system

Abstract

We provide the system. [Solution] A data processing method for analyzing the temporal aspects of human gatherings in tourist areas, A guidance suggestion means that calculates options to avoid the set based on the information collected by the aforementioned data processing means, An output means that displays information from the aforementioned guidance suggestion means as virtual reality on the user's mobile device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Congestion at tourist destinations is a factor that hinders a smooth and comfortable tourist experience for visitors. In particular, the congestion situation during peak hours restricts the movement to and actions during the stay at tourist destinations, causing problems such as stress and waste of time. Also, it is difficult to grasp the real-time congestion situation by conventional methods, and the means to obtain accurate information before the visit are limited. Therefore, there is a need for a new means to grasp the congestion situation in real time and propose appropriate alternative routes and visiting times to visitors.

Means for Solving the Problems

[0005] This invention provides an information processing means for analyzing congestion levels in tourist destinations in real time. This information processing means utilizes external sensor data and social network data to quickly analyze congestion levels. Furthermore, it includes a route suggestion means that calculates the optimal alternative route to avoid congestion based on the analyzed data. This enables optimized guidance tailored to the visitor's current location. The obtained information is visually displayed on the user's terminal using augmented reality, allowing visitors to avoid congestion in real time and enjoy a more comfortable and efficient sightseeing experience.

[0006] An "information processing device" is a device equipped with the function of receiving data necessary for analyzing the congestion situation at tourist destinations in real time, and analyzing that data to understand the occurrence of congestion.

[0007] A "route suggestion device" is a device that, based on analysis results obtained by an information processing device, calculates the optimal alternative route to avoid congestion within or to a tourist destination and proposes it to the user.

[0008] "Display means" refers to a device that has the function of visually displaying alternative route and visit time information generated by the route suggestion means on the user's terminal using augmented reality technology.

[0009] "Location information acquisition means" refers to a device that has the function of detecting the location of a device using technologies such as GPS in order to determine the user's current location.

[0010] "External data" is a general term for various types of data obtained from external sources, such as sensor data and social network data, that are necessary to understand the congestion levels at tourist destinations. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] As an embodiment of this invention, a system for providing a comfortable tourist experience is presented. Users access this system through a smartphone application.

[0033] First, the user launches the app and selects a tourist destination they want to visit. This information, along with the user's location data, is sent to the server.

[0034] Next, the server collects external data, such as sensor data and social network data, to understand the real-time congestion status of tourist destinations. This data is analyzed by AI algorithms to calculate the congestion status and predictions for each area.

[0035] The server then calculates the optimal alternative route and visit time based on the analysis results, and formats this information as AR content.

[0036] The device acquires this formatted data and integrates it with the user's camera feed in real time for display. This allows the user to visually check the route from their current location to their destination and the congestion levels at each point.

[0037] For example, if area X is predicted to be extremely crowded between 13:00 and 14:00 in tourist area A, the server calculates an alternative route to avoid area X and suggests visiting after 15:00. Upon receiving this information, the terminal provides route guidance overlaid on the user's camera view, helping them avoid congestion. In this way, tourists can sightsee efficiently while minimizing stress caused by congestion.

[0038] This system offers a new style of tourism, providing visitors with a better experience, and also supports sustainable operations in tourist destinations.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches the smartphone app, selects a tourist destination, and enters their visit plan. The device obtains the user's location information using GPS and sends this information to the server.

[0042] Step 2:

[0043] Based on the user's location information and planned destination information received by the server, external data (such as sensor data and social network data) regarding congestion levels at tourist destinations is collected. This allows for real-time congestion data to be obtained.

[0044] Step 3:

[0045] The server uses an AI model to analyze collected external data and predict current and future congestion levels in each area of ​​the tourist destination. Specifically, it uses statistical and machine learning models to identify peak congestion times and congested areas.

[0046] Step 4:

[0047] The server calculates the optimal alternative route and visit time to avoid congestion based on the analysis results. This calculation uses a congestion avoidance algorithm to generate an optimized route and suggested time for each user.

[0048] Step 5:

[0049] The server formats the calculated route information and visit time in augmented reality format and sends it to the terminal.

[0050] Step 6:

[0051] Based on the information received by the device, augmented reality (AR) is displayed on the user's smartphone camera. The user moves along the guided route while viewing the AR information displayed in real time. This allows for sightseeing while avoiding crowds.

[0052] Step 7:

[0053] Users provide feedback based on their experiences while sightseeing. The device sends user behavior information and performance data of selected routes to a server, which is used for future analysis and algorithm improvements.

[0054] (Example 1)

[0055] 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."

[0056] The stress and wasted time caused by overcrowding at tourist destinations are major problems for tourists. Furthermore, the concentration of visitors increases the burden on the environment and facilities of tourist destinations, potentially impacting their sustainability. A system is needed to address these challenges and provide tourists with a comfortable and efficient travel experience.

[0057] 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.

[0058] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, route suggestion means for calculating the optimal alternative route and visit time to avoid congestion based on the information obtained by the information processing means, display means for displaying the information from the route suggestion means as augmented reality on the user's device, and data analysis means for collecting external data, analyzing it using a generative AI model, and predicting congestion. This enables tourists to create optimal visit plans to avoid congestion, thereby promoting the efficient and sustainable use of tourist destinations.

[0059] "Information processing means" refers to the configuration of software and hardware for analyzing congestion levels in tourist areas in real time.

[0060] "Route suggestion means" refers to a technology that calculates the optimal alternative route and visit time to avoid congestion based on analysis results obtained by information processing means.

[0061] "Display means" refers to a function for visually displaying calculated route and visit time information as augmented reality on the user's device.

[0062] "Data analysis means" refers to the technology that collects external data, analyzes it using generated AI models, and performs congestion prediction.

[0063] This invention provides an information system to help tourists comfortably visit tourist destinations. Users launch an application on a mobile device such as a smartphone and select the tourist destination they wish to visit. Upon selection, the device sends the selection information, along with the user's current location, to a server. This process incorporates location tracking functions utilizing GPS sensors and Wi-Fi. Data transmission is securely performed using the HTTPS protocol.

[0064] The server collects data from sensor data and social networking services to understand the congestion levels of tourist destinations in real time. Specifically, it collects information related to tourist destinations using publicly available camera footage and social networking APIs. This collected data is analyzed using a generative AI model to predict congestion levels. TENSORFLOW®, a registered trademark, is used as an AI algorithm platform using Python for the analysis.

[0065] Based on the analysis results, the server calculates the optimal alternative route and visit time for the user. Route optimization algorithms such as the A algorithm and Dijkstra's algorithm are used for this calculation. The calculated information is then formatted as augmented reality content using Unity or ARKit.

[0066] The device integrates augmented reality content received from the server into the camera view and displays it to the user. This allows users to visually check real-time congestion levels and recommended routes at tourist destinations using their own devices. An example of a prompt message might be, "Based on the current congestion levels at the tourist destination, please tell me the best route and visiting time." This system allows users to enjoy sightseeing efficiently and reduces stress caused by congestion.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The user launches a smartphone application and selects a tourist destination they wish to visit. Based on the user's selection, the app retrieves the user's current location information, including GPS coordinates and time. The retrieved data is sent to the server in JSON format. This process provides the basic information needed to plan the user's intended visit to the tourist destination.

[0070] Step 2:

[0071] The server collects real-time congestion data based on the user's current location information and tourist destination information received. The server aggregates external sensor data (e.g., traffic camera footage) and data from social networking platforms (e.g., public posts). This data serves as the starting point for analyzing congestion levels that affect users' visits to tourist destinations.

[0072] Step 3:

[0073] The server analyzes the collected external data using a generating AI model. Specifically, it applies an AI algorithm (for example, a neural network model using TensorFlow) to predict current and future congestion levels in tourist destinations. The results of this analysis are output as an indicator showing congestion levels during the times when tourists are likely to visit.

[0074] Step 4:

[0075] The server calculates the optimal route and time based on congestion prediction data. Using Dijkstra and A algorithms, it proposes a traffic-avoiding route to the user's destination. This calculation takes into account current traffic conditions and predicted crowd levels. The results are then formatted as AR content.

[0076] Step 5:

[0077] The device integrates AR content received from the server with the camera image in real time. Users can visually check the route from their current location to their destination and the congestion status through the device's screen. This process enables users to enjoy a comfortable and efficient sightseeing experience.

[0078] (Application Example 1)

[0079] 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."

[0080] Overcrowding in tourist destinations is a factor that hinders the visitor experience and also negatively impacts the management of local resources. This invention aims to provide a comfortable tourist experience by helping visitors avoid congestion when visiting tourist destinations.

[0081] 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.

[0082] In this invention, the server includes data processing means for analyzing the crowd situation in a tourist area over time, guidance suggestion means for calculating options to avoid crowds based on the information collected by the data processing means, and output means for displaying the information from the guidance suggestion means as virtual reality on the user's mobile terminal. This makes it possible to avoid expected crowds when visiting a tourist destination, enabling an efficient and comfortable sightseeing experience.

[0083] A "tourist area" refers to a specific region or place where visitors gather for sightseeing purposes.

[0084] "People gathering situation" refers to information that shows the number and density of people gathered at a tourist destination at a specific time.

[0085] "Data processing means" refers to devices and methods for collecting and analyzing various types of information to generate useful information.

