system

Abstract

We provide the system. [Solution] A means for receiving information on a user's planned destination and collecting data related to that planned destination, A means for automatically generating media content based on the aforementioned data, A method for predicting the conditions at a planned visit location and calculating the optimal visit time, Means for providing the generated media content and predictive information to the user's terminal, A means of presenting local information using a virtual guidance system, 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, the method including: 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 as a 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] A problem is that many users visiting tourist destinations cannot deeply understand the historical background and cultural significance at the destination, and end up with just a sightseeing experience. In addition, travel plans are often hindered by overcrowding at the destination, and useful information needs to be provided in advance to avoid this.

Means for Solving the Problems

[0005] The present invention solves this problem by collecting historical data based on information about the user's planned visit destination and automatically generating this as media content by technology. Furthermore, by predicting the congestion of the planned visit destination and calculating the optimal visit time, the efficiency of the travel plan is improved. As a result, it is possible to provide relevant information about the visit destination to the user and support for enjoying sightseeing comfortably.

[0006] "User" refers to an individual or group that uses this system to obtain information about tourist destinations.

[0007] "Planned destination" refers to the specific tourist destination or region that the user intends to visit based on their travel plan.

[0008] "Historical data" refers to data that includes past events, culture, history, and other information related to the planned destination.

[0009] "Media content" refers to information provided in formats such as text, images, audio, and video.

[0010] "Crowd forecast" refers to information that predicts the level of crowding at a specific tourist destination and provides it to users in advance.

[0011] A "communication terminal" is a device used by users to receive and view information, and includes smartphones, tablets, and other similar devices. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

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

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0018] 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.

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] This invention provides a system that enables tourists to enjoy a deeper understanding of their planned destinations and a more efficient travel experience. This system operates through a network, with a server, user communication terminals, and users working together.

[0034] Users access a travel platform application using a communication terminal. Here, they input their desired tourist destinations and itinerary, and send this information to the server. The server then accesses a database based on the received information about the planned destinations to collect relevant historical data. Furthermore, using generative AI technology, this data is automatically generated as media content in the form of text, images, and audio.

[0035] Furthermore, the server analyzes the predicted congestion level using a congestion prediction algorithm for the planned destination. It then transmits media content and congestion prediction information based on this historical data to the user's communication terminal. The user's communication terminal displays this information appropriately, providing the user with historical background and cultural significance of the tourist destination, and offering advice, including the optimal time to visit.

[0036] As a concrete example, when a user registers a plan to visit a historical site in a certain city, the server generates and provides content explaining important historical events and the history of the building related to that site. At the same time, it predicts the peak tourist times for that site and suggests alternative visiting times to avoid them. In this way, users can gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0037] This system allows tourists to not only have a richer travel experience but also to plan their trips efficiently while avoiding crowds at tourist destinations.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user accesses the travel platform application using a communication terminal. The user enters the tourist destinations and dates they plan to visit and sends this information to the server.

[0041] Step 2:

[0042] The server uses the information about the planned destination received from the user to query its internal database and collect the relevant historical data.

[0043] Step 3:

[0044] The server processes the collected historical data using AI technology to automatically generate media content in the form of text, images, and audio.

[0045] Step 4:

[0046] The server runs a congestion prediction algorithm for the planned visit location and analyzes the predicted congestion level on the planned visit date.

[0047] Step 5:

[0048] The server sends the generated media content and congestion prediction information to the user's communication terminal.

[0049] Step 6:

[0050] The device displays the received information on its screen, presenting the user with information about the history and cultural significance of the tourist destination, as well as recommendations for the optimal time to visit.

[0051] Step 7:

[0052] Based on the information provided, users can adjust their travel plans and enjoy an efficient and meaningful travel experience.

[0053] (Example 1)

[0054] 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."

[0055] A challenge exists in that visitors planning to tour tourist destinations often lack sufficient prior information, preventing them from understanding the background and cultural significance of the sites, and from predicting crowd levels during their visits, thus hindering them from enjoying an effective and efficient travel experience. Furthermore, it is often difficult to determine the appropriate time to visit individual tourist destinations, making it challenging to optimize the length of stay.

[0056] 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.

[0057] In this invention, the server includes means for receiving information on the user's planned destinations via a communication device and collecting historical information related to those destinations from a storage device; means for automatically generating information media such as text, still images, and audio data based on prompt instructions using generation AI technology based on the historical information; and means for applying an algorithm to analyze the congestion status of the planned destinations and evaluating the optimal visit time. This enables users to enjoy a comfortable and efficient travel experience by providing a deep understanding of tourist destinations and enabling visit plans that avoid congestion.

[0058] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate various media content such as text, images, and audio based on input provided by users.

[0059] A "prompt instruction" is a guide or instruction provided to a generative AI technology to obtain the desired output.

[0060] "Information media" refers to digital content such as text, still images, and audio data, which are used to provide information to users.

[0061] "Communication equipment" refers to a part of the hardware or software used to receive user input data and to send and receive data with a server.

[0062] A "storage device" refers to a storage system, such as a database, that stores historical information and data related to tourist destinations and makes them accessible as needed.

[0063] A "congestion analysis algorithm" is a mathematical method used to predict future congestion levels and evaluate optimal visiting times based on data such as the number of visitors and time of day at tourist destinations.

[0064] A "communication terminal" is an electronic device used by users to input information and receive and display media content and congestion information sent from a server.

[0065] This invention relates to a system that enables tourists to gain a deep understanding of detailed information about their destinations and to travel efficiently. The system consists of a server, a communication terminal, and a user. The server plays a central role in data processing, and the communication terminal functions as the user interface.

[0066] Users access the travel platform application using a communication device. Here, they enter the names of the tourist destinations they wish to visit and their travel dates. The communication device then transmits the entered information to the server. Specifically, portable electronic devices such as smartphones and tablets are often used as the device. The communication device and the server exchange data via the internet.

[0067] The server collects relevant historical information from a database based on the received information. This database stores various historical information related to tourist destinations. The collected data is processed using a generation AI model, such as GPT-3®, based on prompts, and the corresponding text, image, and audio data is generated. An example of a prompt used in this process is, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The generated media content provides specific information to support a deeper understanding of tourist destinations.

[0068] Furthermore, the server applies its own congestion analysis algorithm to predict congestion levels at the planned destination and calculate the optimal visit time. This information is intended to enable users to have a less stressful visit.

[0069] The generated media content and congestion forecast information are transmitted to the communication terminal. The user's communication terminal receives this information and displays it appropriately through the user interface. For example, it may show historical descriptions of tourist spots or suggested visiting times to avoid peak hours. This allows users to gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user accesses the travel platform application using a communication terminal and enters the name of the tourist destination they wish to visit and the planned date of visit. This input information is sent to the server by the communication terminal. The input consists of the planned destination and date, and an information packet is generated that is sent to the server as output. The communication terminal accurately receives the user's input and packages it into a packet format so that it can be appropriately transmitted to the server.

[0073] Step 2:

[0074] The server receives information about the planned destination from the communication terminal. Based on the received data, it accesses the database, which is the storage device, and collects the corresponding historical information. The input is the data of the planned destination received from the user, and the output is extracted historical data. The server queries the database and aggregates all historical data related to the specified tourist destination.

[0075] Step 3:

[0076] The server inputs collected historical information into a generating AI model, which then automatically generates media content based on prompts. For example, it might use the prompt, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The input consists of collected historical data and prompt instructions, while the output consists of generated text, images, and audio data. The server operates the AI ​​model to generate the desired output according to the instructions.

[0077] Step 4:

[0078] The server uses a congestion analysis algorithm to predict congestion levels at a planned visit location and calculate the optimal visit time. Inputs include past visitor numbers and related data, while outputs are predicted congestion times and optimal visit times. The server processes this data using an algorithm to calculate time periods that users should avoid.

[0079] Step 5:

[0080] The generated media content and congestion prediction information are sent from the server to the communication terminal. The input is the generated media content and prediction information, and the output is delivered to the user's communication terminal. The server formats the information in an appropriate format and sends it efficiently to the communication terminal.

[0081] Step 6:

[0082] The communication terminal receives information transmitted from the server and displays it through the user interface. The user adjusts their sightseeing plan based on this information. Input consists of media content and predictive information from the server, while output is the displayed information received by the user. The communication terminal automatically adjusts the display method to provide the information to the user in an easily understandable manner.

[0083] (Application Example 1)

[0084] 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."

[0085] To enhance the tourism experience, it is necessary to deepen cultural and historical understanding of the destination and to develop efficient travel plans that avoid congestion. However, traditional travel plans rely on individual information gathering and analysis, which is time-consuming and laborious for travelers. Furthermore, acquiring and purchasing local specialties at destinations has not provided a smooth experience.

[0086] 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.

[0087] In this invention, the server includes means for receiving information on the user's planned destination and collecting related data, means for automatically generating media content based on the data, and means for presenting regional information using virtual guidance. This allows the user to deepen their understanding of the history and culture of their planned destination, receive suggestions for the optimal time to visit to avoid crowds, and realize a smooth experience of trading local specialty products in a virtual environment.

[0088] "Planned destination" refers to a place that the user plans to visit for travel or sightseeing purposes.

[0089] "Data" refers to a collection of information that includes historical, cultural, and historical content related to the planned destination.

[0090] "Media content" refers to information expressed in forms such as text, images, and audio, which is provided to users visually or audibly.

[0091] "Predictive information" refers to data that shows the expected congestion level and optimal time to visit a planned destination.

[0092] A "terminal" is a type of electronic device used by users to receive and display information.

[0093] "Virtual guidance" refers to a means of extending and supplementing information about real-world destinations using digital technology.

[0094] The system that implements this application is designed to provide tourist information, and the server plays a crucial role. The server receives information about the user's planned destinations and collects relevant data based on that information. This data includes historical and cultural information about the destinations and is automatically generated as media content such as text, images, and audio using a generative AI model.

[0095] Furthermore, the server utilizes a congestion prediction algorithm for the planned destination to calculate the optimal time to visit. This prediction information is sent to the user's device, allowing the user to plan an efficient trip based on it. The user's device is a smartphone or smart glasses, equipped with software to visually display the information.

[0096] Furthermore, it is possible to use virtual guidance tools to present local information on devices. For example, users can listen to audio guides about local historical sites. Presenting local specialties and recommended travel routes can also enhance the sightseeing experience.

[0097] For example, if a user plans to visit a historical landmark, the server generates content based on extensive historical data about that landmark and provides it to the user in audio or visual format. An example of a prompt used in this case might be, "Generate detailed content based on information about the historical landmark and crowd predictions."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The user enters information about their planned destination using a device.

[0101] The information entered includes the name of the tourist destination and the itinerary of the visit, and this information is sent to the server. The server receives this information and uses it as the basis for collecting data on the planned destination.

[0102] Step 2:

[0103] The server accesses a database based on the received information about the planned destination and collects relevant historical and cultural data.

[0104] Using this input data, the server collects historical data and related information, and obtains refined information as output. The results of the data collection are necessary for generating the next media content.

[0105] Step 3:

[0106] The server uses a generative AI model to automatically generate media content such as text, images, and audio based on the collected data.

[0107] The input here is collected data, and the output is media content in a format that can be provided to the user. In this generation process, the generation AI model utilizes prompts to construct detailed content.

[0108] Step 4:

[0109] The server uses a congestion prediction algorithm for the planned visit location to analyze congestion levels and calculate the optimal visit time.

[0110] The input consists of historical data and pre-provided congestion data, while the output is congestion prediction information, such as times when it is best to avoid visiting. This calculation is an essential step in maximizing the user experience.

[0111] Step 5:

[0112] The generated media content and congestion forecast information are sent to the user's device and displayed appropriately on the device.

[0113] The input from the server is media and predictive information, while the output from the terminal is visual or auditory information provided to the user. This allows the user to obtain information and timing for the optimal destination.

[0114] Step 6:

[0115] It provides real-time tourist information on a terminal using a virtual guidance system.

