diverse information related to tourist destinations from databases and public records.

[0523] "Generative methods" refer to the means of creating new narratives using generative AI models and natural language processing based on collected information.

[0524] "User interface provisioning means" refers to means that have the function of interactively displaying the generated story on the terminal used by the user.

[0525] "Regional collaboration methods" refer to means of planning events and tourist routes in cooperation with local commercial and tourist facilities, based on the generated narratives.

[0526] A "feedback collection method" is a means of collecting opinions and impressions from users who have completed a tourist experience.

[0527] "Improvement methods" refer to means of analyzing collected feedback and using that feedback to improve the quality of story generation.

[0528] A "location identification means" is a means that has the function of identifying the user's current geographical location and providing appropriate information accordingly.

[0529] The system of this invention aims to automatically generate and provide stories based on information about tourist destinations. The server is equipped with information gathering means that collect information about tourist destinations from online databases and local public records. The collected information is analyzed and summarized using natural language processing technology. This extracts themes and keywords that form the basis of the stories. Next, a generation AI model is used to generate stories based on the themes. The generated stories are checked for consistency and automatically corrected as needed. Finally, the formatted stories are provided to devices such as smartphones and tablets.

[0530] The device displays the story through a user interface. The story is delivered using location-based means that accurately determine the user's location and provide information and stories about the target tourist destination visually and aurally. Through this process, tourists can experience detailed information and episodes that they wouldn't normally have access to.

[0531] As a concrete example, when tourists visit historical sites, the device displays stories related to the location in streaming format, and an audio guide explains the historical background. For instance, a prompt such as, "Generate a story from the perspective of an Edo-period merchant about a historical episode related to a merchant house in an old alley in Kyoto," can be input into a generating AI model to create a story. The stories generated in this way help viewers gain a deeper understanding of history and culture.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The server collects information related to tourist destinations from online databases and public records. It uses the name of the tourist destination and related keywords as input. The output is a set of detailed information about the history, culture, and geography of the tourist destination. This includes text and image data.

[0535] Step 2:

[0536] The server analyzes the collected information using natural language processing algorithms. Specifically, it extracts important themes and keywords from the text. The input is the information set obtained in step 1, and the output is a list of themes and keywords that form the basis of the story.

[0537] Step 3:

[0538] The server inputs prompt sentences into the generative AI model and generates a story. These prompt sentences are created based on the keywords and themes obtained in step 2. The input is the output list from step 2, and the output is the completed story text data to be provided to the user.

[0539] Step 4:

[0540] The server checks the generated story for consistency and expressiveness, and makes corrections as needed. An automated machine proofreading tool is used, and the output is the corrected story text. This text is then formatted into a format usable by the user interface.

[0541] Step 5:

[0542] The device obtains the user's location information using a GPS sensor. The input is the latitude and longitude of the current location, and the output is a list of appropriate tourist destination information based on the location.

[0543] Step 6:

[0544] The device selects the most suitable story based on the user's current location and delivers the story through visual and auditory means. Using the story text output in Step 4, it displays on-screen information, provides audio guidance, and plays videos as needed. The output is the audiovisual information the user experiences.

[0545] Step 7:

[0546] After the experience, users provide feedback through their device. This feedback consists of the user's impressions and opinions, and the device sends this data to the server. The output is the feedback data sent to the server.

[0547] Step 8:

[0548] The server analyzes the collected feedback and obtains data to improve the quality of story generation. The input is user feedback, and the output is analysis results that will help improve the quality of future stories.

[0549] 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.

[0550] This invention is a system for enhancing the tourism experience, and in particular aims to provide a more personalized tourism experience by combining it with an emotion engine that recognizes the user's emotions. This system aggregates tourism information, generates narratives, provides users with appropriate experiences, and has the function to adjust the content based on user feedback and emotional information.

[0551] First, the server collects a vast amount of information related to tourist destinations. This information is primarily collected from public records and local government documents, and aggregates data specifically focused on the history and culture of the tourist destinations. Natural language processing is then performed on this information, and the resulting data is used to form a narrative storyline.

[0552] Next, in generating the story, the server reflects the collected information and the user's emotional information. Through emotion recognition, the terminal analyzes emotional data from the user's voice and facial expressions, and this data is quantified by the emotion engine. Based on this data, the storytelling is customized to better suit the individual user. For example, if the user is feeling surprised or excited, the story will include more active developments that reflect those emotions, while if they are relaxed, a calmer development will be chosen.

[0553] After the story is generated, the device provides an interactive experience to the viewer through a dedicated application or web platform. Users can visit tourist destinations and learn about the history and culture of those places through the story. This allows visitors not only to visit tourist spots but also to experience the stories behind them with emotion.

[0554] Furthermore, the server uses emotion recognition technology to collect data and collaborates with local communities to plan tourist routes and events. The planned events are narrative-based and may include elements that amplify users' emotions.

[0555] Finally, after the sightseeing experience, users can provide feedback through the application. The device sends this feedback along with emotional data to the server, where the system analyzes and improves. This allows for more appropriate and personalized content in future story generation and sightseeing route construction.

[0556] As a concrete example, when visiting a historical site, the narrative is tailored to enhance the user's excitement by including dramatic episodes related to the construction of the site. At the same time, events are prepared on-site, and photo spots are set up where participants can take pictures dressed in period clothing. Such experiences deeply satisfy the user's emotions and make them feel a sense of familiarity with even unfamiliar tourist destinations.

[0557] Thus, this invention is configured as a system that allows users to deeply learn about the culture and history of tourist destinations and have experiences that reflect their emotions. This enriches the tourist experience and enhances the value of local communities as tourism resources.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server collects historical and cultural information related to tourist destinations from online databases and local government sources. This data includes the historical background, legends, and cultural significance of the tourist attractions.

[0561] Step 2:

[0562] The server performs natural language processing on the collected information. This extracts the key points and themes of the information, and identifies keywords that will serve as the basis for generating a story.