[0086] A "guidance suggestion method" refers to a system or algorithm that suggests the optimal route and visit time to a user based on collected data.

[0087] "Virtual reality" refers to visual information generated using computer technology and integrated with the real environment.

[0088] "User's mobile device" refers to portable information and communication devices such as smartphones and tablets.

[0089] "Location data acquisition means" refers to technologies and devices that use GPS or similar systems to determine the user's current location.

[0090] "External information" refers to data obtained from outside the system, including sensor data and social network data.

[0091] To implement this invention, the user first uses a dedicated application on their mobile device. The user selects a tourist destination they wish to visit and sends that information to the server. The server collects external information from various sensors installed in the tourist destination area and from social networking platforms, and uses this information to analyze the crowd situation. Data analysis software such as Python or R can be used for data processing.

[0092] Next, the server uses the analyzed data to calculate the optimal route and visit time to guide users to avoid crowded areas. AI algorithms are used for these calculations, and machine learning libraries such as TensorFlow and PyTorch can be utilized.

[0093] The information selected by the guidance suggestion system is displayed as virtual reality on the user's mobile device using augmented reality (AR). AR development platforms such as Unity and ARKit are used for this. Based on this, the user can visually confirm the route from their current location to their destination.

[0094] As a concrete example, when a user plans a "day tour of the city" by operating the interface, the system suggests avoiding crowded museums in the morning and relaxing at a cafe, then displays a route to visit tourist attractions from midday onwards.

[0095] Examples of prompts to input into a generative AI model:

[0096] "Propose a tourist information system that provides congestion predictions and optimal routes when a user selects a tourist destination."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The user launches the application on their mobile device and selects a tourist destination they wish to visit. The input consists of the user's selected tourist destination information and their current location, which is then sent from the device to the server. The output is the user information received by the server.

[0100] Step 2:

[0101] The server collects external information from sensors installed in tourist areas and from social media, and analyzes the crowd situation. The input consists of real-time data from sensors and social media data. This data is analyzed using Python, R, or other languages ​​to predict congestion. The output is crowd prediction information for each area of ​​the tourist destination.

[0102] Step 3:

[0103] The server calculates the optimal route and visit time to avoid congestion based on the analysis results. The inputs are crowd prediction information and user selection information. An AI algorithm is used to generate the optimal route to the destination. The output is alternative route guidance information.

[0104] Step 4:

[0105] The server formats the generated guidance information as AR content. The input is alternative route guidance information, which is visualized using tools such as ARKit or Unity. The output is formatted AR data.

[0106] Step 5:

[0107] The device uses the received AR data and, while taking the user's current location into consideration, displays virtual reality overlaid on the camera image. The input consists of formatted AR data and the user's location information. The output is a virtual reality representation visually presented to the user.

[0108] 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.

[0109] As an embodiment of this invention, a tourist information system incorporating an emotion engine is described to provide tourists with an immersive sightseeing experience. Users can use this system on smartphones or wearable devices.

[0110] First, the user launches the application on their device and enters the tourist destinations they want to visit and the experiences they wish to have. Based on this information, the device uses GPS to determine the user's current location and sends that information to the server.

[0111] Next, the server collects sensor data and social network data from tourist destinations and analyzes congestion levels in real time. This includes a process that uses AI and statistical analysis to predict congestion and understand pedestrian flow in each area.

[0112] Furthermore, an emotion engine built into the user's device uses cameras and other sensors to measure the user's emotional state from their facial expressions and actions, acquiring this data in real time. The emotion engine understands the user's stress level and preferred situations, and provides feedback to the system.

[0113] The server combines acquired sentiment data and congestion data to generate optimal route suggestions. These suggestions include providing personalized guidance, such as locations where the user can relax more or routes that avoid unexpected congestion. For example, if a user is feeling stressed in a particular area, the sentiment engine's data will suggest moving to a different area or taking a short break.

[0114] Ultimately, the device receives information transmitted from the server in an augmented reality format and displays it overlaid on the user's smartphone camera. This allows the user to receive a personalized sightseeing experience on the spot, increasing their satisfaction with the experience.

[0115] This system enables flexible guidance that takes into account the emotions of visitors in tourist destinations, thereby providing a deeper and more valuable experience. This embodiment contributes to the creation of new added value in the tourism sector.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The user launches an app on their smartphone or wearable device, selects a tourist destination, and enters their purpose of visit and desired experiences. The device obtains this information and their current location via GPS and sends it to the server.

[0119] Step 2:

[0120] The server receives information from users and collects real-time data from tourist destinations. This data includes sensor data, traffic data, and social media trend information, and is used to understand congestion levels.

[0121] Step 3:

[0122] The server uses AI algorithms to analyze collected real-time data and predict congestion at each tourist spot. This prediction evaluates peak congestion times and pedestrian flow to forecast future conditions.

[0123] Step 4:

[0124] The device's built-in emotion engine analyzes the user's emotional state from their facial expressions, heart rate, and voice. This emotional data is used to assess the user's current emotional comfort level and stress level.

[0125] Step 5:

[0126] The server receives data from the emotion engine and analyzes it along with congestion levels. Based on the user's emotional state, the server re-evaluates personalized routes and destinations to generate an optimized sightseeing plan.

[0127] Step 6:

[0128] The server generates a personalized sightseeing plan and sends it to the user's device. This plan includes recommended routes and activity information tailored to the user's preferences.

[0129] Step 7:

[0130] The device presents the received information to the user as augmented reality (AR) visualization data. This allows the user to proceed with sightseeing while viewing real-time guidance displayed on video.

[0131] Step 8:

[0132] As users progress through the tour following the guide, their device constantly operates an emotion engine, sending feedback to the server in real time. This dynamically adjusts the tour plan, improving the user experience.

[0133] (Example 2)

[0134] 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".

[0135] Conventional tourist information systems typically suggest routes that take into account the congestion levels of tourist destinations, but they have struggled to provide an optimized tourist experience that takes into account the emotional state and preferences of individual users. As a result, users may not be able to have a relaxing tourist experience, and the quality of the experience does not improve.

[0136] 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.

[0137] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, emotion analysis means for measuring the user's emotional state and optimizing the tourist experience, and route suggestion means for calculating and proposing personalized routes. This makes it possible to propose guidelines that take the user's emotions into consideration, and to provide a tourist experience tailored to individual needs.

[0138] An "information processing means" is a component that has the function of collecting and analyzing multiple data points in a tourist destination in real time to understand the congestion situation.

[0139] "Emotion analysis means" refers to a component that analyzes emotional information acquired through the user's device and generates information to provide the user with the optimal tourist experience.

[0140] A "route suggestion means" is a component that has the function of calculating and suggesting a route and destination optimized for the user based on acquired congestion status and sentiment information.

[0141] "Display means" refers to a component that has the function of displaying information generated from the route suggestion means on the user's terminal in augmented reality format and providing visual guidance.

[0142] A "generative AI model" is a model that applies artificial intelligence technology to generate user-friendly suggestions and prompts from data.

[0143] One embodiment of this invention is a system that provides users with personalized tourism experiences. This system consists of a user's device, a server, and multiple software components.

[0144] First, the user launches a dedicated application on their smartphone or wearable device and enters the tourist destinations they want to visit and the experiences they desire. This device is equipped with GPS to obtain location information.

[0145] Next, the device acquires the user's input information and GPS location data and sends it to the server. The device uses the Google® Maps API to provide accurate location information. The device is also equipped with sensors such as a camera and microphone to collect data for sentiment analysis.

[0146] The server possesses powerful information processing capabilities to collect external data related to tourist destinations and analyze congestion levels and user-related information in real time. This includes sensor data and social network data, which are analyzed using machine learning algorithms. In this process, the server utilizes Apache® Hadoop and the Python Pandas library.

[0147] Next, the emotion analysis system analyzes the user's facial expressions and voice in real time through the user's device to understand the user's emotional state. Here, an emotion analysis API is used to measure the user's stress level and emotions.

[0148] The generative AI model uses this data to generate routes and sightseeing plans suitable for the user and creates prompt messages. An example of a prompt message is, "Please tell me about quiet places in Kyoto."

[0149] Finally, the device receives optimal route information sent from the server and displays it in the user's field of view using augmented reality technology. This is achieved using ARKit or ARCore. As a result, users receive real-time optimized sightseeing guidance and can have a more personalized and better sightseeing experience.

[0150] As a concrete example, if a user experiences stress in a certain area of ​​Tokyo, the system uses that data to immediately suggest moving to another area where they can relax. The user can visually confirm their next action through the device, allowing them to continue a comfortable sightseeing experience. Thus, the present invention aims to improve the quality of the sightseeing experience by providing a flexible system that takes into account the user's emotions and circumstances.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] Users launch a dedicated application on their smartphone or wearable device and input the tourist destinations they wish to visit and the experiences they desire. This input includes location information and details of the desired experiences. As output, the user's current location information, along with the input information, is obtained through location services.

[0154] Step 2:

[0155] The device sends user-entered information and GPS location data to the server. In this step, the Google Maps API is used to process the precise location data and provide the server with structured data as output.

[0156] Step 3:

[0157] The server collects and analyzes external data related to tourist destinations based on data received from terminals. This data processing includes sensor data and social network data. AI algorithms and statistical analysis tools are used to predict congestion levels and user area characteristics. Analysis results are generated as output.