[0116] The input consists of generated content and real-time guidance information, while the output is an interactive experience for the user. Users can experience digitally enhanced tourist information using their devices.

[0117] 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.

[0118] This invention provides a system that enhances tourists' experiences at their destinations and enables the provision of information tailored to their individual emotions. This system operates by combining a server, a user's communication terminal, and an emotion engine.

[0119] First, the user enters their planned destination into the travel platform application via a communication terminal. The server receives this information and accesses a database to collect historical data on the planned destination. Based on this, it utilizes generative AI technology to generate relevant text, images, and audio media content. Furthermore, the server predicts congestion at the planned destination and calculates the optimal time to visit.

[0120] In addition, an emotion engine installed in the user's communication device analyzes the user's emotions in real time. The results of the emotion engine's analysis influence the content selection provided by the server. Specifically, the emotion engine reads the user's emotions from their facial expressions and voice, and adjusts the information provided accordingly. For example, if the user is feeling stressed, the server can suggest relaxing tourist spots or activities.

[0121] For example, if the emotion engine detects that a user is experiencing stress while visiting a busy urban area, the server will provide information recommending that the user visit a nearby quiet park or relaxation facility at an appropriate time. In this way, the emotion engine helps to improve the user's experience in accordance with their emotional state.

[0122] Embodiments of the present invention enable tourism experiences to go beyond simply providing visual information and offer personalized services that resonate with the user's emotions. This allows users to enjoy a more satisfying travel experience.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] Users access the travel platform application via a communication device and enter their planned tourist destinations and dates. Users can also fill in their purpose of stay and preferences as needed.

[0126] Step 2:

[0127] The server searches its internal database based on information received from the user to collect historical data related to the planned visit location. The server then uses AI technology to automatically generate media content in text, image, and audio formats from this data.

[0128] Step 3:

[0129] The server runs a congestion prediction algorithm for the planned visit location, analyzes the congestion situation on the planned visit date, and calculates the optimal visit time.

[0130] Step 4:

[0131] An emotion engine built into the communication terminal uses the camera and microphone to analyze the user's emotions in real time from their facial expressions and voice. The emotion engine determines the user's current emotional state.

[0132] Step 5:

[0133] The server considers the results of the emotion engine analysis and selects the media content most suitable for the user. For example, if it determines that relaxation is needed, the server will select content with relaxation effects.

[0134] Step 6:

[0135] The server transmits selected media content and congestion prediction information to the user's communication terminal. This allows the user to receive information that corresponds to their emotional state.

[0136] Step 7:

[0137] The device displays received content and provides users with historical information and cultural significance of tourist destinations, optimal times to visit, and suggestions for places and activities that match their mood.

[0138] Step 8:

[0139] Based on this information, users can adjust their visit plans and enjoy a sightseeing experience tailored to their individual needs. User feedback is later collected as data in the system and used to improve the system's accuracy.

[0140] (Example 2)

[0141] 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".

[0142] Current travel guide services provide general information about destinations, but they do not adequately address the individual needs, circumstances, and especially emotional states of users. Furthermore, optimizing travel plans to account for crowd levels is difficult. There is a need to solve these problems and provide users with personalized experiences.

[0143] 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.

[0144] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio using natural language processing technology; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; and means for analyzing the user's emotions in real time and adjusting the information provided based on the analysis results. This makes it possible to provide users with personalized information tailored to their emotions and propose optimal visit plans that avoid congestion.

[0145] "Planned destination" refers to a place or region that the user plans to visit for the purpose of travel or sightseeing.

[0146] A "communication device" is a device that sends and receives data over the internet, enabling the exchange of information between a user and a server.

[0147] "Historical data" refers to information accumulated based on past events and actions, including historical, cultural, and geographical data related to the planned destination.

[0148] "Natural language processing technology" is a technology that uses computers to understand, generate, and manipulate natural human language, and is used for analyzing and generating text data.

[0149] "Media content" refers to information provided in forms such as text, images, and audio, and is information that has visual or auditory value.

[0150] "Crowding" refers to the degree of congestion or density of people in a particular place or space.

[0151] "Optimal visiting time" refers to the time or period of time when a user visits a planned destination to avoid crowds and to sightsee effectively and efficiently.

[0152] "Emotion" refers to the psychological response that a user shows to a specific situation or stimulus, and includes states such as stress, joy, and excitement.

[0153] "Analysis" refers to the process of thoroughly examining data and information to understand its meaning and patterns.

[0154] "Adjusting information" refers to optimizing the information provided according to the user's situation and needs, and modifying its content as necessary.

[0155] This invention is a system for providing users with an optimal tourism experience, operating in combination with a server, user communication devices, and an engine for analyzing emotions in real time. Its main components include data reception, content generation, congestion prediction, emotion analysis, and information provision functions.

[0156] The server receives information about planned destinations entered by the user using a communication device. Hardware used here includes internet-connected smartphones and tablets. The server is connected to a database containing historical data related to the planned destinations, and uses this database to collect relevant data. Based on the collected data, a generative AI model automatically generates media content such as text, images, and audio. Natural language processing techniques are used in the generative AI model.

[0157] Next, the server analyzes historical data and real-time information to predict congestion at the planned visit location and calculate the optimal visit time. AI-based analysis technology is applied at this stage. Furthermore, an emotion engine built into the user's communication device uses a camera and microphone to analyze the user's facial expressions and voice, determining their emotional state in real time. The analysis results are sent to the server, influencing the selection of information provided.

[0158] For example, if a user enters "I plan to go to Tokyo Skytree" into a tourism application, the server will create an optimal visit plan based on tourist information around Skytree and past congestion data. Furthermore, if the user indicates using a communication device that they want to relax, the server will suggest places such as quiet cafes or parks. In this case, prompts to the generating AI model would be in the form of "Please provide information about the area around Tokyo Skytree" or "Please suggest places where I can relax."

[0159] Thus, according to the embodiments of the invention, users can obtain personalized tourist information and utilize optimal travel plans tailored to their emotions and circumstances.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] Users access the travel platform using a communication terminal and enter their planned destinations. The entered data is sent from the terminal to the server. Users are required to enter specific place names and desired locations in text format. The terminal's output is the request data sent to the server.

[0163] Step 2:

[0164] The server collects relevant historical data from a database based on the received data on the planned destination. The database contains data on past congestion levels, tourist attractions, and cultural and historical information for the destination. The server filters and aggregates this data to form a dataset for input into the generative AI model. The server's output is the prepared data for use by the generative AI model.

[0165] Step 3:

[0166] The server inputs collected historical data into a generation AI model, which automatically generates media content such as text, images, and audio. The generation AI model utilizes natural language processing techniques to output useful and relevant content for the user. The prompt used is "Generate tourist information about the planned destination." The server's output is the generated media content.

[0167] Step 4:

[0168] The server applies congestion prediction technology to forecast future congestion levels at the planned destination. By analyzing past congestion data and current conditions in real time, it models congestion patterns for the destination. Based on this information, the server calculates the optimal visit time and creates a suggested visit schedule for the user. The server's output is a schedule that includes the recommended visit time.

[0169] Step 5:

[0170] The emotion engine installed in the user's device activates, capturing the user's facial expressions with the camera and collecting audio with the microphone. The device analyzes this data in real time to infer the user's emotional state. For example, it analyzes whether the user is feeling stressed or expressing a desire to relax. This result is sent to the server and used to inform the next content selection. The output of the device is the user's emotional state data.

[0171] Step 6:

[0172] The server adjusts the media content and tourist information it provides based on emotional data transmitted from the emotion engine. It offers information in a way that resonates with the user's emotions, such as suggesting quiet locations if the user wants to relax. The server then constructs the final information package and sends it to the user's communication terminal. The server's output is customized tourist information adapted to the user's emotions.

[0173] (Application Example 2)

[0174] 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".

[0175] In modern tourism experiences, it is difficult to provide not just information, but also a personalized experience tailored to the user's emotions and circumstances. Tourists are easily affected by time and crowd conditions, and may experience stress and dissatisfaction, requiring effective methods to address these issues. Therefore, to improve the quality of tourism, there is a need to realize a system that can provide appropriate information and plans that respond to the user's emotions.

[0176] 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.

[0177] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio based on the historical data; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; means for incorporating an engine for analyzing the user's emotional state and adjusting the media content based on the analysis results; and means for providing the generated media content and congestion prediction information to the user's communication device. This makes it possible to provide sightseeing plans tailored to the user's emotions and a comfortable experience by avoiding congestion.

[0178] "Information about planned destinations" refers to detailed information about places the user plans to visit, including the names of tourist attractions and the planned duration of stay.

[0179] "Historical data" refers to past records and information about the planned destination, including reviews from past visitors and weather information.

[0180] "Media content" refers to content in the form of text, images, audio, etc., that provides information about tourist destinations visually or audibly.

[0181] "Congestion forecasting" is a method for predicting the number of people and the level of crowding at a planned destination, and uses this information to determine the optimal time to visit.

[0182] The "emotional state analysis engine" is a function that analyzes the user's emotions through their facial expressions and voice, and adjusts content and suggestions based on that information.

[0183] A "communication device" refers to a device owned by the user, such as a smartphone or tablet, through which they can receive tourist information.

[0184] The system that realizes this invention consists of a server, a user's communication terminal, and an engine that analyzes emotional states. The server receives information about the user's planned visit locations and collects related historical data. Based on this historical data, it automatically generates media content such as text, images, and audio using an open-source generative AI model. Furthermore, the server predicts the congestion level of the planned visit locations and calculates an appropriate visit time.

[0185] The user's communication device is equipped with an emotion engine for real-time analysis of their emotional state. For example, it uses the Microsoft® Azure® Emotion API to analyze emotions through the user's facial expressions and voice. The results of this emotion analysis are used to adjust media content generated on the server to match the user's emotions.

[0186] Furthermore, the communication terminal receives and provides users with optimized media content and congestion forecast information. Specifically, when a user inputs information about their planned destination, the server generates content based on that information and makes optimal suggestions according to the results of sentiment analysis. For example, for a user experiencing stress, it might suggest visiting a quiet tourist spot or a relaxation facility.

[0187] For example, if a user experiences stress while sightseeing in Kyoto and is on their way to a crowded tourist spot, the emotion engine will detect this, and the server will suggest nearby quiet parks or temples. This kind of information provides the user with the best possible sightseeing experience.

[0188] By combining generative AI models with an emotion engine, it's possible to provide personalized travel experiences tailored to the user's emotions. An example of a prompt might be: "A traveler is feeling stressed while visiting a famous autumn foliage spot in Kyoto. Read their current emotions and suggest quieter tourist spots to avoid crowds."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server receives information about the planned destination from the user's communication terminal. The input is tourist destination information selected by the user, and the output is a query to a database based on that information. As part of data processing, the server collects historical data related to the planned destination.

[0192] Step 2:

[0193] The server automatically generates media content such as text, images, and audio using a generative AI model based on collected historical data. The input is historical data, and the output is the generated multimedia content. In this process, as a data computation, the information is combined by generative AI technology and transformed into a form that can be presented visually or aurally.

[0194] Step 3:

[0195] The server predicts congestion at a planned destination based on collected data and real-time traffic information, and calculates the optimal visit time. Inputs are historical data and current traffic information, and output is congestion prediction information. The appropriate visit time is calculated based on the congestion data predicted by the algorithm.

[0196] Step 4:

[0197] An emotion engine installed in the user's communication terminal analyzes the user's emotional state from their facial expressions and voice. The input is real-time data from the user via camera and microphone, and the output is emotional state data. An emotion recognition algorithm is used to process the data and quantify the emotion.

[0198] Step 5:

[0199] The server receives analysis results from the emotion engine and adjusts the media content it provides based on those results. The input is emotional state data, and the output is the adjusted media content. The server customizes the information to match the user's current emotions and enhances information about places where they can relax.