[0563] Step 3:

[0564] The device reads the user's voice and facial expressions using an emotion sensor, and the emotion engine analyzes the emotional data. This process quantifies the user's current emotional state.

[0565] Step 4:

[0566] The server uses a generative AI model to generate stories based on emotional data. The stories are optimized to match the user's emotions, taking into account extracted keywords and emotional data.

[0567] Step 5:

[0568] The server sends the generated story to the terminal and formats it in a user-accessible format. This includes text display, audio guides, and visual aids. The user then uses this to begin their sightseeing experience.

[0569] Step 6:

[0570] Users experience a story delivered through their device. While visiting tourist destinations, they can feel the historical background and narrative elements in real time. Throughout this process, the user's emotional responses are continuously monitored by emotion sensors.

[0571] Step 7:

[0572] The server collaborates with local commercial and tourist facilities to plan story-based tourist routes and events. This allows tourists to participate in local events and activities related to the story.

[0573] Step 8:

[0574] After their sightseeing experience, users submit feedback and comments through the application. The device then sends this data to the server.

[0575] Step 9:

[0576] The server analyzes the collected feedback and sentiment data to improve the story generation process and the content of the tourist routes. This ensures that the next tourist experience will be of even higher quality.

[0577] (Example 2)

[0578] 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."

[0579] Modern tourism experiences are often uniform for some users, lacking a deep understanding of the local culture and history. Furthermore, standardized tourist routes and events fail to adequately reflect the interests and emotions of individual users. As a result, tourists are unable to fully grasp the essence of their destinations, and the tourism resources of local communities are not being utilized to their fullest potential. Therefore, there is a need to provide tourism experiences that are more tailored to individual users and to generate narratives that take into account the emotions of the users.

[0580] 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.

[0581] In this invention, the server includes information acquisition means for collecting tourism-related data, generation means for analyzing the collected data and generating a story, and personalization means for analyzing the user's emotional information and customizing the story. This makes it possible for users to gain a deeper understanding of the local culture and history through their tourism experience and to receive a personalized experience tailored to their individual interests and emotions.

[0582] "Information acquisition methods" refer to the processes and technologies for collecting tourism-related data, including accessing data from public records and local sources.

[0583] "Generative means" refers to software or algorithms used to analyze collected tourism-related data and generate themed narratives.

[0584] "Personalization methods" refer to technologies that analyze users' emotional information and use that data to customize the story content to suit the user's needs.

[0585] "User interface provision means" refers to a mechanism that displays a customized story on a user's device, allowing the user to experience it interactively.

[0586] "Local cooperation methods" refer to the methods and processes for collaborating with local commercial and tourist facilities to plan events related to the generated narrative and to attract tourists.

[0587] "Feedback collection methods" refer to systems and techniques for gathering opinions and feedback from users after their travel experience.

[0588] "Improvement methods" refer to methods and algorithms for analyzing collected feedback and using the results to improve the quality of story generation.

[0589] This system is a complex system for personalizing the tourist experience, and it operates through the collaboration of three entities: servers, terminals, and users. The specific operations of each entity are described below.

[0590] The server plays a central role in collecting and analyzing tourism-related data. Through various data acquisition methods, the server accesses public records and local government databases to collect vast amounts of information about tourist destinations. This includes the history, culture, and current events of the destinations. The collected data is analyzed using NLTK, a Python natural language processing library, to generate narratives that form the basis of the tourism experience. Generative AI models are used to generate these narratives, proposing complex storylines. The generated narratives are then customized based on the user's emotional information.

[0591] The device functions as a tool for analyzing the user's emotions. It is equipped with a camera and microphone to capture the user's voice and facial expressions, and the acquired data is analyzed through software such as OpenCV and DeepFace. This quantifies the user's current emotional state and sends it to a server. Based on this data, the server optimizes the story content for the user.

[0592] Users experience the story through a dedicated application on their device. This application, with its interactive user interface, loads and displays the narrative, enabling a real-time, participatory experience through audio guides and AR technology. Users can experience stories related to the tourist destination they are visiting, gaining a deeper understanding of the local culture and history.

[0593] As a concrete example, when visiting a historical site, the server generates a dramatic story related to that site. Based on the user's emotional information, the story's progression is adjusted, incorporating events that evoke surprise and excitement in the user. Meanwhile, at the site, participatory events such as taking photos in period costumes are guided through the device's navigation system.

[0594] An example of a prompt for the generative AI model is: "Choose a historical adventure the user wants to experience and suggest a new travel experience based on their emotional information. For example, consider the places they want to visit and the points that excite them, and generate a story set in a specific era."

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The server collects tourism-related data. The input consists of tourist destination information from government and local authority databases, including historical, cultural, and event information. The server collects this input using public APIs and web scraping techniques and stores it in a database. The output is tourist destination data stored in a parseable format.

[0598] Step 2:

[0599] The server analyzes the collected data using a natural language processing engine. The input is tourist destination information data collected in step 1, which is then tokenized and categorized using the Python NLTK library. The output of the data analysis is storytelling elements that enable the generative AI model to generate a narrative.

[0600] Step 3:

[0601] The server generates a story using a generative AI model. The input is the storytelling elements obtained in step 2. The server combines these elements to generate prompt sentences, which are then input into the AI ​​model. The AI ​​model generates a story based on the theme and saves it as output.

[0602] Step 4:

[0603] The device analyzes the user's emotional information. Input consists of user voice and facial expression data, captured using a camera and microphone. The device uses libraries such as OpenCV and DeepFace to analyze this input and quantify the emotional data. The output is numerical data representing the user's current emotional state.

[0604] Step 5:

[0605] The server customizes the story based on the user's emotional information. The input consists of the emotional numerical data obtained in step 4 and the story generated in step 3. The server combines this data and adjusts the story content according to the user's emotions. The output is a personalized, customized story.

[0606] Step 6:

[0607] The device provides the user with a customized story. The input is the customized story obtained in step 5. The device provides a real-time interactive experience using voice guidance and AR technology through a dedicated application. The output is the story displayed visually and the user's interaction experience.