[0158] Step 4:

[0159] The device's built-in emotion analysis system collects user emotion data through the camera and microphone. Specifically, it analyzes the user's facial expressions and tone of voice to infer their emotional state. An emotion recognition API is used for this process, and user emotion information is generated as output.

[0160] Step 5:

[0161] The server integrates emotional data with analyzed external data and uses a generative AI model to create personalized sightseeing routes and experience plans. Data processing involves complex calculations that consider user emotions and congestion levels. The final output is a route suggestion and prompt message best suited to the user. An example of a prompt message might be, "Please tell me about quiet places in Kyoto."

[0162] Step 6:

[0163] The device receives output information from the server and displays it in the user's field of view using augmented reality (AR) technology. Specifically, ARKit and ARCore are utilized, and the user receives visual guidance through the device. As output, AR display information is provided overlaid on the user's device. As a result, the user can receive real-time optimized tourist information.

[0164] (Application Example 2)

[0165] 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".

[0166] Traditional tourism systems and food delivery services have struggled to provide personalized suggestions based on users' emotional states and real-time congestion levels. This can lead to users not receiving the expected experience and experiencing decreased satisfaction. Furthermore, visiting crowded areas or stores can cause stress. Therefore, there is a need for a system that can simultaneously avoid congestion and provide the optimal experience while considering the user's emotional state.

[0167] 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.

[0168] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations and restaurants in real time, emotion recognition means for analyzing the emotional state of users, and customization suggestion means for proposing the most suitable stores and experiences to users. This makes it possible to provide individually optimized tourist and dining experiences based on the emotional state of users and congestion information.

[0169] "Information processing means" refers to devices or software that perform calculations to acquire and analyze the congestion status of tourist destinations and restaurants in real time.

[0170] A "route suggestion means" is a device or algorithm that calculates and suggests alternative routes to help users avoid congestion based on acquired congestion information.

[0171] "Emotion recognition means" refers to devices or software that have the function of understanding a user's emotional state by analyzing the user's facial expressions and movements.

[0172] A "customized suggestion method" refers to a device or system that individually suggests optimal dining facilities or tourist experiences based on user sentiment data and real-time information.

[0173] "Display means" refers to functions or devices for visually displaying proposed information as augmented reality on a user device.

[0174] "Location information acquisition means" refers to devices or technologies that have the function of identifying the user's current location and providing it to other systems.

[0175] "External information" refers to information collected from outside the system, such as sensor data and evaluation data on online platforms.

[0176] The system implementing this invention analyzes the congestion status of tourist destinations and restaurants in real time and provides an optimal experience that takes into account the user's emotional state. The system consists of information processing means, route suggestion means, emotion recognition means, customization suggestion means, display means, and location information acquisition means.

[0177] The server acquires sensor data from tourist destinations and restaurants, as well as rating data from social networks, in real time, and analyzes congestion levels using information processing tools. The server processes this data using AI tools and statistical analysis software (e.g., TensorFlow, SciPy).

[0178] Users acquire emotional data by using the cameras of their smartphones or wearable devices as a means of emotion recognition, analyzing their facial expressions and movements. This is where an emotion engine (e.g., Affectiva) comes into play.

[0179] The on-device display provides route suggestions and customized experiences received from the server in augmented reality format, presenting the user with the optimal route and experience. This allows users to avoid congestion and enjoy a personalized experience.

[0180] For example, a user might launch an application, and the emotion engine analyzes their facial expression, determining that they are seeking relaxation. In that case, the server might suggest a less crowded restaurant and recommend relaxing drinks and food.

[0181] Examples of prompts for a generative AI model are as follows:

[0182] "Please provide image data of the user's face. Next, use an emotion engine to predict what the user currently wants (e.g., relax, refresh), and based on that prediction, identify available restaurants in the current area and suggest dishes best suited for relaxation."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The user initiates the application on the device. The user inputs desired tourist destinations and experiences. This information, along with location data, is sent to the server by the device. In this process, input is the desired destinations and experiences, and output is the transmission of data to the server.

[0186] Step 2:

[0187] The server uses location information acquisition means to determine the user's current location. The identified location information, along with sensor data from tourist attractions and restaurants, and rating data from social networks, are acquired in real time. This data is analyzed by information processing means to evaluate congestion levels in real time. Inputs are location data and external data, and output is real-time congestion information.

[0188] Step 3:

[0189] The device utilizes emotion recognition to capture the user's facial expressions and actions with a camera, and analyzes the user's emotional state using an emotion engine. This process converts image data into emotion data and sends it to a server. The input is image data, and the output is emotional state data.

[0190] Step 4:

[0191] The server integrates sentiment data and congestion data, and uses route suggestion and customization suggestion means to calculate the optimal alternative route and experience for the user. AI and statistical analysis software are used to process this data and generate personalized suggestions for each user. The input is sentiment data and congestion data, and the output is suggestion information.

[0192] Step 5:

[0193] The device receives suggestion information from the server and displays it to the user in augmented reality format. The device provides visually rich information to enhance the user experience and support specific actions during travel. The input is suggestion information, and the output is an augmented reality display.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] As an embodiment of this invention, a system for providing a comfortable tourist experience is presented. Users access this system through a smartphone application.

[0211] First, the user launches the app and selects a tourist destination they want to visit. This information, along with the user's location data, is sent to the server.

[0212] Next, the server collects external data, such as sensor data and social network data, to understand the real-time congestion status of tourist destinations. This data is analyzed by AI algorithms to calculate the congestion status and predictions for each area.

[0213] The server then calculates the optimal alternative route and visit time based on the analysis results, and formats this information as AR content.

[0214] The device acquires this formatted data and integrates it with the user's camera feed in real time for display. This allows the user to visually check the route from their current location to their destination and the congestion levels at each point.

[0215] For example, if area X is predicted to be extremely crowded between 13:00 and 14:00 in tourist area A, the server calculates an alternative route to avoid area X and suggests visiting after 15:00. Upon receiving this information, the terminal provides route guidance overlaid on the user's camera view, helping them avoid congestion. In this way, tourists can sightsee efficiently while minimizing stress caused by congestion.

[0216] This system offers a new style of tourism, providing visitors with a better experience, and also supports sustainable operations in tourist destinations.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user launches the smartphone app, selects a tourist destination, and enters their visit plan. The device obtains the user's location information using GPS and sends this information to the server.

[0220] Step 2:

[0221] Based on the user's location information and planned destination information received by the server, external data (such as sensor data and social network data) regarding congestion levels at tourist destinations is collected. This allows for real-time congestion data to be obtained.

[0222] Step 3:

[0223] The server uses an AI model to analyze collected external data and predict current and future congestion levels in each area of ​​the tourist destination. Specifically, it uses statistical and machine learning models to identify peak congestion times and congested areas.

[0224] Step 4:

[0225] The server calculates the optimal alternative route and visit time to avoid congestion based on the analysis results. This calculation uses a congestion avoidance algorithm to generate an optimized route and suggested time for each user.

[0226] Step 5:

[0227] The server formats the calculated route information and visit time in augmented reality format and sends it to the terminal.

[0228] Step 6:

[0229] Based on the information received by the device, augmented reality (AR) is displayed on the user's smartphone camera. The user moves along the guided route while viewing the AR information displayed in real time. This allows for sightseeing while avoiding crowds.

[0230] Step 7:

[0231] Users provide feedback based on their experiences while sightseeing. The device sends user behavior information and performance data of selected routes to a server, which is used for future analysis and algorithm improvements.

[0232] (Example 1)

[0233] 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."

[0234] The stress and wasted time caused by overcrowding at tourist destinations are major problems for tourists. Furthermore, the concentration of visitors increases the burden on the environment and facilities of tourist destinations, potentially impacting their sustainability. A system is needed to address these challenges and provide tourists with a comfortable and efficient travel experience.

[0235] 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.

[0236] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, route suggestion means for calculating the optimal alternative route and visit time to avoid congestion based on the information obtained by the information processing means, display means for displaying the information from the route suggestion means as augmented reality on the user's device, and data analysis means for collecting external data, analyzing it using a generative AI model, and predicting congestion. This enables tourists to create optimal visit plans to avoid congestion, thereby promoting the efficient and sustainable use of tourist destinations.

[0237] "Information processing means" refers to the configuration of software and hardware for analyzing congestion levels in tourist areas in real time.

[0238] "Route suggestion means" refers to a technology that calculates the optimal alternative route and visit time to avoid congestion based on analysis results obtained by information processing means.

[0239] "Display means" refers to a function for visually displaying calculated route and visit time information as augmented reality on the user's device.

[0240] "Data analysis means" refers to the technology that collects external data, analyzes it using generated AI models, and performs congestion prediction.

[0241] This invention provides an information system to help tourists comfortably visit tourist destinations. Users launch an application on a mobile device such as a smartphone and select the tourist destination they wish to visit. Upon selection, the device sends the selection information, along with the user's current location, to a server. This process incorporates location tracking functions utilizing GPS sensors and Wi-Fi. Data transmission is securely performed using the HTTPS protocol.