[0200] Step 6:

[0201] The server provides the user's communication terminal with adjusted media content and congestion forecast information. The input is adjusted content and congestion information, and the output is presented on the user interface. The user can use this information to make visits that align with their mood and travel plans.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention provides a system that enables tourists to enjoy a deeper understanding of their planned destinations and a more efficient travel experience. This system operates through a network, with a server, user communication terminals, and users working together.

[0219] Users access a travel platform application using a communication terminal. Here, they input their desired tourist destinations and itinerary, and send this information to the server. The server then accesses a database based on the received information about the planned destinations to collect relevant historical data. Furthermore, using generative AI technology, this data is automatically generated as media content in the form of text, images, and audio.

[0220] Furthermore, the server analyzes the predicted congestion level using a congestion prediction algorithm for the planned destination. It then transmits media content and congestion prediction information based on this historical data to the user's communication terminal. The user's communication terminal displays this information appropriately, providing the user with historical background and cultural significance of the tourist destination, and offering advice, including the optimal time to visit.

[0221] As a concrete example, when a user registers a plan to visit a historical site in a certain city, the server generates and provides content explaining important historical events and the history of the building related to that site. At the same time, it predicts the peak tourist times for that site and suggests alternative visiting times to avoid them. In this way, users can gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0222] This system allows tourists to not only have a richer travel experience but also to plan their trips efficiently while avoiding crowds at tourist destinations.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The user accesses the travel platform application using a communication terminal. The user enters the tourist destinations and dates they plan to visit and sends this information to the server.

[0226] Step 2:

[0227] The server uses the information about the planned destination received from the user to query its internal database and collect the relevant historical data.

[0228] Step 3:

[0229] The server processes the collected historical data using AI technology to automatically generate media content in the form of text, images, and audio.

[0230] Step 4:

[0231] The server runs a congestion prediction algorithm for the planned visit location and analyzes the predicted congestion level on the planned visit date.

[0232] Step 5:

[0233] The server sends the generated media content and congestion prediction information to the user's communication terminal.

[0234] Step 6:

[0235] The device displays the received information on its screen, presenting the user with information about the history and cultural significance of the tourist destination, as well as recommendations for the optimal time to visit.

[0236] Step 7:

[0237] Based on the information provided, users can adjust their travel plans and enjoy an efficient and meaningful travel experience.

[0238] (Example 1)

[0239] 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."

[0240] A challenge exists in that visitors planning to tour tourist destinations often lack sufficient prior information, preventing them from understanding the background and cultural significance of the sites, and from predicting crowd levels during their visits, thus hindering them from enjoying an effective and efficient travel experience. Furthermore, it is often difficult to determine the appropriate time to visit individual tourist destinations, making it challenging to optimize the length of stay.

[0241] 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.

[0242] In this invention, the server includes means for receiving information on the user's planned destinations via a communication device and collecting historical information related to those destinations from a storage device; means for automatically generating information media such as text, still images, and audio data based on prompt instructions using generation AI technology based on the historical information; and means for applying an algorithm to analyze the congestion status of the planned destinations and evaluating the optimal visit time. This enables users to enjoy a comfortable and efficient travel experience by providing a deep understanding of tourist destinations and enabling visit plans that avoid congestion.

[0243] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate various media content such as text, images, and audio based on input provided by users.

[0244] A "prompt instruction" is a guide or instruction provided to a generative AI technology to obtain the desired output.

[0245] "Information media" refers to digital content such as text, still images, and audio data, which are used to provide information to users.

[0246] "Communication equipment" refers to a part of the hardware or software used to receive user input data and to send and receive data with a server.

[0247] A "storage device" refers to a storage system, such as a database, that stores historical information and data related to tourist destinations and makes them accessible as needed.

[0248] A "congestion analysis algorithm" is a mathematical method used to predict future congestion levels and evaluate optimal visiting times based on data such as the number of visitors and time of day at tourist destinations.

[0249] A "communication terminal" is an electronic device used by users to input information and receive and display media content and congestion information sent from a server.

[0250] This invention relates to a system that enables tourists to gain a deep understanding of detailed information about their destinations and to travel efficiently. The system consists of a server, a communication terminal, and a user. The server plays a central role in data processing, and the communication terminal functions as the user interface.

[0251] Users access the travel platform application using a communication device. Here, they enter the names of the tourist destinations they wish to visit and their travel dates. The communication device then transmits the entered information to the server. Specifically, portable electronic devices such as smartphones and tablets are often used as the device. The communication device and the server exchange data via the internet.

[0252] The server collects relevant historical information from a database based on the received information. This database stores various historical information related to tourist destinations. The collected data is processed using a generation AI model, such as GPT-3, based on prompts, and the corresponding text, image, and audio data is generated. An example of a prompt used in this process is, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The generated media content provides specific information to support a deeper understanding of tourist destinations.

[0253] Furthermore, the server applies its own congestion analysis algorithm to predict congestion levels at the planned destination and calculate the optimal visit time. This information is intended to enable users to have a less stressful visit.

[0254] The generated media content and congestion forecast information are transmitted to the communication terminal. The user's communication terminal receives this information and displays it appropriately through the user interface. For example, it may show historical descriptions of tourist spots or suggested visiting times to avoid peak hours. This allows users to gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The user accesses the travel platform application using a communication terminal and enters the name of the tourist destination they wish to visit and the planned date of visit. This input information is sent to the server by the communication terminal. The input consists of the planned destination and date, and an information packet is generated that is sent to the server as output. The communication terminal accurately receives the user's input and packages it into a packet format so that it can be appropriately transmitted to the server.

[0258] Step 2:

[0259] The server receives information about the planned destination from the communication terminal. Based on the received data, it accesses the database, which is the storage device, and collects the corresponding historical information. The input is the data of the planned destination received from the user, and the output is extracted historical data. The server queries the database and aggregates all historical data related to the specified tourist destination.

[0260] Step 3:

[0261] The server inputs collected historical information into a generating AI model, which then automatically generates media content based on prompts. For example, it might use the prompt, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The input consists of collected historical data and prompt instructions, while the output consists of generated text, images, and audio data. The server operates the AI ​​model to generate the desired output according to the instructions.

[0262] Step 4:

[0263] The server uses a congestion analysis algorithm to predict congestion levels at a planned visit location and calculate the optimal visit time. Inputs include past visitor numbers and related data, while outputs are predicted congestion times and optimal visit times. The server processes this data using an algorithm to calculate time periods that users should avoid.

[0264] Step 5:

[0265] The generated media content and congestion prediction information are sent from the server to the communication terminal. The input is the generated media content and prediction information, and the output is delivered to the user's communication terminal. The server formats the information in an appropriate format and sends it efficiently to the communication terminal.

[0266] Step 6:

[0267] The communication terminal receives information transmitted from the server and displays it through the user interface. The user adjusts their sightseeing plan based on this information. Input consists of media content and predictive information from the server, while output is the displayed information received by the user. The communication terminal automatically adjusts the display method to provide the information to the user in an easily understandable manner.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] To enhance the tourism experience, it is necessary to deepen cultural and historical understanding of the destination and to develop efficient travel plans that avoid congestion. However, traditional travel plans rely on individual information gathering and analysis, which is time-consuming and laborious for travelers. Furthermore, acquiring and purchasing local specialties at destinations has not provided a smooth experience.

[0271] 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.

[0272] In this invention, the server includes means for receiving information on the user's planned destination and collecting related data, means for automatically generating media content based on the data, and means for presenting regional information using virtual guidance. This allows the user to deepen their understanding of the history and culture of their planned destination, receive suggestions for the optimal time to visit to avoid crowds, and realize a smooth experience of trading local specialty products in a virtual environment.

[0273] "Planned destination" refers to a place that the user plans to visit for travel or sightseeing purposes.

[0274] "Data" refers to a collection of information that includes historical, cultural, and historical content related to the planned destination.

[0275] "Media content" refers to information expressed in forms such as text, images, and audio, which is provided to users visually or audibly.

[0276] "Predictive information" refers to data that shows the expected congestion level and optimal time to visit a planned destination.

[0277] A "terminal" is a type of electronic device used by users to receive and display information.

[0278] "Virtual guidance" refers to a means of expanding and complementing information on actual visited places using digital technology.

[0279] The system for realizing the application example is a system aimed at providing tourism information, and the server plays an important role. The server receives information on the user's planned visit destination and collects relevant data based on it. This data includes historical and cultural information on the planned visit destination and is automatically generated as media content such as text, images, and audio using a generative AI model.

[0280] Furthermore, the server utilizes a congestion prediction algorithm for the planned visit destination to calculate the optimal visit time. This prediction information is transmitted to the user's terminal, and the user can make an efficient travel plan based on this. The user's terminal is a device such as a smartphone or smart glasses and is equipped with software for visually displaying information.

[0281] Also, it is possible to present regional information on the device using virtual guidance means. For example, an audio guide about local historical landmarks can be listened to. Also, by presenting local specialties and recommended travel routes, a more fulfilling tourism experience is provided.

[0282] As a specific example, when a user plans to visit a historical landmark, the server generates content based on the rich historical data related to that landmark and provides it to the user in audio or visual form. An example of the prompt text used in this case is something like "Please generate detailed content based on information about the historical landmark and congestion prediction."

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The user inputs information about the destination to be visited using the terminal.

[0286] The information entered is such as the name of the tourist destination and the visit schedule, and this is sent to the server. The server receives this information and uses it as the basis for data collection regarding the destination to be visited.

[0287] Step 2:

[0288] Based on the received information about the destination to be visited, the server accesses the database and collects relevant historical and cultural data.

[0289] Using this input data, the server collects historical data and related information and obtains refined information as output. The results of the data collection are necessary for the generation of the following media content.

[0290] Step 3:

[0291] The server uses a generation AI model to automatically generate media content such as text, images, and audio based on the collected data.

[0292] The input here is the collected data, and the output is media content in a form that can be provided to the user. In this generation process, the generation AI model utilizes prompt sentences to construct detailed content.

[0293] Step 4:

[0294] The server uses a congestion prediction algorithm for the destination to be visited to analyze the congestion situation and calculate the optimal visit time.

[0295] The input is historical and pre-provided congestion data, and the output is congestion prediction information such as time periods when it is better to avoid visiting. This calculation is an essential step for maximizing the user experience.

[0296] Step 5:

[0297] The generated media content and congestion forecast information are sent to the user's device and displayed appropriately on the device.

[0298] The input from the server is media and predictive information, while the output from the terminal is visual or auditory information provided to the user. This allows the user to obtain information and timing for the optimal destination.

[0299] Step 6:

[0300] It provides real-time tourist information on a terminal using a virtual guidance system.

[0301] The input consists of generated content and real-time guidance information, while the output is an interactive experience for the user. Users can experience digitally enhanced tourist information using their devices.

[0302] 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.

[0303] This invention provides a system that enhances tourists' experiences at their destinations and enables the provision of information tailored to their individual emotions. This system operates by combining a server, a user's communication terminal, and an emotion engine.

[0304] First, the user enters their planned destination into the travel platform application via a communication terminal. The server receives this information and accesses a database to collect historical data on the planned destination. Based on this, it utilizes generative AI technology to generate relevant text, images, and audio media content. Furthermore, the server predicts congestion at the planned destination and calculates the optimal time to visit.

[0305] In addition, an emotion engine installed in the user's communication terminal analyzes the user's emotions in real time. The analysis results of the emotion engine affect the content selection provided by the server. Specifically, the emotion engine reads emotions from the user's expressions and voices, and adjusts the provided information according to the emotions. For example, when the user is feeling stressed, the server can propose relaxation tourist spots and activities.

[0306] As a specific example, when the emotion engine detects an emotion indicating stress while the user is visiting a busy urban area, the server provides information recommending visiting quiet parks and relaxation facilities in the vicinity at an appropriate time. In this way, the emotion engine provides support for improving the experience according to the user's emotional state.