[0608] Step 7:

[0609] Users provide feedback after their experience. The input consists of the user's own opinions and impressions, which they enter into the application's feedback form. The device sends this data to the server, where it is stored as analyzable data. The output is the feedback data for analysis.

[0610] (Application Example 2)

[0611] 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."

[0612] Current tourist experiences are generally standardized and not personalized to suit the individual interests and emotions of tourists. As a result, tourist experiences are uniform and it is difficult to create a truly memorable experience. Furthermore, opportunities to learn about the history and culture of tourist destinations are limited, making it difficult to maintain visitors' interest over the long term.

[0613] 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.

[0614] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story using natural language processing based on the collected tourist information, and emotion analysis means for analyzing the user's emotions using an emotion recognition engine. This makes it possible to individually adjust the story based on the user's emotions and provide a personalized tourist experience.

[0615] "Information gathering means" refers to a device or function that collects information related to tourist destinations from public records and local government documents.

[0616] A "generative means" is a device or function that applies natural language processing to collected information to form a narrative.

[0617] "User interface provision means" refers to a device or function for providing the generated story to the user via a terminal.

[0618] A "regional collaboration tool" is a device or function that collaborates with local communities to plan tourist routes and events based on generated narratives.

[0619] "Emotional analysis means" refers to a device or function that analyzes a user's voice and facial expressions and quantifies them as emotional data.

[0620] "Personalization means" refers to a device or function that adjusts the development of a story based on data obtained from emotion analysis means, in order to provide an optimal experience for each individual user.

[0621] A "feedback collection method" refers to a device or function that collects opinions and impressions from users after their tourist experience.

[0622] "Improvement measures" refer to devices or functions that analyze collected feedback and use it to improve future story generation.

[0623] To implement this invention, the system is configured as follows: First, the server uses information gathering means to aggregate data related to tourist destinations. This data is collected from public records and local government documents, and comprehensively handles information about the history and culture of the tourist destinations. The server then applies natural language processing technology to the collected information and functions as a generation means for generating narratives. This forms a storyline about the tourist destinations.

[0624] The device is equipped with emotion analysis capabilities, acquiring emotional data by analyzing the user's voice and facial expressions. This emotional data is quantified by an emotion engine, and the story's progression is adjusted by server-side personalization. For example, if the user is excited, a dramatic story is provided, while a calm story is chosen when the user is relaxed. Smart glasses and similar devices are used for emotion analysis.

[0625] Users can experience the story through a user interface, which operates interactively on their device. Throughout the sightseeing flow, users can input opinions and impressions using feedback collection tools. This feedback is analyzed by improvement tools to inform future story generation. For example, if a user participates in a virtual tour of Venice and their emotions are excited, a story related to the carnival will be dynamically suggested.

[0626] An example of a prompt to input into a generative AI model would be: "The user is currently taking a virtual tour of Venice. His emotional state is one of excitement. Please suggest the best storyline for him in this situation."

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] The server collects data related to tourist destinations using information gathering tools. It gathers data based on public records and local government documents and stores it in a database. This data covers a wide range of topics, including the history, culture, and geography of the tourist destination. The input is a specified tourist destination, and the output is a dataset of information related to that destination.

[0630] Step 2:

[0631] The server generates stories by performing natural language processing on the collected data. Specifically, it extracts keywords and performs contextual analysis to create storylines with a theme for each tourist destination. The input is the dataset of tourist destinations obtained in step 1, and the output is text data in the form of storylines.

[0632] Step 3:

[0633] Users visit tourist destinations using their devices and receive and play stories through the user interface. Smart glasses or mobile devices are used, and story data is displayed through these devices. The user's input is the selection of a device, and the output is the story as visual and auditory information.

[0634] Step 4:

[0635] The device uses emotion analysis technology to analyze the user's emotions in real time using their facial expressions and voice. Specifically, it acquires data using a camera and microphone, and then analyzes and quantifies it using an emotion engine. The input for this step is the user's facial expressions and voice data, and the output is numerical data representing the user's emotions.

[0636] Step 5:

[0637] The server uses personalization methods to dynamically adjust the story based on sentiment analysis data. It determines the user's emotional state and generates a story development that is appropriate for it. The input is the sentiment data obtained in step 4, and the output is the adjusted storyline.

[0638] Step 6:

[0639] After their sightseeing experience, users input their opinions and impressions based on their experience using feedback collection tools. This can be done through questionnaires or voice input, and the data is sent to the server. The input is user feedback information, and the output is feedback data.

[0640] Step 7:

[0641] As a means of improvement, the server analyzes the collected feedback data and uses it to improve the story generation algorithm. By analyzing the feedback using data analysis techniques and reflecting the new information in the stories and event planning for tourist destinations, the system aims to improve its accuracy. The input is feedback data, and the output is an improved algorithm or new ideas for the next story generation.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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".

[0659] The present invention aims to enhance the tourist experience by automatically generating narratives based on the history and culture of tourist destinations and providing them to tourists. This system mainly consists of three components: a server, terminals, and users.

[0660] First, the server collects information related to the tourist destination. This information is obtained from online databases and local public records, and specifically includes data on historical documents and cultural heritage. Based on this collected data, the server uses natural language processing to extract key points and identify the themes and keywords that will form the basis of the story.

[0661] Next, the server generates a story using a generative AI model based on the collected information. This model has been pre-trained on a vast amount of historical literature and materials, and has the ability to create new stories from the input data. The generated story is checked for grammatical and stylistic consistency, and automatically corrected as needed. Once the corrections are complete, the story is formatted so that it can be displayed on the devices used by tourists.

[0662] Next, the generated story is delivered to the user through a device. This device can be a smartphone or tablet, and users can view the story through a dedicated application or webpage. The story is presented interactively, using not only text but also audio guides and visuals. This allows users to not only visit tourist destinations but also experience the stories behind them simultaneously.

[0663] Furthermore, this narrative will strengthen ties with the local community. The server will plan tourist routes and events based on the generated story, and work with local governments and businesses to attract visitors. Specifically, guided tours based on the story and events featuring local specialty products will be held.