[0242] The server collects data from sensor data and social networking services to understand the congestion levels of tourist destinations in real time. Specifically, it collects information related to tourist destinations using publicly available camera footage and social networking APIs. This collected data is analyzed using a generative AI model to predict congestion levels. TensorFlow is used as the AI ​​algorithm platform, which is based on Python, for the analysis.

[0243] Based on the analysis results, the server calculates the optimal alternative route and visit time for the user. Route optimization algorithms such as the A algorithm and Dijkstra's algorithm are used for this calculation. The calculated information is then formatted as augmented reality content using Unity or ARKit.

[0244] The device integrates augmented reality content received from the server into the camera view and displays it to the user. This allows users to visually check real-time congestion levels and recommended routes at tourist destinations using their own devices. An example of a prompt message might be, "Based on the current congestion levels at the tourist destination, please tell me the best route and visiting time." This system allows users to enjoy sightseeing efficiently and reduces stress caused by congestion.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The user launches a smartphone application and selects a tourist destination they wish to visit. Based on the user's selection, the app retrieves the user's current location information, including GPS coordinates and time. The retrieved data is sent to the server in JSON format. This process provides the basic information needed to plan the user's intended visit to the tourist destination.

[0248] Step 2:

[0249] The server collects real-time congestion data based on the user's current location information and tourist destination information received. The server aggregates external sensor data (e.g., traffic camera footage) and data from social networking platforms (e.g., public posts). This data serves as the starting point for analyzing congestion levels that affect users' visits to tourist destinations.

[0250] Step 3:

[0251] The server analyzes the collected external data using a generating AI model. Specifically, it applies an AI algorithm (for example, a neural network model using TensorFlow) to predict current and future congestion levels in tourist destinations. The results of this analysis are output as an indicator showing congestion levels during the times when tourists are likely to visit.

[0252] Step 4:

[0253] The server calculates the optimal route and time based on congestion prediction data. Using Dijkstra and A algorithms, it proposes a traffic-avoiding route to the user's destination. This calculation takes into account current traffic conditions and predicted crowd levels. The results are then formatted as AR content.

[0254] Step 5:

[0255] The device integrates AR content received from the server with the camera image in real time. Users can visually check the route from their current location to their destination and the congestion status through the device's screen. This process enables users to enjoy a comfortable and efficient sightseeing experience.

[0256] (Application Example 1)

[0257] 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."

[0258] Overcrowding in tourist destinations is a factor that hinders the visitor experience and also negatively impacts the management of local resources. This invention aims to provide a comfortable tourist experience by helping visitors avoid congestion when visiting tourist destinations.

[0259] 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.

[0260] In this invention, the server includes data processing means for analyzing the crowd situation in a tourist area over time, guidance suggestion means for calculating options to avoid crowds based on the information collected by the data processing means, and output means for displaying the information from the guidance suggestion means as virtual reality on the user's mobile terminal. This makes it possible to avoid expected crowds when visiting a tourist destination, enabling an efficient and comfortable sightseeing experience.

[0261] A "tourist area" refers to a specific region or place where visitors gather for sightseeing purposes.

[0262] "People gathering situation" refers to information that shows the number and density of people gathered at a tourist destination at a specific time.

[0263] "Data processing means" refers to devices and methods for collecting and analyzing various types of information to generate useful information.

[0264] A "guidance suggestion method" refers to a system or algorithm that suggests the optimal route and visit time to a user based on collected data.

[0265] "Virtual reality" refers to visual information generated using computer technology and integrated with the real environment.

[0266] "User's mobile device" refers to portable information and communication devices such as smartphones and tablets.

[0267] "Location data acquisition means" refers to technologies and devices that use GPS or similar systems to determine the user's current location.

[0268] "External information" refers to data obtained from outside the system, including sensor data and social network data.

[0269] To implement this invention, the user first uses a dedicated application on their mobile device. The user selects a tourist destination they wish to visit and sends that information to the server. The server collects external information from various sensors installed in the tourist destination area and from social networking platforms, and uses this information to analyze the crowd situation. Data analysis software such as Python or R can be used for data processing.

[0270] Next, the server uses the analyzed data to calculate the optimal route and visit time to guide users to avoid crowded areas. AI algorithms are used for these calculations, and machine learning libraries such as TensorFlow and PyTorch can be utilized.

[0271] The information selected by the guidance suggestion system is displayed as virtual reality on the user's mobile device using augmented reality (AR). AR development platforms such as Unity and ARKit are used for this. Based on this, the user can visually confirm the route from their current location to their destination.

[0272] As a concrete example, when a user plans a "day tour of the city" by operating the interface, the system suggests avoiding crowded museums in the morning and relaxing at a cafe, then displays a route to visit tourist attractions from midday onwards.

[0273] Examples of prompts to input into a generative AI model:

[0274] "Propose a tourist information system that provides congestion predictions and optimal routes when a user selects a tourist destination."

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The user launches the application on their mobile device and selects a tourist destination they wish to visit. The input consists of the user's selected tourist destination information and their current location, which is then sent from the device to the server. The output is the user information received by the server.

[0278] Step 2:

[0279] The server collects external information from sensors installed in tourist areas and from social media, and analyzes the crowd situation. The input consists of real-time data from sensors and social media data. This data is analyzed using Python, R, or other languages ​​to predict congestion. The output is crowd prediction information for each area of ​​the tourist destination.

[0280] Step 3:

[0281] The server calculates the optimal route and travel time to avoid congestion based on the analysis results. The inputs are the collective prediction information and the user's selection information. An AI algorithm is used to generate the optimal route to the destination. The output is the alternative route guidance information.

[0282] Step 4:

[0283] The server formats the generated guidance information as AR content. The input is the alternative route guidance information, which is visualized using tools such as ARKit or Unity. The output is the formatted AR data.

[0284] Step 5:

[0285] The terminal uses the received AR data and displays virtual reality overlaid on the camera image while considering the user's current location information. The inputs are the formatted AR data and the user's location information. The output is the virtual reality representation visually provided to the user.

[0286] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0287] As an embodiment of implementing this invention, a tourist guide system incorporating an emotion engine will be described to provide an immersive tourist experience for tourists. The user can use this system on a smartphone or wearable device.

[0288] First, the user launches the application on the device and inputs the tourist destination to visit and the experience desired. Based on this information, the terminal uses GPS to confirm the user's current location and transmits that information to the server.

[0289] Next, the server collects sensor data and social network data from tourist destinations and analyzes congestion levels in real time. This includes a process that uses AI and statistical analysis to predict congestion and understand pedestrian flow in each area.

[0290] Furthermore, an emotion engine built into the user's device uses cameras and other sensors to measure the user's emotional state from their facial expressions and actions, acquiring this data in real time. The emotion engine understands the user's stress level and preferred situations, and provides feedback to the system.

[0291] The server combines acquired sentiment data and congestion data to generate optimal route suggestions. These suggestions include providing personalized guidance, such as locations where the user can relax more or routes that avoid unexpected congestion. For example, if a user is feeling stressed in a particular area, the sentiment engine's data will suggest moving to a different area or taking a short break.

[0292] Ultimately, the device receives information transmitted from the server in an augmented reality format and displays it overlaid on the user's smartphone camera. This allows the user to receive a personalized sightseeing experience on the spot, increasing their satisfaction with the experience.

[0293] This system enables flexible guidance that takes into account the emotions of visitors in tourist destinations, thereby providing a deeper and more valuable experience. This embodiment contributes to the creation of new added value in the tourism sector.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The user launches an app on their smartphone or wearable device, selects a tourist destination, and enters their purpose of visit and desired experiences. The device obtains this information and their current location via GPS and sends it to the server.

[0297] Step 2:

[0298] The server receives information from the user and collects real-time data at tourist destinations. This data includes sensor data, traffic data, and SNS trend information, and is used to grasp the congestion situation.

[0299] Step 3:

[0300] The server uses an AI algorithm to analyze the collected real-time data and predict the congestion of each tourist spot. This prediction evaluates the peak time of congestion and the movement of the flow of people, and speculates on the future situation.

[0301] Step 4:

[0302] The emotion engine installed on the terminal analyzes the user's emotional state from the user's expression, heartbeat, and voice. These emotion data are used to evaluate the user's current emotional comfort level and stress level.

[0303] Step 5:

[0304] The server receives the data from the emotion engine and performs analysis in combination with the congestion situation. Based on the user's emotional state, the server re-evaluates the individualized routes and destinations, and generates an optimized tourism plan.

[0305] Step 6:

[0306] The server transmits the individualized tourism plan generated to the terminal. This plan includes recommended routes and activity information corresponding to the user's emotions.

[0307] Step 7:

[0308] The device presents the received information to the user as augmented reality (AR) visualization data. This allows the user to proceed with sightseeing while viewing real-time guidance displayed on video.

[0309] Step 8:

[0310] As users progress through the tour following the guide, their device constantly operates an emotion engine, sending feedback to the server in real time. This dynamically adjusts the tour plan, improving the user experience.

[0311] (Example 2)

[0312] 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".

[0313] Conventional tourist information systems typically suggest routes that take into account the congestion levels of tourist destinations, but they have struggled to provide an optimized tourist experience that takes into account the emotional state and preferences of individual users. As a result, users may not be able to have a relaxing tourist experience, and the quality of the experience does not improve.

[0314] 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.