[0307] According to an embodiment of the present invention, it is possible to provide an individualized service that conforms to the user's emotions beyond simply providing visual information for the tourism experience. As a result, users can enjoy a more satisfying trip.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The user accesses the travel platform application through the communication terminal and enters the tourist destination to be visited and its schedule. The user can also enter the purpose and preferences of the stay as needed.

[0311] Step 2:

[0312] Based on the information received from the user, the server searches the internal database to collect historical data related to the destination to be visited. The server uses this data generation AI technology to automatically generate media content in text, image, and audio formats.

[0313] Step 3:

[0314] The server runs a congestion prediction algorithm for the planned visit location, analyzes the congestion situation on the planned visit date, and calculates the optimal visit time.

[0315] Step 4:

[0316] An emotion engine built into the communication terminal uses the camera and microphone to analyze the user's emotions in real time from their facial expressions and voice. The emotion engine determines the user's current emotional state.

[0317] Step 5:

[0318] The server considers the results of the emotion engine analysis and selects the media content most suitable for the user. For example, if it determines that relaxation is needed, the server will select content with relaxation effects.

[0319] Step 6:

[0320] The server transmits selected media content and congestion prediction information to the user's communication terminal. This allows the user to receive information that corresponds to their emotional state.

[0321] Step 7:

[0322] The device displays received content and provides users with historical information and cultural significance of tourist destinations, optimal times to visit, and suggestions for places and activities that match their mood.

[0323] Step 8:

[0324] Based on this information, users can adjust their visit plans and enjoy a sightseeing experience tailored to their individual needs. User feedback is later collected as data in the system and used to improve the system's accuracy.

[0325] (Example 2)

[0326] 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".

[0327] Current travel guide services provide general information about destinations, but they do not adequately address the individual needs, circumstances, and especially emotional states of users. Furthermore, optimizing travel plans to account for crowd levels is difficult. There is a need to solve these problems and provide users with personalized experiences.

[0328] 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.

[0329] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio using natural language processing technology; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; and means for analyzing the user's emotions in real time and adjusting the information provided based on the analysis results. This makes it possible to provide users with personalized information tailored to their emotions and propose optimal visit plans that avoid congestion.

[0330] "Planned destination" refers to a place or region that the user plans to visit for the purpose of travel or sightseeing.

[0331] A "communication device" is a device that sends and receives data over the internet, enabling the exchange of information between a user and a server.

[0332] "Historical data" refers to information accumulated based on past events and actions, including historical, cultural, and geographical data related to the planned destination.

[0333] "Natural language processing technology" is a technology that uses computers to understand, generate, and manipulate natural human language, and is used for analyzing and generating text data.

[0334] "Media content" refers to information provided in forms such as text, images, and audio, and is information that has visual or auditory value.

[0335] "Crowding" refers to the degree of congestion or density of people in a particular place or space.

[0336] "Optimal visiting time" refers to the time or period of time when a user visits a planned destination to avoid crowds and to sightsee effectively and efficiently.

[0337] "Emotion" refers to the psychological response that a user shows to a specific situation or stimulus, and includes states such as stress, joy, and excitement.

[0338] "Analysis" refers to the process of thoroughly examining data and information to understand its meaning and patterns.

[0339] "Adjusting information" refers to optimizing the information provided according to the user's situation and needs, and modifying its content as necessary.

[0340] This invention is a system for providing users with an optimal tourism experience, operating in combination with a server, user communication devices, and an engine for analyzing emotions in real time. Its main components include data reception, content generation, congestion prediction, emotion analysis, and information provision functions.

[0341] The server receives information about planned destinations entered by the user using a communication device. Hardware used here includes internet-connected smartphones and tablets. The server is connected to a database containing historical data related to the planned destinations, and uses this database to collect relevant data. Based on the collected data, a generative AI model automatically generates media content such as text, images, and audio. Natural language processing techniques are used in the generative AI model.

[0342] Next, the server analyzes historical data and real-time information to predict congestion at the planned visit location and calculate the optimal visit time. AI-based analysis technology is applied at this stage. Furthermore, an emotion engine built into the user's communication device uses a camera and microphone to analyze the user's facial expressions and voice, determining their emotional state in real time. The analysis results are sent to the server, influencing the selection of information provided.

[0343] For example, if a user enters "I plan to go to Tokyo Skytree" into a tourism application, the server will create an optimal visit plan based on tourist information around Skytree and past congestion data. Furthermore, if the user indicates using a communication device that they want to relax, the server will suggest places such as quiet cafes or parks. In this case, prompts to the generating AI model would be in the form of "Please provide information about the area around Tokyo Skytree" or "Please suggest places where I can relax."

[0344] Thus, according to the embodiments of the invention, users can obtain personalized tourist information and utilize optimal travel plans tailored to their emotions and circumstances.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] Users access the travel platform using a communication terminal and enter their planned destinations. The entered data is sent from the terminal to the server. Users are required to enter specific place names and desired locations in text format. The terminal's output is the request data sent to the server.

[0348] Step 2:

[0349] The server collects relevant historical data from a database based on the received data on the planned destination. The database contains data on past congestion levels, tourist attractions, and cultural and historical information for the destination. The server filters and aggregates this data to form a dataset for input into the generative AI model. The server's output is the prepared data for use by the generative AI model.

[0350] Step 3:

[0351] The server inputs collected historical data into a generation AI model, which automatically generates media content such as text, images, and audio. The generation AI model utilizes natural language processing techniques to output useful and relevant content for the user. The prompt used is "Generate tourist information about the planned destination." The server's output is the generated media content.

[0352] Step 4:

[0353] The server applies congestion prediction technology to forecast future congestion levels at the planned destination. By analyzing past congestion data and current conditions in real time, it models congestion patterns for the destination. Based on this information, the server calculates the optimal visit time and creates a suggested visit schedule for the user. The server's output is a schedule that includes the recommended visit time.

[0354] Step 5:

[0355] The emotion engine installed in the user's device activates, capturing the user's facial expressions with the camera and collecting audio with the microphone. The device analyzes this data in real time to infer the user's emotional state. For example, it analyzes whether the user is feeling stressed or expressing a desire to relax. This result is sent to the server and used to inform the next content selection. The output of the device is the user's emotional state data.

[0356] Step 6:

[0357] The server adjusts the media content and tourist information it provides based on emotional data transmitted from the emotion engine. It offers information in a way that resonates with the user's emotions, such as suggesting quiet locations if the user wants to relax. The server then constructs the final information package and sends it to the user's communication terminal. The server's output is customized tourist information adapted to the user's emotions.

[0358] (Application Example 2)

[0359] 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."

[0360] In modern tourism experiences, it is difficult to provide not just information, but also a personalized experience tailored to the user's emotions and circumstances. Tourists are easily affected by time and crowd conditions, and may experience stress and dissatisfaction, requiring effective methods to address these issues. Therefore, to improve the quality of tourism, there is a need to realize a system that can provide appropriate information and plans that respond to the user's emotions.

[0361] 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.

[0362] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio based on the historical data; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; means for incorporating an engine for analyzing the user's emotional state and adjusting the media content based on the analysis results; and means for providing the generated media content and congestion prediction information to the user's communication device. This makes it possible to provide sightseeing plans tailored to the user's emotions and a comfortable experience by avoiding congestion.

[0363] "Information about planned destinations" refers to detailed information about places the user plans to visit, including the names of tourist attractions and the planned duration of stay.

[0364] "Historical data" refers to past records and information about the planned destination, including reviews from past visitors and weather information.

[0365] "Media content" refers to content in the form of text, images, audio, etc., that provides information about tourist destinations visually or audibly.

[0366] "Congestion forecasting" is a method for predicting the number of people and the level of crowding at a planned destination, and uses this information to determine the optimal time to visit.

[0367] The "emotional state analysis engine" is a function that analyzes the user's emotions through their facial expressions and voice, and adjusts content and suggestions based on that information.

[0368] A "communication device" refers to a device owned by the user, such as a smartphone or tablet, through which they can receive tourist information.

[0369] The system that realizes this invention consists of a server, a user's communication terminal, and an engine that analyzes emotional states. The server receives information about the user's planned visit locations and collects related historical data. Based on this historical data, it automatically generates media content such as text, images, and audio using an open-source generative AI model. Furthermore, the server predicts the congestion level of the planned visit locations and calculates an appropriate visit time.

[0370] The user's communication device is equipped with an emotion engine for real-time analysis of their emotional state. For example, it uses the Microsoft Azure Emotion API to analyze emotions through the user's facial expressions and voice. The results of this emotion analysis are used to adjust media content generated on the server to match the user's emotions.

[0371] Furthermore, the communication terminal receives and provides users with optimized media content and congestion forecast information. Specifically, when a user inputs information about their planned destination, the server generates content based on that information and makes optimal suggestions according to the results of sentiment analysis. For example, for a user experiencing stress, it might suggest visiting a quiet tourist spot or a relaxation facility.

[0372] For example, if a user experiences stress while sightseeing in Kyoto and is on their way to a crowded tourist spot, the emotion engine will detect this, and the server will suggest nearby quiet parks or temples. This kind of information provides the user with the best possible sightseeing experience.

[0373] By combining generative AI models with an emotion engine, it's possible to provide personalized travel experiences tailored to the user's emotions. An example of a prompt might be: "A traveler is feeling stressed while visiting a famous autumn foliage spot in Kyoto. Read their current emotions and suggest quieter tourist spots to avoid crowds."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server receives information about the planned destination from the user's communication terminal. The input is tourist destination information selected by the user, and the output is a query to a database based on that information. As part of data processing, the server collects historical data related to the planned destination.

[0377] Step 2:

[0378] The server automatically generates media content such as text, images, and audio using a generative AI model based on collected historical data. The input is historical data, and the output is the generated multimedia content. In this process, as a data computation, the information is combined by generative AI technology and transformed into a form that can be presented visually or aurally.

[0379] Step 3:

[0380] The server predicts congestion at a planned destination based on collected data and real-time traffic information, and calculates the optimal visit time. Inputs are historical data and current traffic information, and output is congestion prediction information. The appropriate visit time is calculated based on the congestion data predicted by the algorithm.

[0381] Step 4:

[0382] An emotion engine installed in the user's communication terminal analyzes the user's emotional state from their facial expressions and voice. The input is real-time data from the user via camera and microphone, and the output is emotional state data. An emotion recognition algorithm is used to process the data and quantify the emotion.

[0383] Step 5:

[0384] The server receives analysis results from the emotion engine and adjusts the media content it provides based on those results. The input is emotional state data, and the output is the adjusted media content. The server customizes the information to match the user's current emotions and enhances information about places where they can relax.

[0385] Step 6:

[0386] The server provides the user's communication terminal with adjusted media content and congestion forecast information. The input is adjusted content and congestion information, and the output is presented on the user interface. The user can use this information to make visits that align with their mood and travel plans.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention provides a system that enables tourists to enjoy a deeper understanding of their planned destinations and a more efficient travel experience. This system operates through a network, with a server, user communication terminals, and users working together.

[0404] Users access a travel platform application using a communication terminal. Here, they input their desired tourist destinations and itinerary, and send this information to the server. The server then accesses a database based on the received information about the planned destinations to collect relevant historical data. Furthermore, using generative AI technology, this data is automatically generated as media content in the form of text, images, and audio.

[0405] Furthermore, the server analyzes the predicted congestion level using a congestion prediction algorithm for the planned destination. It then transmits media content and congestion prediction information based on this historical data to the user's communication terminal. The user's communication terminal displays this information appropriately, providing the user with historical background and cultural significance of the tourist destination, and offering advice, including the optimal time to visit.

[0406] As a concrete example, when a user registers a plan to visit a historical site in a certain city, the server generates and provides content explaining important historical events and the history of the building related to that site. At the same time, it predicts the peak tourist times for that site and suggests alternative visiting times to avoid them. In this way, users can gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0407] This system allows tourists to not only have a richer travel experience but also to plan their trips efficiently while avoiding crowds at tourist destinations.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The user accesses the travel platform application using a communication terminal. The user enters the tourist destinations and dates they plan to visit and sends this information to the server.