[0664] Finally, users who have completed their sightseeing experience can submit their opinions and impressions as feedback through the application. The device sends this feedback to a server, where the collected feedback is analyzed and used to improve future story generation and plan new routes. In this way, the system is constantly evolving, aiming to provide a better sightseeing experience.

[0665] Thus, the system of the present invention comprehensively supports the entire tourism experience, from collecting tourism information and generating narratives to providing them to users, collaborating with local communities, and improving them through feedback. Specifically, historical narratives related to a particular tourist destination are generated, and events such as guided tours and sales of related products are planned, allowing tourists to enjoy an experience beyond mere sightseeing. This process also revitalizes the local economy and creates new value for tourism.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The server collects information related to tourist destinations. This primarily involves retrieving data from online databases and local public records, gathering data on the historical sites and cultural heritage of tourist destinations.

[0669] Step 2:

[0670] The server performs natural language processing on the collected information, extracting key points and highly relevant keywords. This forms the basis for the themes and plot necessary for story generation.

[0671] Step 3:

[0672] The server uses a generative AI model to generate stories based on extracted keywords and themes. The stories are generated as text, and then language and grammar checks are performed. Automatic corrections are applied as needed.

[0673] Step 4:

[0674] The server sends the generated story to the terminal and formats it so that it can be accessed by the user. Specifically, it is converted into a text format that can be read, with added audio guides and visual displays.

[0675] Step 5:

[0676] The device delivers stories to users through dedicated applications and web pages. Users can access the stories using smartphones and tablets and enjoy an interactive experience that combines audio and visuals.

[0677] Step 6:

[0678] Server collaborates with local governments and businesses to plan narrative-based tourist routes and events. This includes planning guided tours and sales events for local products.

[0679] Step 7:

[0680] After their sightseeing experience, users provide feedback through the application. This feedback includes opinions on the story content and the overall experience.

[0681] Step 8:

[0682] The device sends the collected feedback to the server. The server analyzes the feedback and uses it to develop improvement plans to enhance the quality of the story and the content of the tourist experience.

[0683] (Example 1)

[0684] 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".

[0685] Experiences at tourist destinations tend to be fleeting if visitors don't fully understand the history and cultural background hidden within the place. To deeply experience the charm of a place, it's necessary to effectively convey the underlying stories and history, but this is difficult to do automatically. Furthermore, there's a need to improve the quality of tourist resources by leveraging post-visit experiences.

[0686] 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.

[0687] In this invention, the server includes server means for collecting information, natural language processing means for analyzing the collected information and extracting keywords, and generative AI model means for generating stories based on the keywords. This makes it possible to automatically generate stories based on the history and culture of tourist destinations and provide high-quality content to deepen the tourist experience. Furthermore, it is possible to improve the quality of tourist resources through a continuous improvement cycle by utilizing user feedback.

[0688] "Server means" refers to a computer system that has the equipment or role of collecting information and performing data analysis and model execution.

[0689] "Natural language processing methods" refer to processes and technologies that analyze text data and extract important information and keywords.

[0690] "Generative AI model means" refers to a machine learning model or its function that automatically generates new stories or content using collected data and prompts.

[0691] "Correction and formatting means" refers to the process of automatically correcting grammatical and stylistic errors in the generated story and preparing it for display on a device.

[0692] "Interface provision means" refers to technologies that function as applications or web pages for displaying or playing a generated story on a user's terminal.

[0693] "Regional collaboration means" refers to the processes and methods for cooperating with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0694] "Feedback collection methods" refer to the processes and systems for collecting opinions and feedback from users and accumulating them as data.

[0695] "Improvement methods" refer to processes and technologies used to analyze collected feedback and improve the quality of systems and content based on the analysis results.

[0696] This invention is a system that generates stories based on the history and culture of tourist destinations and provides them to users. This system consists of three elements: a server, a terminal, and a user.

[0697] First, the server collects information about tourist destinations. The server accesses public databases and local records via the internet, retrieving information about the history, culture, and other relevant details of the destinations. Specific examples include digital archives provided by local tourism associations and information on historical heritage sites. The server uses natural language processing techniques to process this information, extracting relevant keywords and themes.

[0698] Next, the server generates a story using a generative AI model based on the extracted keywords. This system's generative AI model has learned from a wide range of historical documents and past data, and has the ability to automatically create new stories that are in line with the background of tourist destinations. The generated stories are automatically corrected as needed to ensure consistency in grammar and expression. An example of a prompt sentence is the instruction, "Generate a story based on Arashiyama in the medieval period."

[0699] The generated story is then delivered to the user via a device. This device may be a smartphone or tablet, and the story is displayed to the user through a dedicated application or webpage. The story is provided not only in text format but can also be played as an audio guide, allowing for an interactive experience when combined with visuals.

[0700] Finally, users who have completed their sightseeing experience can provide feedback on their thoughts and impressions through their device. The feedback provided by users is sent to a server via the device, which analyzes it and uses it to improve the quality of the sightseeing experience and the stories generated. In this way, the system can continuously improve and continue to provide users with more valuable sightseeing experiences.

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The server collects information related to tourist destinations.

[0704] The input consists of data sources such as online databases and local public records. Specifically, the server connects to these information sources via the internet and retrieves data on historical documents and cultural heritage related to tourist destinations. The obtained information is then stored as output.

[0705] Step 2:

[0706] The server performs natural language processing on the collected information to extract keywords.

[0707] The input is the raw data collected in Step 1. The server uses a natural language processing engine to analyze the data and extract highly relevant keywords and themes. This process outputs the elements that form the basis for story generation.

[0708] Step 3:

[0709] The server generates stories using a generative AI model.

[0710] The input consists of the keywords and prompt sentence obtained in Step 2. Specifically, the server inputs the prompt sentence and keywords into the generation AI model and executes a command such as, "Generate a story based on Arashiyama in the medieval period." The output is the generated story.

[0711] Step 4:

[0712] The server performs modifications and formatting of the generated story.