[0315] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, emotion analysis means for measuring the user's emotional state and optimizing the tourist experience, and route suggestion means for calculating and proposing personalized routes. This makes it possible to propose guidelines that take the user's emotions into consideration, and to provide a tourist experience tailored to individual needs.

[0316] An "information processing means" is a component that has the function of collecting and analyzing multiple data points in a tourist destination in real time to understand the congestion situation.

[0317] "Emotion analysis means" refers to a component that analyzes emotional information acquired through the user's device and generates information to provide the user with the optimal tourist experience.

[0318] A "route suggestion means" is a component that has the function of calculating and suggesting a route and destination optimized for the user based on acquired congestion status and sentiment information.

[0319] "Display means" refers to a component that has the function of displaying information generated from the route suggestion means on the user's terminal in augmented reality format and providing visual guidance.

[0320] A "generative AI model" is a model that applies artificial intelligence technology to generate user-friendly suggestions and prompts from data.

[0321] One embodiment of this invention is a system that provides users with personalized tourism experiences. This system consists of a user's device, a server, and multiple software components.

[0322] First, the user launches a dedicated application on their smartphone or wearable device and enters the tourist destinations they want to visit and the experiences they desire. This device is equipped with GPS to obtain location information.

[0323] Next, the device acquires the user's input information and GPS location data and sends it to the server. The device uses the Google Maps API to provide accurate location information. The device is also equipped with sensors such as a camera and microphone to collect data for sentiment analysis.

[0324] The server possesses powerful information processing capabilities to collect external data related to tourist destinations and analyze congestion levels and user-related information in real time. This includes sensor data and social network data, which are analyzed using machine learning algorithms. In this process, the server utilizes Apache Hadoop and the Python Pandas library.

[0325] Next, the emotion analysis system analyzes the user's facial expressions and voice in real time through the user's device to understand the user's emotional state. Here, an emotion analysis API is used to measure the user's stress level and emotions.

[0326] The generative AI model uses this data to generate routes and sightseeing plans suitable for the user and creates prompt messages. An example of a prompt message is, "Please tell me about quiet places in Kyoto."

[0327] Finally, the device receives optimal route information sent from the server and displays it in the user's field of view using augmented reality technology. This is achieved using ARKit or ARCore. As a result, users receive real-time optimized sightseeing guidance and can have a more personalized and better sightseeing experience.

[0328] As a concrete example, if a user experiences stress in a certain area of ​​Tokyo, the system uses that data to immediately suggest moving to another area where they can relax. The user can visually confirm their next action through the device, allowing them to continue a comfortable sightseeing experience. Thus, the present invention aims to improve the quality of the sightseeing experience by providing a flexible system that takes into account the user's emotions and circumstances.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] Users launch a dedicated application on their smartphone or wearable device and input the tourist destinations they wish to visit and the experiences they desire. This input includes location information and details of the desired experiences. As output, the user's current location information, along with the input information, is obtained through location services.

[0332] Step 2:

[0333] The device sends user-entered information and GPS location data to the server. In this step, the Google Maps API is used to process the precise location data and provide the server with structured data as output.

[0334] Step 3:

[0335] The server collects and analyzes external data related to tourist destinations based on data received from terminals. This data processing includes sensor data and social network data. AI algorithms and statistical analysis tools are used to predict congestion levels and user area characteristics. Analysis results are generated as output.

[0336] Step 4:

[0337] The device's built-in emotion analysis system collects user emotion data through the camera and microphone. Specifically, it analyzes the user's facial expressions and tone of voice to infer their emotional state. An emotion recognition API is used for this process, and user emotion information is generated as output.

[0338] Step 5:

[0339] The server integrates emotional data with analyzed external data and uses a generative AI model to create personalized sightseeing routes and experience plans. Data processing involves complex calculations that consider user emotions and congestion levels. The final output is a route suggestion and prompt message best suited to the user. An example of a prompt message might be, "Please tell me about quiet places in Kyoto."

[0340] Step 6:

[0341] The device receives output information from the server and displays it in the user's field of view using augmented reality (AR) technology. Specifically, ARKit and ARCore are utilized, and the user receives visual guidance through the device. As output, AR display information is provided overlaid on the user's device. As a result, the user can receive real-time optimized tourist information.

[0342] (Application Example 2)

[0343] 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."

[0344] Traditional tourism systems and food delivery services have struggled to provide personalized suggestions based on users' emotional states and real-time congestion levels. This can lead to users not receiving the expected experience and experiencing decreased satisfaction. Furthermore, visiting crowded areas or stores can cause stress. Therefore, there is a need for a system that can simultaneously avoid congestion and provide the optimal experience while considering the user's emotional state.

[0345] 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.

[0346] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations and restaurants in real time, emotion recognition means for analyzing the emotional state of users, and customization suggestion means for proposing the most suitable stores and experiences to users. This makes it possible to provide individually optimized tourist and dining experiences based on the emotional state of users and congestion information.

[0347] "Information processing means" refers to devices or software that perform calculations to acquire and analyze the congestion status of tourist destinations and restaurants in real time.

[0348] A "route suggestion means" is a device or algorithm that calculates and suggests alternative routes to help users avoid congestion based on acquired congestion information.

[0349] "Emotion recognition means" refers to devices or software that have the function of understanding a user's emotional state by analyzing the user's facial expressions and movements.

[0350] A "customized suggestion method" refers to a device or system that individually suggests optimal dining facilities or tourist experiences based on user sentiment data and real-time information.

[0351] "Display means" refers to functions or devices for visually displaying proposed information as augmented reality on a user device.

[0352] "Location information acquisition means" refers to devices or technologies that have the function of identifying the user's current location and providing it to other systems.

[0353] "External information" refers to information collected from outside the system, such as sensor data and evaluation data on online platforms.

[0354] The system implementing this invention analyzes the congestion status of tourist destinations and restaurants in real time and provides an optimal experience that takes into account the user's emotional state. The system consists of information processing means, route suggestion means, emotion recognition means, customization suggestion means, display means, and location information acquisition means.

[0355] The server acquires sensor data from tourist destinations and restaurants, as well as rating data from social networks, in real time, and analyzes congestion levels using information processing tools. The server processes this data using AI tools and statistical analysis software (e.g., TensorFlow, SciPy).

[0356] Users acquire emotional data by using the cameras of their smartphones or wearable devices as a means of emotion recognition, analyzing their facial expressions and movements. This is where an emotion engine (e.g., Affectiva) comes into play.

[0357] The on-device display provides route suggestions and customized experiences received from the server in augmented reality format, presenting the user with the optimal route and experience. This allows users to avoid congestion and enjoy a personalized experience.

[0358] For example, a user might launch an application, and the emotion engine analyzes their facial expression, determining that they are seeking relaxation. In that case, the server might suggest a less crowded restaurant and recommend relaxing drinks and food.

[0359] Examples of prompts for a generative AI model are as follows:

[0360] "Please provide image data of the user's face. Next, use an emotion engine to predict what the user currently wants (e.g., relax, refresh), and based on that prediction, identify available restaurants in the current area and suggest dishes best suited for relaxation."

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The user initiates the application on the device. The user inputs desired tourist destinations and experiences. This information, along with location data, is sent to the server by the device. In this process, input is the desired destinations and experiences, and output is the transmission of data to the server.

[0364] Step 2:

[0365] The server uses location information acquisition means to determine the user's current location. The identified location information, along with sensor data from tourist attractions and restaurants, and rating data from social networks, are acquired in real time. This data is analyzed by information processing means to evaluate congestion levels in real time. Inputs are location data and external data, and output is real-time congestion information.

[0366] Step 3:

[0367] The device utilizes emotion recognition to capture the user's facial expressions and actions with a camera, and analyzes the user's emotional state using an emotion engine. This process converts image data into emotion data and sends it to a server. The input is image data, and the output is emotional state data.

[0368] Step 4:

[0369] The server integrates sentiment data and congestion data, and uses route suggestion and customization suggestion means to calculate the optimal alternative route and experience for the user. AI and statistical analysis software are used to process this data and generate personalized suggestions for each user. The input is sentiment data and congestion data, and the output is suggestion information.

[0370] Step 5:

[0371] The device receives suggestion information from the server and displays it to the user in augmented reality format. The device provides visually rich information to enhance the user experience and support specific actions during travel. The input is suggestion information, and the output is an augmented reality display.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] As an embodiment of this invention, a system for providing a comfortable tourist experience is presented. Users access this system through a smartphone application.

[0389] First, the user launches the app and selects a tourist destination they want to visit. This information, along with the user's location data, is sent to the server.

[0390] Next, the server collects external data, such as sensor data and social network data, to understand the real-time congestion status of tourist destinations. This data is analyzed by AI algorithms to calculate the congestion status and predictions for each area.

[0391] The server then calculates the optimal alternative route and visit time based on the analysis results, and formats this information as AR content.

[0392] The device acquires this formatted data and integrates it with the user's camera feed in real time for display. This allows the user to visually check the route from their current location to their destination and the congestion levels at each point.

[0393] For example, if area X is predicted to be extremely crowded between 13:00 and 14:00 in tourist area A, the server calculates an alternative route to avoid area X and suggests visiting after 15:00. Upon receiving this information, the terminal provides route guidance overlaid on the user's camera view, helping them avoid congestion. In this way, tourists can sightsee efficiently while minimizing stress caused by congestion.