[0411] Step 2:

[0412] The server uses the information about the planned destination received from the user to query its internal database and collect the relevant historical data.

[0413] Step 3:

[0414] The server processes the collected historical data using AI technology to automatically generate media content in the form of text, images, and audio.

[0415] Step 4:

[0416] The server runs a congestion prediction algorithm for the planned visit location and analyzes the predicted congestion level on the planned visit date.

[0417] Step 5:

[0418] The server sends the generated media content and congestion prediction information to the user's communication terminal.

[0419] Step 6:

[0420] The device displays the received information on its screen, presenting the user with information about the history and cultural significance of the tourist destination, as well as recommendations for the optimal time to visit.

[0421] Step 7:

[0422] Based on the information provided, users can adjust their travel plans and enjoy an efficient and meaningful travel experience.

[0423] (Example 1)

[0424] 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."

[0425] A challenge exists in that visitors planning to tour tourist destinations often lack sufficient prior information, preventing them from understanding the background and cultural significance of the sites, and from predicting crowd levels during their visits, thus hindering them from enjoying an effective and efficient travel experience. Furthermore, it is often difficult to determine the appropriate time to visit individual tourist destinations, making it challenging to optimize the length of stay.

[0426] 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.

[0427] In this invention, the server includes means for receiving information on the user's planned destinations via a communication device and collecting historical information related to those destinations from a storage device; means for automatically generating information media such as text, still images, and audio data based on prompt instructions using generation AI technology based on the historical information; and means for applying an algorithm to analyze the congestion status of the planned destinations and evaluating the optimal visit time. This enables users to enjoy a comfortable and efficient travel experience by providing a deep understanding of tourist destinations and enabling visit plans that avoid congestion.

[0428] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate various media content such as text, images, and audio based on input provided by users.

[0429] A "prompt instruction" is a guide or instruction provided to a generative AI technology to obtain the desired output.

[0430] "Information media" refers to digital content such as text, still images, and audio data, which are used to provide information to users.

[0431] "Communication equipment" refers to a part of the hardware or software used to receive user input data and to send and receive data with a server.

[0432] A "storage device" refers to a storage system, such as a database, that stores historical information and data related to tourist destinations and makes them accessible as needed.

[0433] A "congestion analysis algorithm" is a mathematical method used to predict future congestion levels and evaluate optimal visiting times based on data such as the number of visitors and time of day at tourist destinations.

[0434] A "communication terminal" is an electronic device used by users to input information and receive and display media content and congestion information sent from a server.

[0435] This invention relates to a system that enables tourists to gain a deep understanding of detailed information about their destinations and to travel efficiently. The system consists of a server, a communication terminal, and a user. The server plays a central role in data processing, and the communication terminal functions as the user interface.

[0436] Users access the travel platform application using a communication device. Here, they enter the names of the tourist destinations they wish to visit and their travel dates. The communication device then transmits the entered information to the server. Specifically, portable electronic devices such as smartphones and tablets are often used as the device. The communication device and the server exchange data via the internet.

[0437] The server collects relevant historical information from a database based on the received information. This database stores various historical information related to tourist destinations. The collected data is processed using a generation AI model, such as GPT-3, based on prompts, and the corresponding text, image, and audio data is generated. An example of a prompt used in this process is, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The generated media content provides specific information to support a deeper understanding of tourist destinations.

[0438] Furthermore, the server applies its own congestion analysis algorithm to predict congestion levels at the planned destination and calculate the optimal visit time. This information is intended to enable users to have a less stressful visit.

[0439] The generated media content and congestion forecast information are transmitted to the communication terminal. The user's communication terminal receives this information and displays it appropriately through the user interface. For example, it may show historical descriptions of tourist spots or suggested visiting times to avoid peak hours. This allows users to gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The user accesses the travel platform application using a communication terminal and enters the name of the tourist destination they wish to visit and the planned date of visit. This input information is sent to the server by the communication terminal. The input consists of the planned destination and date, and an information packet is generated that is sent to the server as output. The communication terminal accurately receives the user's input and packages it into a packet format so that it can be appropriately transmitted to the server.

[0443] Step 2:

[0444] The server receives information about the planned destination from the communication terminal. Based on the received data, it accesses the database, which is the storage device, and collects the corresponding historical information. The input is the data of the planned destination received from the user, and the output is extracted historical data. The server queries the database and aggregates all historical data related to the specified tourist destination.

[0445] Step 3:

[0446] The server inputs collected historical information into a generating AI model, which then automatically generates media content based on prompts. For example, it might use the prompt, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The input consists of collected historical data and prompt instructions, while the output consists of generated text, images, and audio data. The server operates the AI ​​model to generate the desired output according to the instructions.

[0447] Step 4:

[0448] The server uses a congestion analysis algorithm to predict congestion levels at a planned visit location and calculate the optimal visit time. Inputs include past visitor numbers and related data, while outputs are predicted congestion times and optimal visit times. The server processes this data using an algorithm to calculate time periods that users should avoid.

[0449] Step 5:

[0450] The generated media content and congestion prediction information are sent from the server to the communication terminal. The input is the generated media content and prediction information, and the output is delivered to the user's communication terminal. The server formats the information in an appropriate format and sends it efficiently to the communication terminal.

[0451] Step 6:

[0452] The communication terminal receives information transmitted from the server and displays it through the user interface. The user adjusts their sightseeing plan based on this information. Input consists of media content and predictive information from the server, while output is the displayed information received by the user. The communication terminal automatically adjusts the display method to provide the information to the user in an easily understandable manner.

[0453] (Application Example 1)

[0454] 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."

[0455] To enhance the tourism experience, it is necessary to deepen cultural and historical understanding of the destination and to develop efficient travel plans that avoid congestion. However, traditional travel plans rely on individual information gathering and analysis, which is time-consuming and laborious for travelers. Furthermore, acquiring and purchasing local specialties at destinations has not provided a smooth experience.

[0456] 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.

[0457] In this invention, the server includes means for receiving information on the user's planned destination and collecting related data, means for automatically generating media content based on the data, and means for presenting regional information using virtual guidance. This allows the user to deepen their understanding of the history and culture of their planned destination, receive suggestions for the optimal time to visit to avoid crowds, and realize a smooth experience of trading local specialty products in a virtual environment.

[0458] "Planned destination" refers to a place that the user plans to visit for travel or sightseeing purposes.

[0459] "Data" refers to a collection of information that includes historical, cultural, and historical content related to the planned destination.

[0460] "Media content" refers to information expressed in forms such as text, images, and audio, which is provided to users visually or audibly.

[0461] "Predictive information" refers to data that shows the expected congestion level and optimal time to visit a planned destination.

[0462] A "terminal" is a type of electronic device used by users to receive and display information.

[0463] "Virtual guidance" refers to a means of extending and supplementing information about real-world destinations using digital technology.

[0464] The system that implements this application is designed to provide tourist information, and the server plays a crucial role. The server receives information about the user's planned destinations and collects relevant data based on that information. This data includes historical and cultural information about the destinations and is automatically generated as media content such as text, images, and audio using a generative AI model.

[0465] Furthermore, the server utilizes a congestion prediction algorithm for the planned destination to calculate the optimal time to visit. This prediction information is sent to the user's device, allowing the user to plan an efficient trip based on it. The user's device is a smartphone or smart glasses, equipped with software to visually display the information.

[0466] Furthermore, it is possible to use virtual guidance tools to present local information on devices. For example, users can listen to audio guides about local historical sites. Presenting local specialties and recommended travel routes can also enhance the sightseeing experience.

[0467] For example, if a user plans to visit a historical landmark, the server generates content based on extensive historical data about that landmark and provides it to the user in audio or visual format. An example of a prompt used in this case might be, "Generate detailed content based on information about the historical landmark and crowd predictions."

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The user enters information about their planned destination using a device.

[0471] The information entered includes the name of the tourist destination and the itinerary of the visit, and this information is sent to the server. The server receives this information and uses it as the basis for collecting data on the planned destination.

[0472] Step 2:

[0473] The server accesses a database based on the received information about the planned destination and collects relevant historical and cultural data.

[0474] Using this input data, the server collects historical data and related information, and obtains refined information as output. The results of the data collection are necessary for generating the next media content.

[0475] Step 3:

[0476] The server uses a generative AI model to automatically generate media content such as text, images, and audio based on the collected data.

[0477] The input here is collected data, and the output is media content in a format that can be provided to the user. In this generation process, the generation AI model utilizes prompts to construct detailed content.

[0478] Step 4:

[0479] The server uses a congestion prediction algorithm for the planned visit location to analyze congestion levels and calculate the optimal visit time.

[0480] The input consists of historical data and pre-provided congestion data, while the output is congestion prediction information, such as times when it is best to avoid visiting. This calculation is an essential step in maximizing the user experience.

[0481] Step 5:

[0482] The generated media content and congestion forecast information are sent to the user's device and displayed appropriately on the device.

[0483] The input from the server is media and predictive information, while the output from the terminal is visual or auditory information provided to the user. This allows the user to obtain information and timing for the optimal destination.

[0484] Step 6:

[0485] It provides real-time tourist information on a terminal using a virtual guidance system.

[0486] The input consists of generated content and real-time guidance information, while the output is an interactive experience for the user. Users can experience digitally enhanced tourist information using their devices.

[0487] 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.

[0488] This invention provides a system that enhances tourists' experiences at their destinations and enables the provision of information tailored to their individual emotions. This system operates by combining a server, a user's communication terminal, and an emotion engine.

[0489] First, the user enters their planned destination into the travel platform application via a communication terminal. The server receives this information and accesses a database to collect historical data on the planned destination. Based on this, it utilizes generative AI technology to generate relevant text, images, and audio media content. Furthermore, the server predicts congestion at the planned destination and calculates the optimal time to visit.

[0490] In addition, an emotion engine installed in the user's communication device analyzes the user's emotions in real time. The results of the emotion engine's analysis influence the content selection provided by the server. Specifically, the emotion engine reads the user's emotions from their facial expressions and voice, and adjusts the information provided accordingly. For example, if the user is feeling stressed, the server can suggest relaxing tourist spots or activities.

[0491] For example, if the emotion engine detects that a user is experiencing stress while visiting a busy urban area, the server will provide information recommending that the user visit a nearby quiet park or relaxation facility at an appropriate time. In this way, the emotion engine helps to improve the user's experience in accordance with their emotional state.

[0492] Embodiments of the present invention enable tourism experiences to go beyond simply providing visual information and offer personalized services that resonate with the user's emotions. This allows users to enjoy a more satisfying travel experience.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Users access the travel platform application via a communication device and enter their planned tourist destinations and dates. Users can also fill in their purpose of stay and preferences as needed.

[0496] Step 2:

[0497] The server searches its internal database based on information received from the user to collect historical data related to the planned visit location. The server then uses AI technology to automatically generate media content in text, image, and audio formats from this data.

[0498] Step 3:

[0499] The server runs a congestion prediction algorithm for the planned visit location, analyzes the congestion situation on the planned visit date, and calculates the optimal visit time.

[0500] Step 4:

[0501] An emotion engine built into the communication terminal uses the camera and microphone to analyze the user's emotions in real time from their facial expressions and voice. The emotion engine determines the user's current emotional state.

[0502] Step 5:

[0503] The server considers the results of the emotion engine analysis and selects the media content most suitable for the user. For example, if it determines that relaxation is needed, the server will select content with relaxation effects.

[0504] Step 6:

[0505] The server transmits selected media content and congestion prediction information to the user's communication terminal. This allows the user to receive information that corresponds to their emotional state.

[0506] Step 7:

[0507] The device displays received content and provides users with historical information and cultural significance of tourist destinations, optimal times to visit, and suggestions for places and activities that match their mood.

[0508] Step 8:

[0509] Based on this information, users can adjust their visit plans and enjoy a sightseeing experience tailored to their individual needs. User feedback is later collected as data in the system and used to improve the system's accuracy.