[0713] The input is the story generated in step 3. The server uses a grammar checking program to verify the story's consistency and correct any errors as needed. The completed story is then formatted so that it can be displayed appropriately on the user's terminal. The output is the formatted story.

[0714] Step 5:

[0715] The device delivers a shaped narrative to the user.

[0716] The input is the story completed in step 4. The device displays the story to the user via an application or web page. Furthermore, it is possible to play the story as an audio guide using the device's text-to-speech function. The output is the story that the user views or listens to.

[0717] Step 6:

[0718] The server plans tourism projects in collaboration with local communities, based on the story.

[0719] The input is the story and local resource data completed in Step 4. The server works with local organizations to develop events and tourist routes related to the story. This makes it possible to create new tourist experiences. The output is information on the planned events and routes.

[0720] Step 7:

[0721] Users provide feedback after their sightseeing experience.

[0722] The input consists of the user's experiences and opinions. Users use a terminal application to input their opinions on the content they have experienced and submit feedback. The output is data stored on the server as user feedback.

[0723] Step 8:

[0724] The server analyzes the collected feedback to improve the quality of story generation.

[0725] The input is the feedback data obtained in step 7. The server uses an analysis algorithm to analyze this data and utilize it for future story generation and new tourism planning. This enables continuous improvement of the system and allows for the provision of an optimized experience for users. The output is improvement measures based on the analysis results.

[0726] (Application Example 1)

[0727] 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".

[0728] Currently, the information tourists receive at tourist destinations relies heavily on limited visual and textual information. Furthermore, narratives used to convey the charm of these destinations are not fully utilized and are not effectively used to capture tourists' attention. As a result, there is a challenge in that understanding of the history and culture of tourist destinations does not deepen, and the quality of the tourist experience does not improve.

[0729] 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.

[0730] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story based on the collected tourist information, and location identification means for identifying the user's current location and providing the story visually and aurally. This allows tourists to interactively experience stories based on local history and culture through visual devices, deepening their understanding of the tourist destination.

[0731] "Information gathering means" refers to means that have the function of collecting diverse information related to tourist destinations from databases and public records.

[0732] "Generative methods" refer to the means of creating new narratives using generative AI models and natural language processing based on collected information.

[0733] "User interface provisioning means" refers to means that have the function of interactively displaying the generated story on the terminal used by the user.

[0734] "Regional collaboration methods" refer to means of planning events and tourist routes in cooperation with local commercial and tourist facilities, based on the generated narratives.

[0735] A "feedback collection method" is a means of collecting opinions and impressions from users who have completed a tourist experience.

[0736] "Improvement methods" refer to means of analyzing collected feedback and using that feedback to improve the quality of story generation.

[0737] A "location identification means" is a means that has the function of identifying the user's current geographical location and providing appropriate information accordingly.

[0738] The system of this invention aims to automatically generate and provide stories based on information about tourist destinations. The server is equipped with information gathering means that collect information about tourist destinations from online databases and local public records. The collected information is analyzed and summarized using natural language processing technology. This extracts themes and keywords that form the basis of the stories. Next, a generation AI model is used to generate stories based on the themes. The generated stories are checked for consistency and automatically corrected as needed. Finally, the formatted stories are provided to devices such as smartphones and tablets.

[0739] The device displays the story through a user interface. The story is delivered using location-based means that accurately determine the user's location and provide information and stories about the target tourist destination visually and aurally. Through this process, tourists can experience detailed information and episodes that they wouldn't normally have access to.

[0740] As a concrete example, when tourists visit historical sites, the device displays stories related to the location in streaming format, and an audio guide explains the historical background. For instance, a prompt such as, "Generate a story from the perspective of an Edo-period merchant about a historical episode related to a merchant house in an old alley in Kyoto," can be input into a generating AI model to create a story. The stories generated in this way help viewers gain a deeper understanding of history and culture.

[0741] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0742] Step 1:

[0743] The server collects information related to tourist destinations from online databases and public records. It uses the name of the tourist destination and related keywords as input. The output is a set of detailed information about the history, culture, and geography of the tourist destination. This includes text and image data.

[0744] Step 2:

[0745] The server analyzes the collected information using natural language processing algorithms. Specifically, it extracts important themes and keywords from the text. The input is the information set obtained in step 1, and the output is a list of themes and keywords that form the basis of the story.

[0746] Step 3:

[0747] The server inputs prompt sentences into the generative AI model and generates a story. These prompt sentences are created based on the keywords and themes obtained in step 2. The input is the output list from step 2, and the output is the completed story text data to be provided to the user.

[0748] Step 4:

[0749] The server checks the generated story for consistency and expressiveness, and makes corrections as needed. An automated machine proofreading tool is used, and the output is the corrected story text. This text is then formatted into a format usable by the user interface.

[0750] Step 5:

[0751] The device obtains the user's location information using a GPS sensor. The input is the latitude and longitude of the current location, and the output is a list of appropriate tourist destination information based on the location.

[0752] Step 6:

[0753] The device selects the most suitable story based on the user's current location and delivers the story through visual and auditory means. Using the story text output in Step 4, it displays on-screen information, provides audio guidance, and plays videos as needed. The output is the audiovisual information the user experiences.

[0754] Step 7:

[0755] After the experience, users provide feedback through their device. This feedback consists of the user's impressions and opinions, and the device sends this data to the server. The output is the feedback data sent to the server.

[0756] Step 8:

[0757] The server analyzes the collected feedback and obtains data to improve the quality of story generation. The input is user feedback, and the output is analysis results that will help improve the quality of future stories.

[0758] 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.

[0759] This invention is a system for enhancing the tourism experience, and in particular aims to provide a more personalized tourism experience by combining it with an emotion engine that recognizes the user's emotions. This system aggregates tourism information, generates narratives, provides users with appropriate experiences, and has the function to adjust the content based on user feedback and emotional information.

[0760] First, the server collects a vast amount of information related to tourist destinations. This information is primarily collected from public records and local government documents, and aggregates data specifically focused on the history and culture of the tourist destinations. Natural language processing is then performed on this information, and the resulting data is used to form a narrative storyline.