[0394] This system offers a new style of tourism, providing visitors with a better experience, and also supports sustainable operations in tourist destinations.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The user launches the smartphone app, selects a tourist destination, and enters their visit plan. The device obtains the user's location information using GPS and sends this information to the server.

[0398] Step 2:

[0399] Based on the user's location information and planned destination information received by the server, external data (such as sensor data and social network data) regarding congestion levels at tourist destinations is collected. This allows for real-time congestion data to be obtained.

[0400] Step 3:

[0401] The server uses an AI model to analyze collected external data and predict current and future congestion levels in each area of ​​the tourist destination. Specifically, it uses statistical and machine learning models to identify peak congestion times and congested areas.

[0402] Step 4:

[0403] The server calculates the optimal alternative route and visit time to avoid congestion based on the analysis results. This calculation uses a congestion avoidance algorithm to generate an optimized route and suggested time for each user.

[0404] Step 5:

[0405] The server formats the calculated route information and visit time in augmented reality format and sends it to the terminal.

[0406] Step 6:

[0407] Based on the information received by the device, augmented reality (AR) is displayed on the user's smartphone camera. The user moves along the guided route while viewing the AR information displayed in real time. This allows for sightseeing while avoiding crowds.

[0408] Step 7:

[0409] Users provide feedback based on their experiences while sightseeing. The device sends user behavior information and performance data of selected routes to a server, which is used for future analysis and algorithm improvements.

[0410] (Example 1)

[0411] 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."

[0412] The stress and wasted time caused by overcrowding at tourist destinations are major problems for tourists. Furthermore, the concentration of visitors increases the burden on the environment and facilities of tourist destinations, potentially impacting their sustainability. A system is needed to address these challenges and provide tourists with a comfortable and efficient travel experience.

[0413] 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.

[0414] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, route suggestion means for calculating the optimal alternative route and visit time to avoid congestion based on the information obtained by the information processing means, display means for displaying the information from the route suggestion means as augmented reality on the user's device, and data analysis means for collecting external data, analyzing it using a generative AI model, and predicting congestion. This enables tourists to create optimal visit plans to avoid congestion, thereby promoting the efficient and sustainable use of tourist destinations.

[0415] "Information processing means" refers to the configuration of software and hardware for analyzing congestion levels in tourist areas in real time.

[0416] "Route suggestion means" refers to a technology that calculates the optimal alternative route and visit time to avoid congestion based on analysis results obtained by information processing means.

[0417] "Display means" refers to a function for visually displaying calculated route and visit time information as augmented reality on the user's device.

[0418] "Data analysis means" refers to the technology that collects external data, analyzes it using generated AI models, and performs congestion prediction.

[0419] This invention provides an information system to help tourists comfortably visit tourist destinations. Users launch an application on a mobile device such as a smartphone and select the tourist destination they wish to visit. Upon selection, the device sends the selection information, along with the user's current location, to a server. This process incorporates location tracking functions utilizing GPS sensors and Wi-Fi. Data transmission is securely performed using the HTTPS protocol.

[0420] The server collects data from sensor data and social networking services to understand the congestion levels of tourist destinations in real time. Specifically, it collects information related to tourist destinations using publicly available camera footage and social networking APIs. This collected data is analyzed using a generative AI model to predict congestion levels. TensorFlow is used as the AI ​​algorithm platform, which is based on Python, for the analysis.

[0421] Based on the analysis results, the server calculates the optimal alternative route and visit time for the user. Route optimization algorithms such as the A algorithm and Dijkstra's algorithm are used for this calculation. The calculated information is then formatted as augmented reality content using Unity or ARKit.

[0422] The device integrates augmented reality content received from the server into the camera view and displays it to the user. This allows users to visually check real-time congestion levels and recommended routes at tourist destinations using their own devices. An example of a prompt message might be, "Based on the current congestion levels at the tourist destination, please tell me the best route and visiting time." This system allows users to enjoy sightseeing efficiently and reduces stress caused by congestion.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The user launches a smartphone application and selects a tourist destination they wish to visit. Based on the user's selection, the app retrieves the user's current location information, including GPS coordinates and time. The retrieved data is sent to the server in JSON format. This process provides the basic information needed to plan the user's intended visit to the tourist destination.

[0426] Step 2:

[0427] The server collects real-time congestion data based on the user's current location information and tourist destination information received. The server aggregates external sensor data (e.g., traffic camera footage) and data from social networking platforms (e.g., public posts). This data serves as the starting point for analyzing congestion levels that affect users' visits to tourist destinations.

[0428] Step 3:

[0429] The server analyzes the collected external data using a generating AI model. Specifically, it applies an AI algorithm (for example, a neural network model using TensorFlow) to predict current and future congestion levels in tourist destinations. The results of this analysis are output as an indicator showing congestion levels during the times when tourists are likely to visit.

[0430] Step 4:

[0431] The server calculates the optimal route and time based on congestion prediction data. Using Dijkstra and A algorithms, it proposes a traffic-avoiding route to the user's destination. This calculation takes into account current traffic conditions and predicted crowd levels. The results are then formatted as AR content.

[0432] Step 5:

[0433] The device integrates AR content received from the server with the camera image in real time. Users can visually check the route from their current location to their destination and the congestion status through the device's screen. This process enables users to enjoy a comfortable and efficient sightseeing experience.

[0434] (Application Example 1)

[0435] 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."

[0436] Overcrowding in tourist destinations is a factor that hinders the visitor experience and also negatively impacts the management of local resources. This invention aims to provide a comfortable tourist experience by helping visitors avoid congestion when visiting tourist destinations.

[0437] 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.

[0438] In this invention, the server includes data processing means for analyzing the crowd situation in a tourist area over time, guidance suggestion means for calculating options to avoid crowds based on the information collected by the data processing means, and output means for displaying the information from the guidance suggestion means as virtual reality on the user's mobile terminal. This makes it possible to avoid expected crowds when visiting a tourist destination, enabling an efficient and comfortable sightseeing experience.

[0439] A "tourist area" refers to a specific region or place where visitors gather for sightseeing purposes.

[0440] "People gathering situation" refers to information that shows the number and density of people gathered at a tourist destination at a specific time.

[0441] "Data processing means" refers to devices and methods for collecting and analyzing various types of information to generate useful information.

[0442] A "guidance suggestion method" refers to a system or algorithm that suggests the optimal route and visit time to a user based on collected data.

[0443] "Virtual reality" refers to visual information generated using computer technology and integrated with the real environment.

[0444] "User's mobile device" refers to portable information and communication devices such as smartphones and tablets.

[0445] "Location data acquisition means" refers to technologies and devices that use GPS or similar systems to determine the user's current location.

[0446] "External information" refers to data obtained from outside the system, including sensor data and social network data.

[0447] To implement this invention, the user first uses a dedicated application on their mobile device. The user selects a tourist destination they wish to visit and sends that information to the server. The server collects external information from various sensors installed in the tourist destination area and from social networking platforms, and uses this information to analyze the crowd situation. Data analysis software such as Python or R can be used for data processing.

[0448] Next, the server uses the analyzed data to calculate the optimal route and visit time to guide users to avoid crowded areas. AI algorithms are used for these calculations, and machine learning libraries such as TensorFlow and PyTorch can be utilized.

[0449] The information selected by the guidance suggestion system is displayed as virtual reality on the user's mobile device using augmented reality (AR). AR development platforms such as Unity and ARKit are used for this. Based on this, the user can visually confirm the route from their current location to their destination.

[0450] As a concrete example, when a user plans a "day tour of the city" by operating the interface, the system suggests avoiding crowded museums in the morning and relaxing at a cafe, then displays a route to visit tourist attractions from midday onwards.

[0451] Examples of prompts to input into a generative AI model:

[0452] "Propose a tourist information system that provides congestion predictions and optimal routes when a user selects a tourist destination."

[0453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0454] Step 1:

[0455] The user launches the application on their mobile device and selects a tourist destination they wish to visit. The input consists of the user's selected tourist destination information and their current location, which is then sent from the device to the server. The output is the user information received by the server.

[0456] Step 2:

[0457] The server collects external information from sensors installed in tourist areas and from social media, and analyzes the crowd situation. The input consists of real-time data from sensors and social media data. This data is analyzed using Python, R, or other languages ​​to predict congestion. The output is crowd prediction information for each area of ​​the tourist destination.

[0458] Step 3:

[0459] The server calculates the optimal route and visit time to avoid congestion based on the analysis results. The inputs are crowd prediction information and user selection information. An AI algorithm is used to generate the optimal route to the destination. The output is alternative route guidance information.

[0460] Step 4:

[0461] The server formats the generated guidance information as AR content. The input is alternative route guidance information, which is visualized using tools such as ARKit or Unity. The output is formatted AR data.

[0462] Step 5:

[0463] The device uses the received AR data and, while taking the user's current location into consideration, displays virtual reality overlaid on the camera image. The input consists of formatted AR data and the user's location information. The output is a virtual reality representation visually presented to the user.

[0464] 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.

[0465] As an embodiment of this invention, a tourist information system incorporating an emotion engine is described to provide tourists with an immersive sightseeing experience. Users can use this system on smartphones or wearable devices.

[0466] First, the user launches the application on their device and enters the tourist destinations they want to visit and the experiences they wish to have. Based on this information, the device uses GPS to determine the user's current location and sends that information to the server.