[0510] (Example 2)

[0511] 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."

[0512] Current travel guide services provide general information about destinations, but they do not adequately address the individual needs, circumstances, and especially emotional states of users. Furthermore, optimizing travel plans to account for crowd levels is difficult. There is a need to solve these problems and provide users with personalized experiences.

[0513] 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.

[0514] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio using natural language processing technology; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; and means for analyzing the user's emotions in real time and adjusting the information provided based on the analysis results. This makes it possible to provide users with personalized information tailored to their emotions and propose optimal visit plans that avoid congestion.

[0515] "Planned destination" refers to a place or region that the user plans to visit for the purpose of travel or sightseeing.

[0516] A "communication device" is a device that sends and receives data over the internet, enabling the exchange of information between a user and a server.

[0517] "Historical data" refers to information accumulated based on past events and actions, including historical, cultural, and geographical data related to the planned destination.

[0518] "Natural language processing technology" is a technology that uses computers to understand, generate, and manipulate natural human language, and is used for analyzing and generating text data.

[0519] "Media content" refers to information provided in forms such as text, images, and audio, and is information that has visual or auditory value.

[0520] "Crowding" refers to the degree of congestion or density of people in a particular place or space.

[0521] "Optimal visiting time" refers to the time or period of time when a user visits a planned destination to avoid crowds and to sightsee effectively and efficiently.

[0522] "Emotion" refers to the psychological response that a user shows to a specific situation or stimulus, and includes states such as stress, joy, and excitement.

[0523] "Analysis" refers to the process of thoroughly examining data and information to understand its meaning and patterns.

[0524] "Adjusting information" refers to optimizing the information provided according to the user's situation and needs, and modifying its content as necessary.

[0525] This invention is a system for providing users with an optimal tourism experience, operating in combination with a server, user communication devices, and an engine for analyzing emotions in real time. Its main components include data reception, content generation, congestion prediction, emotion analysis, and information provision functions.

[0526] The server receives information about planned destinations entered by the user using a communication device. Hardware used here includes internet-connected smartphones and tablets. The server is connected to a database containing historical data related to the planned destinations, and uses this database to collect relevant data. Based on the collected data, a generative AI model automatically generates media content such as text, images, and audio. Natural language processing techniques are used in the generative AI model.

[0527] Next, the server analyzes historical data and real-time information to predict congestion at the planned visit location and calculate the optimal visit time. AI-based analysis technology is applied at this stage. Furthermore, an emotion engine built into the user's communication device uses a camera and microphone to analyze the user's facial expressions and voice, determining their emotional state in real time. The analysis results are sent to the server, influencing the selection of information provided.

[0528] For example, if a user enters "I plan to go to Tokyo Skytree" into a tourism application, the server will create an optimal visit plan based on tourist information around Skytree and past congestion data. Furthermore, if the user indicates using a communication device that they want to relax, the server will suggest places such as quiet cafes or parks. In this case, prompts to the generating AI model would be in the form of "Please provide information about the area around Tokyo Skytree" or "Please suggest places where I can relax."

[0529] Thus, according to the embodiments of the invention, users can obtain personalized tourist information and utilize optimal travel plans tailored to their emotions and circumstances.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] Users access the travel platform using a communication terminal and enter their planned destinations. The entered data is sent from the terminal to the server. Users are required to enter specific place names and desired locations in text format. The terminal's output is the request data sent to the server.

[0533] Step 2:

[0534] The server collects relevant historical data from a database based on the received data on the planned destination. The database contains data on past congestion levels, tourist attractions, and cultural and historical information for the destination. The server filters and aggregates this data to form a dataset for input into the generative AI model. The server's output is the prepared data for use by the generative AI model.

[0535] Step 3:

[0536] The server inputs collected historical data into a generation AI model, which automatically generates media content such as text, images, and audio. The generation AI model utilizes natural language processing techniques to output useful and relevant content for the user. The prompt used is "Generate tourist information about the planned destination." The server's output is the generated media content.

[0537] Step 4:

[0538] The server applies congestion prediction technology to forecast future congestion levels at the planned destination. By analyzing past congestion data and current conditions in real time, it models congestion patterns for the destination. Based on this information, the server calculates the optimal visit time and creates a suggested visit schedule for the user. The server's output is a schedule that includes the recommended visit time.

[0539] Step 5:

[0540] The emotion engine installed in the user's device activates, capturing the user's facial expressions with the camera and collecting audio with the microphone. The device analyzes this data in real time to infer the user's emotional state. For example, it analyzes whether the user is feeling stressed or expressing a desire to relax. This result is sent to the server and used to inform the next content selection. The output of the device is the user's emotional state data.

[0541] Step 6:

[0542] The server adjusts the media content and tourist information it provides based on emotional data transmitted from the emotion engine. It offers information in a way that resonates with the user's emotions, such as suggesting quiet locations if the user wants to relax. The server then constructs the final information package and sends it to the user's communication terminal. The server's output is customized tourist information adapted to the user's emotions.

[0543] (Application Example 2)

[0544] 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."

[0545] In modern tourism experiences, it is difficult to provide not just information, but also a personalized experience tailored to the user's emotions and circumstances. Tourists are easily affected by time and crowd conditions, and may experience stress and dissatisfaction, requiring effective methods to address these issues. Therefore, to improve the quality of tourism, there is a need to realize a system that can provide appropriate information and plans that respond to the user's emotions.

[0546] 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.

[0547] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio based on the historical data; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; means for incorporating an engine for analyzing the user's emotional state and adjusting the media content based on the analysis results; and means for providing the generated media content and congestion prediction information to the user's communication device. This makes it possible to provide sightseeing plans tailored to the user's emotions and a comfortable experience by avoiding congestion.

[0548] "Information about planned destinations" refers to detailed information about places the user plans to visit, including the names of tourist attractions and the planned duration of stay.

[0549] "Historical data" refers to past records and information about the planned destination, including reviews from past visitors and weather information.

[0550] "Media content" refers to content in the form of text, images, audio, etc., that provides information about tourist destinations visually or audibly.

[0551] "Congestion forecasting" is a method for predicting the number of people and the level of crowding at a planned destination, and uses this information to determine the optimal time to visit.

[0552] The "emotional state analysis engine" is a function that analyzes the user's emotions through their facial expressions and voice, and adjusts content and suggestions based on that information.

[0553] A "communication device" refers to a device owned by the user, such as a smartphone or tablet, through which they can receive tourist information.

[0554] The system that realizes this invention consists of a server, a user's communication terminal, and an engine that analyzes emotional states. The server receives information about the user's planned visit locations and collects related historical data. Based on this historical data, it automatically generates media content such as text, images, and audio using an open-source generative AI model. Furthermore, the server predicts the congestion level of the planned visit locations and calculates an appropriate visit time.

[0555] The user's communication device is equipped with an emotion engine for real-time analysis of their emotional state. For example, it uses the Microsoft Azure Emotion API to analyze emotions through the user's facial expressions and voice. The results of this emotion analysis are used to adjust media content generated on the server to match the user's emotions.

[0556] Furthermore, the communication terminal receives and provides users with optimized media content and congestion forecast information. Specifically, when a user inputs information about their planned destination, the server generates content based on that information and makes optimal suggestions according to the results of sentiment analysis. For example, for a user experiencing stress, it might suggest visiting a quiet tourist spot or a relaxation facility.

[0557] For example, if a user experiences stress while sightseeing in Kyoto and is on their way to a crowded tourist spot, the emotion engine will detect this, and the server will suggest nearby quiet parks or temples. This kind of information provides the user with the best possible sightseeing experience.

[0558] By combining generative AI models with an emotion engine, it's possible to provide personalized travel experiences tailored to the user's emotions. An example of a prompt might be: "A traveler is feeling stressed while visiting a famous autumn foliage spot in Kyoto. Read their current emotions and suggest quieter tourist spots to avoid crowds."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The server receives information about the planned destination from the user's communication terminal. The input is tourist destination information selected by the user, and the output is a query to a database based on that information. As part of data processing, the server collects historical data related to the planned destination.

[0562] Step 2:

[0563] The server automatically generates media content such as text, images, and audio using a generative AI model based on collected historical data. The input is historical data, and the output is the generated multimedia content. In this process, as a data computation, the information is combined by generative AI technology and transformed into a form that can be presented visually or aurally.

[0564] Step 3:

[0565] The server predicts congestion at a planned destination based on collected data and real-time traffic information, and calculates the optimal visit time. Inputs are historical data and current traffic information, and output is congestion prediction information. The appropriate visit time is calculated based on the congestion data predicted by the algorithm.

[0566] Step 4:

[0567] An emotion engine installed in the user's communication terminal analyzes the user's emotional state from their facial expressions and voice. The input is real-time data from the user via camera and microphone, and the output is emotional state data. An emotion recognition algorithm is used to process the data and quantify the emotion.

[0568] Step 5:

[0569] The server receives analysis results from the emotion engine and adjusts the media content it provides based on those results. The input is emotional state data, and the output is the adjusted media content. The server customizes the information to match the user's current emotions and enhances information about places where they can relax.

[0570] Step 6:

[0571] The server provides the user's communication terminal with adjusted media content and congestion forecast information. The input is adjusted content and congestion information, and the output is presented on the user interface. The user can use this information to make visits that align with their mood and travel plans.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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".

[0589] This invention provides a system that enables tourists to enjoy a deeper understanding of their planned destinations and a more efficient travel experience. This system operates through a network, with a server, user communication terminals, and users working together.

[0590] Users access a travel platform application using a communication terminal. Here, they input their desired tourist destinations and itinerary, and send this information to the server. The server then accesses a database based on the received information about the planned destinations to collect relevant historical data. Furthermore, using generative AI technology, this data is automatically generated as media content in the form of text, images, and audio.

[0591] Furthermore, the server analyzes the predicted congestion level using a congestion prediction algorithm for the planned destination. It then transmits media content and congestion prediction information based on this historical data to the user's communication terminal. The user's communication terminal displays this information appropriately, providing the user with historical background and cultural significance of the tourist destination, and offering advice, including the optimal time to visit.

[0592] As a concrete example, when a user registers a plan to visit a historical site in a certain city, the server generates and provides content explaining important historical events and the history of the building related to that site. At the same time, it predicts the peak tourist times for that site and suggests alternative visiting times to avoid them. In this way, users can gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0593] This system allows tourists to not only have a richer travel experience but also to plan their trips efficiently while avoiding crowds at tourist destinations.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The user accesses the travel platform application using a communication terminal. The user enters the tourist destinations and dates they plan to visit and sends this information to the server.

[0597] Step 2:

[0598] The server uses the information about the planned destination received from the user to query its internal database and collect the relevant historical data.

[0599] Step 3:

[0600] The server processes the collected historical data using AI technology to automatically generate media content in the form of text, images, and audio.

[0601] Step 4:

[0602] The server runs a congestion prediction algorithm for the planned visit location and analyzes the predicted congestion level on the planned visit date.

[0603] Step 5:

[0604] The server sends the generated media content and congestion prediction information to the user's communication terminal.

[0605] Step 6:

[0606] The device displays the received information on its screen, presenting the user with information about the history and cultural significance of the tourist destination, as well as recommendations for the optimal time to visit.

[0607] Step 7:

[0608] Based on the information provided, users can adjust their travel plans and enjoy an efficient and meaningful travel experience.

[0609] (Example 1)

[0610] 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".

[0611] A challenge exists in that visitors planning to tour tourist destinations often lack sufficient prior information, preventing them from understanding the background and cultural significance of the sites, and from predicting crowd levels during their visits, thus hindering them from enjoying an effective and efficient travel experience. Furthermore, it is often difficult to determine the appropriate time to visit individual tourist destinations, making it challenging to optimize the length of stay.

[0612] 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.