[0761] Next, in generating the story, the server reflects the collected information and the user's emotional information. Through emotion recognition, the terminal analyzes emotional data from the user's voice and facial expressions, and this data is quantified by the emotion engine. Based on this data, the storytelling is customized to better suit the individual user. For example, if the user is feeling surprised or excited, the story will include more active developments that reflect those emotions, while if they are relaxed, a calmer development will be chosen.

[0762] After the story is generated, the device provides an interactive experience to the viewer through a dedicated application or web platform. Users can visit tourist destinations and learn about the history and culture of those places through the story. This allows visitors not only to visit tourist spots but also to experience the stories behind them with emotion.

[0763] Furthermore, the server uses emotion recognition technology to collect data and collaborates with local communities to plan tourist routes and events. The planned events are narrative-based and may include elements that amplify users' emotions.

[0764] Finally, after the sightseeing experience, users can provide feedback through the application. The device sends this feedback along with emotional data to the server, where the system analyzes and improves. This allows for more appropriate and personalized content in future story generation and sightseeing route construction.

[0765] As a concrete example, when visiting a historical site, the narrative is tailored to enhance the user's excitement by including dramatic episodes related to the construction of the site. At the same time, events are prepared on-site, and photo spots are set up where participants can take pictures dressed in period clothing. Such experiences deeply satisfy the user's emotions and make them feel a sense of familiarity with even unfamiliar tourist destinations.

[0766] Thus, this invention is configured as a system that allows users to deeply learn about the culture and history of tourist destinations and have experiences that reflect their emotions. This enriches the tourist experience and enhances the value of local communities as tourism resources.

[0767] The following describes the processing flow.

[0768] Step 1:

[0769] The server collects historical and cultural information related to tourist destinations from online databases and local government sources. This data includes the historical background, legends, and cultural significance of the tourist attractions.

[0770] Step 2:

[0771] The server performs natural language processing on the collected information. This extracts the key points and themes of the information, and identifies keywords that will serve as the basis for generating a story.

[0772] Step 3:

[0773] The device reads the user's voice and facial expressions using an emotion sensor, and the emotion engine analyzes the emotional data. This process quantifies the user's current emotional state.

[0774] Step 4:

[0775] The server uses a generative AI model to generate stories based on emotional data. The stories are optimized to match the user's emotions, taking into account extracted keywords and emotional data.

[0776] Step 5:

[0777] The server sends the generated story to the terminal and formats it in a user-accessible format. This includes text display, audio guides, and visual aids. The user then uses this to begin their sightseeing experience.

[0778] Step 6:

[0779] Users experience a story delivered through their device. While visiting tourist destinations, they can feel the historical background and narrative elements in real time. Throughout this process, the user's emotional responses are continuously monitored by emotion sensors.

[0780] Step 7:

[0781] The server collaborates with local commercial and tourist facilities to plan story-based tourist routes and events. This allows tourists to participate in local events and activities related to the story.

[0782] Step 8:

[0783] After their sightseeing experience, users submit feedback and comments through the application. The device then sends this data to the server.

[0784] Step 9:

[0785] The server analyzes the collected feedback and sentiment data to improve the story generation process and the content of the tourist routes. This ensures that the next tourist experience will be of even higher quality.

[0786] (Example 2)

[0787] 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".

[0788] Modern tourism experiences are often uniform for some users, lacking a deep understanding of the local culture and history. Furthermore, standardized tourist routes and events fail to adequately reflect the interests and emotions of individual users. As a result, tourists are unable to fully grasp the essence of their destinations, and the tourism resources of local communities are not being utilized to their fullest potential. Therefore, there is a need to provide tourism experiences that are more tailored to individual users and to generate narratives that take into account the emotions of the users.

[0789] 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.

[0790] In this invention, the server includes information acquisition means for collecting tourism-related data, generation means for analyzing the collected data and generating a story, and personalization means for analyzing the user's emotional information and customizing the story. This makes it possible for users to gain a deeper understanding of the local culture and history through their tourism experience and to receive a personalized experience tailored to their individual interests and emotions.

[0791] "Information acquisition methods" refer to the processes and technologies for collecting tourism-related data, including accessing data from public records and local sources.

[0792] "Generative means" refers to software or algorithms used to analyze collected tourism-related data and generate themed narratives.

[0793] "Personalization methods" refer to technologies that analyze users' emotional information and use that data to customize the story content to suit the user's needs.

[0794] "User interface provision means" refers to a mechanism that displays a customized story on a user's device, allowing the user to experience it interactively.

[0795] "Local cooperation methods" refer to the methods and processes for collaborating with local commercial and tourist facilities to plan events related to the generated narrative and to attract tourists.

[0796] "Feedback collection methods" refer to systems and techniques for gathering opinions and feedback from users after their travel experience.

[0797] "Improvement methods" refer to methods and algorithms for analyzing collected feedback and using the results to improve the quality of story generation.

[0798] This system is a complex system for personalizing the tourist experience, and it operates through the collaboration of three entities: servers, terminals, and users. The specific operations of each entity are described below.

[0799] The server plays a central role in collecting and analyzing tourism-related data. Through various data acquisition methods, the server accesses public records and local government databases to collect vast amounts of information about tourist destinations. This includes the history, culture, and current events of the destinations. The collected data is analyzed using NLTK, a Python natural language processing library, to generate narratives that form the basis of the tourism experience. Generative AI models are used to generate these narratives, proposing complex storylines. The generated narratives are then customized based on the user's emotional information.

[0800] The device functions as a tool for analyzing the user's emotions. It is equipped with a camera and microphone to capture the user's voice and facial expressions, and the acquired data is analyzed through software such as OpenCV and DeepFace. This quantifies the user's current emotional state and sends it to a server. Based on this data, the server optimizes the story content for the user.