[0467] Next, the server collects sensor data and social network data from tourist destinations and analyzes congestion levels in real time. This includes a process that uses AI and statistical analysis to predict congestion and understand pedestrian flow in each area.

[0468] Furthermore, an emotion engine built into the user's device uses cameras and other sensors to measure the user's emotional state from their facial expressions and actions, acquiring this data in real time. The emotion engine understands the user's stress level and preferred situations, and provides feedback to the system.

[0469] The server combines acquired sentiment data and congestion data to generate optimal route suggestions. These suggestions include providing personalized guidance, such as locations where the user can relax more or routes that avoid unexpected congestion. For example, if a user is feeling stressed in a particular area, the sentiment engine's data will suggest moving to a different area or taking a short break.

[0470] Ultimately, the device receives information transmitted from the server in an augmented reality format and displays it overlaid on the user's smartphone camera. This allows the user to receive a personalized sightseeing experience on the spot, increasing their satisfaction with the experience.

[0471] This system enables flexible guidance that takes into account the emotions of visitors in tourist destinations, thereby providing a deeper and more valuable experience. This embodiment contributes to the creation of new added value in the tourism sector.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user launches an app on their smartphone or wearable device, selects a tourist destination, and enters their purpose of visit and desired experiences. The device obtains this information and their current location via GPS and sends it to the server.

[0475] Step 2:

[0476] The server receives information from users and collects real-time data from tourist destinations. This data includes sensor data, traffic data, and social media trend information, and is used to understand congestion levels.

[0477] Step 3:

[0478] The server uses AI algorithms to analyze collected real-time data and predict congestion at each tourist spot. This prediction evaluates peak congestion times and pedestrian flow to forecast future conditions.

[0479] Step 4:

[0480] The device's built-in emotion engine analyzes the user's emotional state from their facial expressions, heart rate, and voice. This emotional data is used to assess the user's current emotional comfort level and stress level.

[0481] Step 5:

[0482] The server receives data from the emotion engine and analyzes it along with congestion levels. Based on the user's emotional state, the server re-evaluates personalized routes and destinations to generate an optimized sightseeing plan.

[0483] Step 6:

[0484] The server generates a personalized sightseeing plan and sends it to the user's device. This plan includes recommended routes and activity information tailored to the user's preferences.

[0485] Step 7:

[0486] The device presents the received information to the user as augmented reality (AR) visualization data. This allows the user to proceed with sightseeing while viewing real-time guidance displayed on video.

[0487] Step 8:

[0488] As users progress through the tour following the guide, their device constantly operates an emotion engine, sending feedback to the server in real time. This dynamically adjusts the tour plan, improving the user experience.

[0489] (Example 2)

[0490] 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."

[0491] Conventional tourist information systems typically suggest routes that take into account the congestion levels of tourist destinations, but they have struggled to provide an optimized tourist experience that takes into account the emotional state and preferences of individual users. As a result, users may not be able to have a relaxing tourist experience, and the quality of the experience does not improve.

[0492] 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.

[0493] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, emotion analysis means for measuring the user's emotional state and optimizing the tourist experience, and route suggestion means for calculating and proposing personalized routes. This makes it possible to propose guidelines that take the user's emotions into consideration, and to provide a tourist experience tailored to individual needs.

[0494] An "information processing means" is a component that has the function of collecting and analyzing multiple data points in a tourist destination in real time to understand the congestion situation.

[0495] "Emotion analysis means" refers to a component that analyzes emotional information acquired through the user's device and generates information to provide the user with the optimal tourist experience.

[0496] A "route suggestion means" is a component that has the function of calculating and suggesting a route and destination optimized for the user based on acquired congestion status and sentiment information.

[0497] "Display means" refers to a component that has the function of displaying information generated from the route suggestion means on the user's terminal in augmented reality format and providing visual guidance.

[0498] A "generative AI model" is a model that applies artificial intelligence technology to generate user-friendly suggestions and prompts from data.

[0499] One embodiment of this invention is a system that provides users with personalized tourism experiences. This system consists of a user's device, a server, and multiple software components.

[0500] First, the user launches a dedicated application on their smartphone or wearable device and enters the tourist destinations they want to visit and the experiences they desire. This device is equipped with GPS to obtain location information.

[0501] Next, the device acquires the user's input information and GPS location data and sends it to the server. The device uses the Google Maps API to provide accurate location information. The device is also equipped with sensors such as a camera and microphone to collect data for sentiment analysis.

[0502] The server possesses powerful information processing capabilities to collect external data related to tourist destinations and analyze congestion levels and user-related information in real time. This includes sensor data and social network data, which are analyzed using machine learning algorithms. In this process, the server utilizes Apache Hadoop and the Python Pandas library.

[0503] Next, the emotion analysis system analyzes the user's facial expressions and voice in real time through the user's device to understand the user's emotional state. Here, an emotion analysis API is used to measure the user's stress level and emotions.

[0504] The generative AI model uses this data to generate routes and sightseeing plans suitable for the user and creates prompt messages. An example of a prompt message is, "Please tell me about quiet places in Kyoto."

[0505] Finally, the device receives optimal route information sent from the server and displays it in the user's field of view using augmented reality technology. This is achieved using ARKit or ARCore. As a result, users receive real-time optimized sightseeing guidance and can have a more personalized and better sightseeing experience.

[0506] As a concrete example, if a user experiences stress in a certain area of ​​Tokyo, the system uses that data to immediately suggest moving to another area where they can relax. The user can visually confirm their next action through the device, allowing them to continue a comfortable sightseeing experience. Thus, the present invention aims to improve the quality of the sightseeing experience by providing a flexible system that takes into account the user's emotions and circumstances.

[0507] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0508] Step 1:

[0509] Users launch a dedicated application on their smartphone or wearable device and input the tourist destinations they wish to visit and the experiences they desire. This input includes location information and details of the desired experiences. As output, the user's current location information, along with the input information, is obtained through location services.

[0510] Step 2:

[0511] The device sends user-entered information and GPS location data to the server. In this step, the Google Maps API is used to process the precise location data and provide the server with structured data as output.

[0512] Step 3:

[0513] The server collects and analyzes external data related to tourist destinations based on data received from terminals. This data processing includes sensor data and social network data. AI algorithms and statistical analysis tools are used to predict congestion levels and user area characteristics. Analysis results are generated as output.

[0514] Step 4:

[0515] The device's built-in emotion analysis system collects user emotion data through the camera and microphone. Specifically, it analyzes the user's facial expressions and tone of voice to infer their emotional state. An emotion recognition API is used for this process, and user emotion information is generated as output.

[0516] Step 5:

[0517] The server integrates emotional data with analyzed external data and uses a generative AI model to create personalized sightseeing routes and experience plans. Data processing involves complex calculations that consider user emotions and congestion levels. The final output is a route suggestion and prompt message best suited to the user. An example of a prompt message might be, "Please tell me about quiet places in Kyoto."

[0518] Step 6:

[0519] The device receives output information from the server and displays it in the user's field of view using augmented reality (AR) technology. Specifically, ARKit and ARCore are utilized, and the user receives visual guidance through the device. As output, AR display information is provided overlaid on the user's device. As a result, the user can receive real-time optimized tourist information.

[0520] (Application Example 2)

[0521] 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."

[0522] Traditional tourism systems and food delivery services have struggled to provide personalized suggestions based on users' emotional states and real-time congestion levels. This can lead to users not receiving the expected experience and experiencing decreased satisfaction. Furthermore, visiting crowded areas or stores can cause stress. Therefore, there is a need for a system that can simultaneously avoid congestion and provide the optimal experience while considering the user's emotional state.

[0523] 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.

[0524] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations and restaurants in real time, emotion recognition means for analyzing the emotional state of users, and customization suggestion means for proposing the most suitable stores and experiences to users. This makes it possible to provide individually optimized tourist and dining experiences based on the emotional state of users and congestion information.

[0525] "Information processing means" refers to devices or software that perform calculations to acquire and analyze the congestion status of tourist destinations and restaurants in real time.

[0526] A "route suggestion means" is a device or algorithm that calculates and suggests alternative routes to help users avoid congestion based on acquired congestion information.

[0527] "Emotion recognition means" refers to devices or software that have the function of understanding a user's emotional state by analyzing the user's facial expressions and movements.

[0528] A "customized suggestion method" refers to a device or system that individually suggests optimal dining facilities or tourist experiences based on user sentiment data and real-time information.

[0529] "Display means" refers to functions or devices for visually displaying proposed information as augmented reality on a user device.

[0530] "Location information acquisition means" refers to devices or technologies that have the function of identifying the user's current location and providing it to other systems.

[0531] "External information" refers to information collected from outside the system, such as sensor data and evaluation data on online platforms.

[0532] The system implementing this invention analyzes the congestion status of tourist destinations and restaurants in real time and provides an optimal experience that takes into account the user's emotional state. The system consists of information processing means, route suggestion means, emotion recognition means, customization suggestion means, display means, and location information acquisition means.

[0533] The server acquires sensor data from tourist destinations and restaurants, as well as rating data from social networks, in real time, and analyzes congestion levels using information processing tools. The server processes this data using AI tools and statistical analysis software (e.g., TensorFlow, SciPy).