[0613] In this invention, the server includes means for receiving information on the user's planned destinations via a communication device and collecting historical information related to those destinations from a storage device; means for automatically generating information media such as text, still images, and audio data based on prompt instructions using generation AI technology based on the historical information; and means for applying an algorithm to analyze the congestion status of the planned destinations and evaluating the optimal visit time. This enables users to enjoy a comfortable and efficient travel experience by providing a deep understanding of tourist destinations and enabling visit plans that avoid congestion.

[0614] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate various media content such as text, images, and audio based on input provided by users.

[0615] A "prompt instruction" is a guide or instruction provided to a generative AI technology to obtain the desired output.

[0616] "Information media" refers to digital content such as text, still images, and audio data, which are used to provide information to users.

[0617] "Communication equipment" refers to a part of the hardware or software used to receive user input data and to send and receive data with a server.

[0618] A "storage device" refers to a storage system, such as a database, that stores historical information and data related to tourist destinations and makes them accessible as needed.

[0619] A "congestion analysis algorithm" is a mathematical method used to predict future congestion levels and evaluate optimal visiting times based on data such as the number of visitors and time of day at tourist destinations.

[0620] A "communication terminal" is an electronic device used by users to input information and receive and display media content and congestion information sent from a server.

[0621] This invention relates to a system that enables tourists to gain a deep understanding of detailed information about their destinations and to travel efficiently. The system consists of a server, a communication terminal, and a user. The server plays a central role in data processing, and the communication terminal functions as the user interface.

[0622] Users access the travel platform application using a communication device. Here, they enter the names of the tourist destinations they wish to visit and their travel dates. The communication device then transmits the entered information to the server. Specifically, portable electronic devices such as smartphones and tablets are often used as the device. The communication device and the server exchange data via the internet.

[0623] The server collects relevant historical information from a database based on the received information. This database stores various historical information related to tourist destinations. The collected data is processed using a generation AI model, such as GPT-3, based on prompts, and the corresponding text, image, and audio data is generated. An example of a prompt used in this process is, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The generated media content provides specific information to support a deeper understanding of tourist destinations.

[0624] Furthermore, the server applies its own congestion analysis algorithm to predict congestion levels at the planned destination and calculate the optimal visit time. This information is intended to enable users to have a less stressful visit.

[0625] The generated media content and congestion forecast information are transmitted to the communication terminal. The user's communication terminal receives this information and displays it appropriately through the user interface. For example, it may show historical descriptions of tourist spots or suggested visiting times to avoid peak hours. This allows users to gain a detailed understanding of the background of their destination and enjoy a comfortable and efficient sightseeing experience.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The user accesses the travel platform application using a communication terminal and enters the name of the tourist destination they wish to visit and the planned date of visit. This input information is sent to the server by the communication terminal. The input consists of the planned destination and date, and an information packet is generated that is sent to the server as output. The communication terminal accurately receives the user's input and packages it into a packet format so that it can be appropriately transmitted to the server.

[0629] Step 2:

[0630] The server receives information about the planned destination from the communication terminal. Based on the received data, it accesses the database, which is the storage device, and collects the corresponding historical information. The input is the data of the planned destination received from the user, and the output is extracted historical data. The server queries the database and aggregates all historical data related to the specified tourist destination.

[0631] Step 3:

[0632] The server inputs collected historical information into a generating AI model, which then automatically generates media content based on prompts. For example, it might use the prompt, "Explain the historical background of Senso-ji Temple and suggest times when there are fewer visitors." The input consists of collected historical data and prompt instructions, while the output consists of generated text, images, and audio data. The server operates the AI ​​model to generate the desired output according to the instructions.

[0633] Step 4:

[0634] The server uses a congestion analysis algorithm to predict congestion levels at a planned visit location and calculate the optimal visit time. Inputs include past visitor numbers and related data, while outputs are predicted congestion times and optimal visit times. The server processes this data using an algorithm to calculate time periods that users should avoid.

[0635] Step 5:

[0636] The generated media content and congestion prediction information are sent from the server to the communication terminal. The input is the generated media content and prediction information, and the output is delivered to the user's communication terminal. The server formats the information in an appropriate format and sends it efficiently to the communication terminal.

[0637] Step 6:

[0638] The communication terminal receives information transmitted from the server and displays it through the user interface. The user adjusts their sightseeing plan based on this information. Input consists of media content and predictive information from the server, while output is the displayed information received by the user. The communication terminal automatically adjusts the display method to provide the information to the user in an easily understandable manner.

[0639] (Application Example 1)

[0640] 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".

[0641] To enhance the tourism experience, it is necessary to deepen cultural and historical understanding of the destination and to develop efficient travel plans that avoid congestion. However, traditional travel plans rely on individual information gathering and analysis, which is time-consuming and laborious for travelers. Furthermore, acquiring and purchasing local specialties at destinations has not provided a smooth experience.

[0642] 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.

[0643] In this invention, the server includes means for receiving information on the user's planned destination and collecting related data, means for automatically generating media content based on the data, and means for presenting regional information using virtual guidance. This allows the user to deepen their understanding of the history and culture of their planned destination, receive suggestions for the optimal time to visit to avoid crowds, and realize a smooth experience of trading local specialty products in a virtual environment.

[0644] "Planned destination" refers to a place that the user plans to visit for travel or sightseeing purposes.

[0645] "Data" refers to a collection of information that includes historical, cultural, and historical content related to the planned destination.

[0646] "Media content" refers to information expressed in forms such as text, images, and audio, which is provided to users visually or audibly.

[0647] "Predictive information" refers to data that shows the expected congestion level and optimal time to visit a planned destination.

[0648] A "terminal" is a type of electronic device used by users to receive and display information.

[0649] "Virtual guidance" refers to a means of extending and supplementing information about real-world destinations using digital technology.

[0650] The system that implements this application is designed to provide tourist information, and the server plays a crucial role. The server receives information about the user's planned destinations and collects relevant data based on that information. This data includes historical and cultural information about the destinations and is automatically generated as media content such as text, images, and audio using a generative AI model.

[0651] Furthermore, the server utilizes a congestion prediction algorithm for the planned destination to calculate the optimal time to visit. This prediction information is sent to the user's device, allowing the user to plan an efficient trip based on it. The user's device is a smartphone or smart glasses, equipped with software to visually display the information.

[0652] Furthermore, it is possible to use virtual guidance tools to present local information on devices. For example, users can listen to audio guides about local historical sites. Presenting local specialties and recommended travel routes can also enhance the sightseeing experience.

[0653] For example, if a user plans to visit a historical landmark, the server generates content based on extensive historical data about that landmark and provides it to the user in audio or visual format. An example of a prompt used in this case might be, "Generate detailed content based on information about the historical landmark and crowd predictions."

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] The user enters information about their planned destination using a device.

[0657] The information entered includes the name of the tourist destination and the itinerary of the visit, and this is sent to the server. The server receives this information and uses it as the basis for collecting data on the planned destination.

[0658] Step 2:

[0659] The server accesses a database based on the received information about the planned destination and collects relevant historical and cultural data.

[0660] Using this input data, the server collects historical data and related information, and obtains refined information as output. The results of the data collection are necessary for generating the next media content.

[0661] Step 3:

[0662] The server uses a generative AI model to automatically generate media content such as text, images, and audio based on the collected data.

[0663] The input here is collected data, and the output is media content in a format that can be provided to the user. In this generation process, the generation AI model utilizes prompts to construct detailed content.

[0664] Step 4:

[0665] The server uses a congestion prediction algorithm for the planned visit location to analyze congestion levels and calculate the optimal visit time.

[0666] The input consists of historical data and pre-provided congestion data, while the output is congestion prediction information, such as times when visits should be avoided. This calculation is an essential step in maximizing the user experience.

[0667] Step 5:

[0668] The generated media content and congestion forecast information are sent to the user's device and displayed appropriately on the device.

[0669] The input from the server is media and predictive information, while the output from the terminal is visual or auditory information provided to the user. This allows the user to obtain information and timing for the optimal destination.

[0670] Step 6:

[0671] It provides real-time tourist information on a terminal using a virtual guidance system.

[0672] The input consists of generated content and real-time information, while the output is an interactive experience for the user. Users can experience digitally enhanced tourism information using their devices.

[0673] 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.

[0674] This invention provides a system that enhances tourists' experiences at their destinations and enables the provision of information tailored to their individual emotions. This system operates by combining a server, a user's communication terminal, and an emotion engine.

[0675] First, the user enters their planned destination into the travel platform application via a communication terminal. The server receives this information and accesses a database to collect historical data on the planned destination. Based on this, it utilizes generative AI technology to generate relevant text, images, and audio media content. Furthermore, the server predicts congestion at the planned destination and calculates the optimal time to visit.

[0676] In addition, an emotion engine installed in the user's communication device analyzes the user's emotions in real time. The results of the emotion engine's analysis influence the content selection provided by the server. Specifically, the emotion engine reads the user's emotions from their facial expressions and voice, and adjusts the information provided accordingly. For example, if the user is feeling stressed, the server can suggest relaxing tourist spots or activities.

[0677] For example, if the emotion engine detects that a user is experiencing stress while visiting a busy urban area, the server will provide information recommending that the user visit a nearby quiet park or relaxation facility at an appropriate time. In this way, the emotion engine helps to improve the user's experience in accordance with their emotional state.

[0678] Embodiments of the present invention enable tourism experiences to go beyond simply providing visual information and offer personalized services that resonate with the user's emotions. This allows users to enjoy a more satisfying travel experience.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] Users access the travel platform application via a communication device and enter their planned tourist destinations and dates. Users can also fill in their purpose of stay and preferences as needed.

[0682] Step 2:

[0683] The server searches its internal database based on information received from the user to collect historical data related to the planned visit location. The server then uses AI technology to automatically generate media content in text, image, and audio formats from this data.

[0684] Step 3:

[0685] The server runs a congestion prediction algorithm for the planned visit location, analyzes the congestion situation on the planned visit date, and calculates the optimal visit time.

[0686] Step 4:

[0687] An emotion engine built into the communication terminal uses the camera and microphone to analyze the user's emotions in real time from their facial expressions and voice. The emotion engine determines the user's current emotional state.

[0688] Step 5:

[0689] The server considers the results of the emotion engine analysis and selects the media content most suitable for the user. For example, if it determines that relaxation is needed, the server will select content with relaxation effects.

[0690] Step 6:

[0691] The server transmits selected media content and congestion prediction information to the user's communication terminal. This allows the user to receive information that corresponds to their emotional state.

[0692] Step 7:

[0693] The device displays received content and provides users with historical information and cultural significance of tourist destinations, optimal times to visit, and suggestions for places and activities that match their mood.

[0694] Step 8:

[0695] Based on this information, users can adjust their visit plans and enjoy a sightseeing experience tailored to their individual needs. User feedback is later collected as data in the system and used to improve the system's accuracy.

[0696] (Example 2)

[0697] 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".

[0698] Current travel guide services provide general information about destinations, but they do not adequately address the individual needs, circumstances, and especially emotional states of users. Furthermore, optimizing travel plans to account for crowd levels is difficult. There is a need to solve these problems and provide users with personalized experiences.

[0699] 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.

[0700] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio using natural language processing technology; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; and means for analyzing the user's emotions in real time and adjusting the information provided based on the analysis results. This makes it possible to provide users with personalized information tailored to their emotions and propose optimal visit plans that avoid congestion.

[0701] "Planned destination" refers to a place or region that the user plans to visit for the purpose of travel or sightseeing.

[0702] A "communication device" is a device that sends and receives data over the internet, enabling the exchange of information between a user and a server.

[0703] "Historical data" refers to information accumulated based on past events and actions, including historical, cultural, and geographical data related to the planned destination.

[0704] "Natural language processing technology" is a technology that uses computers to understand, generate, and manipulate natural human language, and is used for analyzing and generating text data.

[0705] "Media content" refers to information provided in forms such as text, images, and audio, and is information that has visual or auditory value.

[0706] "Crowding" refers to the degree of congestion or density of people in a particular place or space.