[0801] Users experience the story through a dedicated application on their device. This application, with its interactive user interface, loads and displays the narrative, enabling a real-time, participatory experience through audio guides and AR technology. Users can experience stories related to the tourist destination they are visiting, gaining a deeper understanding of the local culture and history.

[0802] As a concrete example, when visiting a historical site, the server generates a dramatic story related to that site. Based on the user's emotional information, the story's progression is adjusted, incorporating events that evoke surprise and excitement in the user. Meanwhile, at the site, participatory events such as taking photos in period costumes are guided through the device's navigation system.

[0803] An example of a prompt for the generative AI model is: "Choose a historical adventure the user wants to experience and suggest a new travel experience based on their emotional information. For example, consider the places they want to visit and the points that excite them, and generate a story set in a specific era."

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The server collects tourism-related data. The input consists of tourist destination information from government and local authority databases, including historical, cultural, and event information. The server collects this input using public APIs and web scraping techniques and stores it in a database. The output is tourist destination data stored in a parseable format.

[0807] Step 2:

[0808] The server analyzes the collected data using a natural language processing engine. The input is tourist destination information data collected in step 1, which is then tokenized and categorized using the Python NLTK library. The output of the data analysis is storytelling elements that enable the generative AI model to generate a narrative.

[0809] Step 3:

[0810] The server generates a story using a generative AI model. The input is the storytelling elements obtained in step 2. The server combines these elements to generate prompt sentences, which are then input into the AI ​​model. The AI ​​model generates a story based on the theme and saves it as output.

[0811] Step 4:

[0812] The device analyzes the user's emotional information. Input consists of user voice and facial expression data, captured using a camera and microphone. The device uses libraries such as OpenCV and DeepFace to analyze this input and quantify the emotional data. The output is numerical data representing the user's current emotional state.

[0813] Step 5:

[0814] The server customizes the story based on the user's emotional information. The input consists of the emotional numerical data obtained in step 4 and the story generated in step 3. The server combines this data and adjusts the story content according to the user's emotions. The output is a personalized, customized story.

[0815] Step 6:

[0816] The device provides the user with a customized story. The input is the customized story obtained in step 5. The device provides a real-time interactive experience using voice guidance and AR technology through a dedicated application. The output is the story displayed visually and the user's interaction experience.

[0817] Step 7:

[0818] Users provide feedback after their experience. The input consists of the user's own opinions and impressions, which they enter into the application's feedback form. The device sends this data to the server, where it is stored as analyzable data. The output is the feedback data for analysis.

[0819] (Application Example 2)

[0820] 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".

[0821] Current tourist experiences are generally standardized and not personalized to suit the individual interests and emotions of tourists. As a result, tourist experiences are uniform and it is difficult to create a truly memorable experience. Furthermore, opportunities to learn about the history and culture of tourist destinations are limited, making it difficult to maintain visitors' interest over the long term.

[0822] 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.

[0823] In this invention, the server includes information gathering means for collecting tourist information, generation means for generating a story using natural language processing based on the collected tourist information, and emotion analysis means for analyzing the user's emotions using an emotion recognition engine. This makes it possible to individually adjust the story based on the user's emotions and provide a personalized tourist experience.

[0824] "Information gathering means" refers to a device or function that collects information related to tourist destinations from public records and local government documents.

[0825] A "generative means" is a device or function that applies natural language processing to collected information to form a narrative.

[0826] "User interface provision means" refers to a device or function for providing the generated story to the user via a terminal.

[0827] A "regional collaboration tool" is a device or function that collaborates with local communities to plan tourist routes and events based on generated narratives.

[0828] "Emotional analysis means" refers to a device or function that analyzes a user's voice and facial expressions and quantifies them as emotional data.

[0829] "Personalization means" refers to a device or function that adjusts the development of a story based on data obtained from emotion analysis means, in order to provide an optimal experience for each individual user.

[0830] A "feedback collection method" refers to a device or function that collects opinions and impressions from users after their tourist experience.

[0831] "Improvement measures" refer to devices or functions that analyze collected feedback and use it to improve future story generation.

[0832] To implement this invention, the system is configured as follows: First, the server uses information gathering means to aggregate data related to tourist destinations. This data is collected from public records and local government documents, and comprehensively handles information about the history and culture of the tourist destinations. The server then applies natural language processing technology to the collected information and functions as a generation means for generating narratives. This forms a storyline about the tourist destinations.

[0833] The device is equipped with emotion analysis capabilities, acquiring emotional data by analyzing the user's voice and facial expressions. This emotional data is quantified by an emotion engine, and the story's progression is adjusted by server-side personalization. For example, if the user is excited, a dramatic story is provided, while a calm story is chosen when the user is relaxed. Smart glasses and similar devices are used for emotion analysis.

[0834] Users can experience the story through a user interface, which operates interactively on their device. Throughout the sightseeing flow, users can input opinions and impressions using feedback collection tools. This feedback is analyzed by improvement tools to inform future story generation. For example, if a user participates in a virtual tour of Venice and their emotions are excited, a story related to the carnival will be dynamically suggested.

[0835] An example of a prompt to input into a generative AI model would be: "The user is currently taking a virtual tour of Venice. His emotional state is one of excitement. Please suggest the best storyline for him in this situation."

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] The server collects data related to tourist destinations using information gathering tools. It gathers data based on public records and local government documents and stores it in a database. This data covers a wide range of topics, including the history, culture, and geography of the tourist destination. The input is a specified tourist destination, and the output is a dataset of information related to that destination.

[0839] Step 2:

[0840] The server generates stories by performing natural language processing on the collected data. Specifically, it extracts keywords and performs contextual analysis to create storylines with a theme for each tourist destination. The input is the dataset of tourist destinations obtained in step 1, and the output is text data in the form of storylines.

[0841] Step 3:

[0842] Users visit tourist destinations using their devices and receive and play stories through the user interface. Smart glasses or mobile devices are used, and story data is displayed through these devices. The user's input is the selection of a device, and the output is the story as visual and auditory information.