[0707] "Optimal visiting time" refers to the time or period of time when a user visits a planned destination to avoid crowds and to sightsee effectively and efficiently.

[0708] "Emotion" refers to the psychological response that a user shows to a specific situation or stimulus, and includes states such as stress, joy, and excitement.

[0709] "Analysis" refers to the process of thoroughly examining data and information to understand its meaning and patterns.

[0710] "Adjusting information" refers to optimizing the information provided according to the user's situation and needs, and modifying its content as necessary.

[0711] This invention is a system for providing users with an optimal tourism experience, operating in combination with a server, user communication devices, and an engine for analyzing emotions in real time. Its main components include data reception, content generation, congestion prediction, emotion analysis, and information provision functions.

[0712] The server receives information about planned destinations entered by the user using a communication device. Hardware used here includes internet-connected smartphones and tablets. The server is connected to a database containing historical data related to the planned destinations, and uses this database to collect relevant data. Based on the collected data, a generative AI model automatically generates media content such as text, images, and audio. Natural language processing techniques are used in the generative AI model.

[0713] Next, the server analyzes historical data and real-time information to predict congestion at the planned visit location and calculate the optimal visit time. AI-based analysis technology is applied at this stage. Furthermore, an emotion engine built into the user's communication device uses a camera and microphone to analyze the user's facial expressions and voice, determining their emotional state in real time. The analysis results are sent to the server, influencing the selection of information provided.

[0714] For example, if a user enters "I plan to go to Tokyo Skytree" into a tourism application, the server will create an optimal visit plan based on tourist information around Skytree and past congestion data. Furthermore, if the user indicates using a communication device that they want to relax, the server will suggest places such as quiet cafes or parks. In this case, prompts to the generating AI model would be in the form of "Please provide information about the area around Tokyo Skytree" or "Please suggest places where I can relax."

[0715] Thus, according to the embodiments of the invention, users can obtain personalized tourist information and utilize optimal travel plans tailored to their emotions and circumstances.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] Users access the travel platform using a communication terminal and enter their planned destinations. The entered data is sent from the terminal to the server. Users are required to enter specific place names and desired locations in text format. The terminal's output is the request data sent to the server.

[0719] Step 2:

[0720] The server collects relevant historical data from a database based on the received data on the planned destination. The database contains data on past congestion levels, tourist attractions, and cultural and historical information for the destination. The server filters and aggregates this data to form a dataset for input into the generative AI model. The server's output is the prepared data for use by the generative AI model.

[0721] Step 3:

[0722] The server inputs collected historical data into a generation AI model, which automatically generates media content such as text, images, and audio. The generation AI model utilizes natural language processing techniques to output useful and relevant content for the user. The prompt used is "Generate tourist information about the planned destination." The server's output is the generated media content.

[0723] Step 4:

[0724] The server applies congestion prediction technology to forecast future congestion levels at the planned destination. By analyzing past congestion data and current conditions in real time, it models congestion patterns for the destination. Based on this information, the server calculates the optimal visit time and creates a suggested visit schedule for the user. The server's output is a schedule that includes the recommended visit time.

[0725] Step 5:

[0726] The emotion engine installed in the user's device activates, capturing the user's facial expressions with the camera and collecting audio with the microphone. The device analyzes this data in real time to infer the user's emotional state. For example, it analyzes whether the user is feeling stressed or expressing a desire to relax. This result is sent to the server and used to inform the next content selection. The output of the device is the user's emotional state data.

[0727] Step 6:

[0728] The server adjusts the media content and tourist information it provides based on emotional data transmitted from the emotion engine. It offers information in a way that resonates with the user's emotions, such as suggesting quiet locations if the user wants to relax. The server then constructs the final information package and sends it to the user's communication terminal. The server's output is customized tourist information adapted to the user's emotions.

[0729] (Application Example 2)

[0730] 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".

[0731] In modern tourism experiences, it is difficult to provide not just information, but also a personalized experience tailored to the user's emotions and circumstances. Tourists are easily affected by time and crowd conditions, and may experience stress and dissatisfaction, requiring effective methods to address these issues. Therefore, to improve the quality of tourism, there is a need to realize a system that can provide appropriate information and plans that respond to the user's emotions.

[0732] 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.

[0733] In this invention, the server includes means for receiving information on the user's planned destinations and collecting historical data related to those destinations; means for automatically generating media content such as text, images, and audio based on the historical data; means for predicting the congestion level of the planned destinations and calculating the optimal visit time; means for incorporating an engine for analyzing the user's emotional state and adjusting the media content based on the analysis results; and means for providing the generated media content and congestion prediction information to the user's communication device. This makes it possible to provide sightseeing plans tailored to the user's emotions and a comfortable experience by avoiding congestion.

[0734] "Information about planned destinations" refers to detailed information about places the user plans to visit, including the names of tourist attractions and the planned duration of stay.

[0735] "Historical data" refers to past records and information about the planned destination, including reviews from past visitors and weather information.

[0736] "Media content" refers to content in the form of text, images, audio, etc., that provides information about tourist destinations visually or audibly.

[0737] "Congestion forecasting" is a method for predicting the number of people and the level of crowding at a planned destination, and uses this information to determine the optimal time to visit.

[0738] The "emotional state analysis engine" is a function that analyzes the user's emotions through their facial expressions and voice, and adjusts content and suggestions based on that information.

[0739] A "communication device" refers to a device owned by the user, such as a smartphone or tablet, through which they can receive tourist information.

[0740] The system that realizes this invention consists of a server, a user's communication terminal, and an engine that analyzes emotional states. The server receives information about the user's planned visit locations and collects related historical data. Based on this historical data, it automatically generates media content such as text, images, and audio using an open-source generative AI model. Furthermore, the server predicts the congestion level of the planned visit locations and calculates an appropriate visit time.

[0741] The user's communication device is equipped with an emotion engine for real-time analysis of their emotional state. For example, it uses the Microsoft Azure Emotion API to analyze emotions through the user's facial expressions and voice. The results of this emotion analysis are used to adjust media content generated on the server to match the user's emotions.

[0742] Furthermore, the communication terminal receives and provides users with optimized media content and congestion forecast information. Specifically, when a user inputs information about their planned destination, the server generates content based on that information and makes optimal suggestions according to the results of sentiment analysis. For example, for a user experiencing stress, it might suggest visiting a quiet tourist spot or a relaxation facility.

[0743] For example, if a user experiences stress while sightseeing in Kyoto and is on their way to a crowded tourist spot, the emotion engine will detect this, and the server will suggest nearby quiet parks or temples. This kind of information provides the user with the best possible sightseeing experience.

[0744] By combining generative AI models with an emotion engine, it's possible to provide personalized travel experiences tailored to the user's emotions. An example of a prompt might be: "A traveler is feeling stressed while visiting a famous autumn foliage spot in Kyoto. Read their current emotions and suggest quieter tourist spots to avoid crowds."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server receives information about the planned destination from the user's communication terminal. The input is tourist destination information selected by the user, and the output is a query to a database based on that information. As part of data processing, the server collects historical data related to the planned destination.

[0748] Step 2:

[0749] The server automatically generates media content such as text, images, and audio using a generative AI model based on collected historical data. The input is historical data, and the output is the generated multimedia content. In this process, as a data computation, the information is combined by generative AI technology and transformed into a form that can be presented visually or aurally.

[0750] Step 3:

[0751] The server predicts congestion at a planned destination based on collected data and real-time traffic information, and calculates the optimal visit time. Inputs are historical data and current traffic information, and output is congestion prediction information. The appropriate visit time is calculated based on the congestion data predicted by the algorithm.

[0752] Step 4:

[0753] An emotion engine installed in the user's communication terminal analyzes the user's emotional state from their facial expressions and voice. The input is real-time data from the user via camera and microphone, and the output is emotional state data. An emotion recognition algorithm is used to process the data and quantify the emotion.

[0754] Step 5:

[0755] The server receives analysis results from the emotion engine and adjusts the media content it provides based on those results. The input is emotional state data, and the output is the adjusted media content. The server customizes the information to match the user's current emotions and enhances information about places where they can relax.

[0756] Step 6:

[0757] The server provides the user's communication terminal with adjusted media content and congestion forecast information. The input is adjusted content and congestion information, and the output is presented on the user interface. The user can use this information to make visits that align with their mood and travel plans.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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."

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] A means for receiving information on a user's planned destinations and collecting historical data related to those planned destinations,

[0782] A means for automatically generating media content such as text, images, and audio based on the aforementioned historical data,

[0783] A method for predicting congestion levels at a planned destination and calculating the optimal visit time,

[0784] A means for providing the generated media content and congestion prediction information to the user's communication terminal,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the communication terminal proposes additional information about the destination and a route to the user.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the system collects user feedback and improves the congestion prediction algorithm and the information provided.

[0790] "Example 1"

[0791] (Claim 1)

[0792] A means for receiving information about a user's planned visit location via a communication device and collecting historical information related to said planned visit location from a storage device,

[0793] A means for automatically generating information media such as text, still images, and audio data based on prompt instructions using generation AI technology, based on the aforementioned historical information.

[0794] A means to evaluate the optimal visit time by applying an algorithm to analyze the congestion status of the planned visit location,

[0795] The means for providing the generated information medium and congestion status information to a communication terminal,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the communication terminal proposes additional information about the destination and the route to the user.

[0799] (Claim 3)

[0800] The system according to claim 1, wherein the system collects user responses and improves the congestion analysis algorithm and the information provided.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means for receiving information on a user's planned destination and collecting data related to that planned destination,

[0804] A means for automatically generating media content based on the aforementioned data,

[0805] A method for predicting the conditions at a planned visit location and calculating the optimal visit time,

[0806] Means for providing the generated media content and predictive information to the user's terminal,

[0807] A means of presenting local information using a virtual guidance system,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the terminal proposes additional information and a route to the destination to the user, and enables trading of local specialty products in a virtual environment.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the system collects user responses, improves the prediction algorithm and provided information, and enhances the virtual guidance function.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] A means for receiving information on a user's planned destinations and collecting historical data related to those planned destinations,

[0816] A means for automatically generating media content such as text, images, and audio using natural language processing technology based on the aforementioned historical data,

[0817] A method for predicting congestion levels at a planned destination and calculating the optimal visit time,

[0818] A means of analyzing users' emotions in real time and adjusting the information provided based on the analysis results,

[0819] Means for providing the generated media content and congestion prediction information to the user's communication device,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, wherein the communication device proposes additional information regarding the place to visit and the route to visit to the user.

[0823] (Claim 3)

[0824] The system according to claim 1, wherein the system collects user opinions and improves the congestion prediction technology and the information provided.

[0825] "Application example 2 when combining with an emotional engine"

[0826] (Claim 1)

[0827] A means for receiving information on a user's planned destinations and collecting historical data related to those planned destinations,

[0828] A means for automatically generating media content such as text, images, and audio based on the aforementioned historical data,

[0829] A method for predicting congestion levels at a planned destination and calculating the optimal visit time,

[0830] It incorporates an engine for analyzing the emotional state of users and a means for adjusting media content based on the analysis results,

[0831] Means for providing the generated media content and congestion prediction information to the user's communication device,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the communication device proposes to the user additional information about the place to visit, as well as a route to visit and attractive nearby facilities.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the system collects feedback based on user sentiment data and adaptively improves the congestion prediction algorithm and the information provided. [Explanation of symbols]

[0837] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving information on a user's planned destination and collecting data related to that planned destination, A means for automatically generating media content based on the aforementioned data, A method for predicting the conditions at a planned visit location and calculating the optimal visit time, Means for providing the generated media content and predictive information to the user's terminal, A means of presenting local information using a virtual guidance system, A system that includes this.

2. The system according to claim 1, wherein the terminal proposes additional information and a route to the destination to the user, enabling the trading of local specialty products in a virtual environment.

3. The system according to claim 1, wherein the system collects user responses, improves the prediction algorithm and provided information, and enhances the virtual guidance function.

Images