[0843] Step 4:

[0844] The device uses emotion analysis technology to analyze the user's emotions in real time using their facial expressions and voice. Specifically, it acquires data using a camera and microphone, and then analyzes and quantifies it using an emotion engine. The input for this step is the user's facial expressions and voice data, and the output is numerical data representing the user's emotions.

[0845] Step 5:

[0846] The server uses personalization methods to dynamically adjust the story based on sentiment analysis data. It determines the user's emotional state and generates a story development that is appropriate for it. The input is the sentiment data obtained in step 4, and the output is the adjusted storyline.

[0847] Step 6:

[0848] After their sightseeing experience, users input their opinions and impressions based on their experience using feedback collection tools. This can be done through questionnaires or voice input, and the data is sent to the server. The input is user feedback information, and the output is feedback data.

[0849] Step 7:

[0850] As a means of improvement, the server analyzes the collected feedback data and uses it to improve the story generation algorithm. By analyzing the feedback using data analysis techniques and reflecting the new information in the stories and event planning for tourist destinations, the system aims to improve its accuracy. The input is feedback data, and the output is an improved algorithm or new ideas for the next story generation.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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."

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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 as being incorporated by reference.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] Information gathering methods for collecting tourist information,

[0875] A means of generating a story based on collected tourist information,

[0876] A user interface providing means for providing the generated story to the user terminal,

[0877] A means of regional collaboration that plans tourist routes and events based on the provided stories,

[0878] A feedback collection method for gathering feedback from users after their sightseeing experience,

[0879] The collected feedback is analyzed to improve the quality of story generation, and

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The generation means is a system according to claim 1 that uses natural language processing to extract keywords from tourist information and generate a story based on a theme.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the aforementioned regional collaboration means cooperates with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0885] "Example 1"

[0886] (Claim 1)

[0887] A server means for collecting information,

[0888] A natural language processing method that analyzes collected information and extracts keywords,

[0889] A generative AI model means for generating stories based on keywords,

[0890] Modification and shaping methods for correcting and shaping the generated story,

[0891] An interface providing means for delivering the generated story to the user's terminal,

[0892] A means of regional collaboration to plan tourism projects based on the provided stories,

[0893] A method for collecting feedback from users after their sightseeing experience,

[0894] A means of improving the quality of generated narratives by analyzing collected opinions,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, wherein the generating AI model means automatically generates a story using prompt sentences and checks and corrects grammar and expression.

[0898] (Claim 3)

[0899] The aforementioned regional collaboration means is a system according to claim 1, which, in cooperation with local social infrastructure, plans tourism events related to the generated narrative and attracts tourists.

[0900] "Application Example 1"

[0901] (Claim 1)

[0902] Information gathering methods for collecting tourist information,

[0903] A means of generating a story based on collected tourist information,

[0904] A user interface providing means for providing the generated story to the user terminal,

[0905] A means of regional collaboration that plans routes and events based on the provided stories,

[0906] A feedback collection method for gathering feedback from users after their sightseeing experience,

[0907] The collected feedback is analyzed to improve the quality of story generation, and

[0908] A location-finding means that identifies the user's current location and provides a narrative visually and audibly,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The generation means is a system according to claim 1 that uses natural language processing to extract a theme from tourist information and generates a story based on that theme.

[0912] (Claim 3)

[0913] The aforementioned regional collaboration means is the system according to claim 1, which cooperates with local commercial facilities and tourist facilities to hold events related to the generated story, guides tourists, and allows them to experience the story using visual devices.

[0914] "Example 2 of combining an emotion engine"

[0915] (Claim 1)

[0916] Information acquisition methods for collecting tourism-related data,

[0917] A means of generating narratives by analyzing collected tourism-related data,

[0918] Personalization methods that analyze user emotional information and customize the story,

[0919] A means for providing a user interface that displays a customized story on the user's device,

[0920] A means of regional cooperation that sets up tourist routes and events based on the provided stories,

[0921] A method for collecting feedback from users after their sightseeing experience,

[0922] The collected opinions are analyzed, and improvements are made to enhance the quality of story generation.

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the generation means extracts thematic elements from tourism-related data using natural language processing and generates a story based on the theme.

[0926] (Claim 3)

[0927] The aforementioned means of regional cooperation is the system according to claim 1, which collaborates with local commercial and tourist facilities to hold events related to the generated narrative and attract tourists.

[0928] "Application example 2 when combining with an emotional engine"

[0929] (Claim 1)

[0930] Information gathering methods for collecting tourist information,

[0931] A generation method that generates stories using natural language processing based on collected tourist information,

[0932] A user interface providing means for providing the generated story to the user terminal,

[0933] A means of regional collaboration that plans tourist routes and events based on the provided stories,

[0934] An emotion analysis method that analyzes the user's emotions using an emotion recognition engine,

[0935] Personalization means that adjusts the story based on analyzed emotional data and provides a story development that is suitable for each individual,

[0936] A feedback collection method for gathering feedback from users after their sightseeing experience,

[0937] The collected feedback is analyzed to improve the quality of story generation, and

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, wherein the generation means extracts keywords from tourist information using natural language processing, generates a story based on a theme, and dynamically adjusts the individual story using data from the sentiment analysis means.

[0941] (Claim 3)

[0942] The system according to claim 1, wherein the regional collaboration means cooperates with local facilities and tourist facilities to hold events related to the generated narrative and plans individual events that will attract the interest of tourists based on sentiment analysis data. [Explanation of Symbols]

[0943] 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. Information gathering methods for collecting tourist information, A means of generating a story based on collected tourist information, A user interface providing means for providing the generated story to the user terminal, A means of regional collaboration that plans tourist routes and events based on the provided stories, A feedback collection method for gathering feedback from users after their sightseeing experience, The collected feedback is analyzed to improve the quality of story generation, and A system that includes this.

2. The system according to claim 1, wherein the generation means extracts keywords from tourist information using natural language processing and generates a story based on a theme.

3. The system according to claim 1, wherein the aforementioned regional collaboration means cooperates with local commercial facilities and tourist facilities to hold events related to the generated narrative and attract tourists.

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