[0534] Users acquire emotional data by using the cameras of their smartphones or wearable devices as a means of emotion recognition, analyzing their facial expressions and movements. This is where an emotion engine (e.g., Affectiva) comes into play.

[0535] The on-device display provides route suggestions and customized experiences received from the server in augmented reality format, presenting the user with the optimal route and experience. This allows users to avoid congestion and enjoy a personalized experience.

[0536] For example, a user might launch an application, and the emotion engine analyzes their facial expression, determining that they are seeking relaxation. In that case, the server might suggest a less crowded restaurant and recommend relaxing drinks and food.

[0537] Examples of prompts for a generative AI model are as follows:

[0538] "Please provide image data of the user's face. Next, use an emotion engine to predict what the user currently wants (e.g., relax, refresh), and based on that prediction, identify available restaurants in the current area and suggest dishes best suited for relaxation."

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The user initiates the application on the device. The user inputs desired tourist destinations and experiences. This information, along with location data, is sent to the server by the device. In this process, input is the desired destinations and experiences, and output is the transmission of data to the server.

[0542] Step 2:

[0543] The server uses location information acquisition means to determine the user's current location. The identified location information, along with sensor data from tourist attractions and restaurants, and rating data from social networks, are acquired in real time. This data is analyzed by information processing means to evaluate congestion levels in real time. Inputs are location data and external data, and output is real-time congestion information.

[0544] Step 3:

[0545] The device utilizes emotion recognition to capture the user's facial expressions and actions with a camera, and analyzes the user's emotional state using an emotion engine. This process converts image data into emotion data and sends it to a server. The input is image data, and the output is emotional state data.

[0546] Step 4:

[0547] The server integrates sentiment data and congestion data, and uses route suggestion and customization suggestion means to calculate the optimal alternative route and experience for the user. AI and statistical analysis software are used to process this data and generate personalized suggestions for each user. The input is sentiment data and congestion data, and the output is suggestion information.

[0548] Step 5:

[0549] The device receives suggestion information from the server and displays it to the user in augmented reality format. The device provides visually rich information to enhance the user experience and support specific actions during travel. The input is suggestion information, and the output is an augmented reality display.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] [Fourth Embodiment]

[0554] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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".

[0567] As an embodiment of this invention, a system for providing a comfortable tourist experience is presented. Users access this system through a smartphone application.

[0568] First, the user launches the app and selects a tourist destination they want to visit. This information, along with the user's location data, is sent to the server.

[0569] Next, the server collects external data, such as sensor data and social network data, to understand the real-time congestion status of tourist destinations. This data is analyzed by AI algorithms to calculate the congestion status and predictions for each area.

[0570] The server then calculates the optimal alternative route and visit time based on the analysis results, and formats this information as AR content.

[0571] The device acquires this formatted data and integrates it with the user's camera feed in real time for display. This allows the user to visually check the route from their current location to their destination and the congestion levels at each point.

[0572] For example, if area X is predicted to be extremely crowded between 13:00 and 14:00 in tourist area A, the server calculates an alternative route to avoid area X and suggests visiting after 15:00. Upon receiving this information, the terminal provides route guidance overlaid on the user's camera view, helping them avoid congestion. In this way, tourists can sightsee efficiently while minimizing stress caused by congestion.

[0573] This system offers a new style of tourism, providing visitors with a better experience, and also supports sustainable operations in tourist destinations.

[0574] The following describes the processing flow.

[0575] Step 1:

[0576] The user launches the smartphone app, selects a tourist destination, and enters their visit plan. The device obtains the user's location information using GPS and sends this information to the server.

[0577] Step 2:

[0578] Based on the user's location information and planned destination information received by the server, external data (such as sensor data and social network data) regarding congestion levels at tourist destinations is collected. This allows for real-time congestion data to be obtained.

[0579] Step 3:

[0580] The server uses an AI model to analyze collected external data and predict current and future congestion levels in each area of ​​the tourist destination. Specifically, it uses statistical and machine learning models to identify peak congestion times and congested areas.

[0581] Step 4:

[0582] The server calculates the optimal alternative route and visit time to avoid congestion based on the analysis results. This calculation uses a congestion avoidance algorithm to generate an optimized route and suggested time for each user.

[0583] Step 5:

[0584] The server formats the calculated route information and visit time in augmented reality format and sends it to the terminal.

[0585] Step 6:

[0586] Based on the information received by the device, augmented reality (AR) is displayed on the user's smartphone camera. The user moves along the guided route while viewing the AR information displayed in real time. This allows for sightseeing while avoiding crowds.

[0587] Step 7:

[0588] Users provide feedback based on their experiences while sightseeing. The device sends user behavior information and performance data of selected routes to a server, which is used for future analysis and algorithm improvements.

[0589] (Example 1)

[0590] 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".

[0591] The stress and wasted time caused by overcrowding at tourist destinations are major problems for tourists. Furthermore, the concentration of visitors increases the burden on the environment and facilities of tourist destinations, potentially impacting their sustainability. A system is needed to address these challenges and provide tourists with a comfortable and efficient travel experience.

[0592] 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.

[0593] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, route suggestion means for calculating the optimal alternative route and visit time to avoid congestion based on the information obtained by the information processing means, display means for displaying the information from the route suggestion means as augmented reality on the user's device, and data analysis means for collecting external data, analyzing it using a generative AI model, and predicting congestion. This enables tourists to create optimal visit plans to avoid congestion, thereby promoting the efficient and sustainable use of tourist destinations.

[0594] "Information processing means" refers to the configuration of software and hardware for analyzing congestion levels in tourist areas in real time.

[0595] "Route suggestion means" refers to a technology that calculates the optimal alternative route and visit time to avoid congestion based on analysis results obtained by information processing means.

[0596] "Display means" refers to a function for visually displaying calculated route and visit time information as augmented reality on the user's device.

[0597] "Data analysis means" refers to the technology that collects external data, analyzes it using generated AI models, and performs congestion prediction.

[0598] This invention provides an information system to help tourists comfortably visit tourist destinations. Users launch an application on a mobile device such as a smartphone and select the tourist destination they wish to visit. Upon selection, the device sends the selection information, along with the user's current location, to a server. This process incorporates location tracking functions utilizing GPS sensors and Wi-Fi. Data transmission is securely performed using the HTTPS protocol.

[0599] The server collects data from sensor data and social networking services to understand the congestion levels of tourist destinations in real time. Specifically, it collects information related to tourist destinations using publicly available camera footage and social networking APIs. This collected data is analyzed using a generative AI model to predict congestion levels. TensorFlow is used as the AI ​​algorithm platform, which is based on Python, for the analysis.

[0600] Based on the analysis results, the server calculates the optimal alternative route and visit time for the user. Route optimization algorithms such as the A algorithm and Dijkstra's algorithm are used for this calculation. The calculated information is then formatted as augmented reality content using Unity or ARKit.

[0601] The device integrates augmented reality content received from the server into the camera view and displays it to the user. This allows users to visually check real-time congestion levels and recommended routes at tourist destinations using their own devices. An example of a prompt message might be, "Based on the current congestion levels at the tourist destination, please tell me the best route and visiting time." This system allows users to enjoy sightseeing efficiently and reduces stress caused by congestion.

[0602] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0603] Step 1:

[0604] The user launches a smartphone application and selects a tourist destination they wish to visit. Based on the user's selection, the app retrieves the user's current location information, including GPS coordinates and time. The retrieved data is sent to the server in JSON format. This process provides the basic information needed to plan the user's intended visit to the tourist destination.

[0605] Step 2:

[0606] The server collects real-time congestion data based on the user's current location information and tourist destination information received. The server aggregates external sensor data (e.g., traffic camera footage) and data from social networking platforms (e.g., public posts). This data serves as the starting point for analyzing congestion levels that affect users' visits to tourist destinations.

[0607] Step 3:

[0608] The server analyzes the collected external data using a generating AI model. Specifically, it applies an AI algorithm (for example, a neural network model using TensorFlow) to predict current and future congestion levels in tourist destinations. The results of this analysis are output as an indicator showing congestion levels during the times when tourists are likely to visit.

[0609] Step 4:

[0610] The server calculates the optimal route and time based on congestion prediction data. Using Dijkstra and A algorithms, it proposes a traffic-avoiding route to the user's destination. This calculation takes into account current traffic conditions and predicted crowd levels. The results are then formatted as AR content.

[0611] Step 5:

[0612] The device integrates AR content received from the server with the camera image in real time. Users can visually check the route from their current location to their destination and the congestion status through the device's screen. This process enables users to enjoy a comfortable and efficient sightseeing experience.

[0613] (Application Example 1)

[0614] 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".

[0615] Overcrowding in tourist destinations is a factor that hinders the visitor experience and also negatively impacts the management of local resources. This invention aims to provide a comfortable tourist experience by helping visitors avoid congestion when visiting tourist destinations.

[0616] 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.

[0617] In this invention, the server includes data processing means for analyzing the crowd situation in a tourist area over time, guidance suggestion means for calculating options to avoid crowds based on the information collected by the data processing means, and output means for displaying the information from the guidance suggestion means as virtual reality on the user's mobile terminal. This makes it possible to avoid expected crowds when visiting a tourist destination, enabling an efficient and comfortable sightseeing experience.

[0618] A "tourist area" refers to a specific region or place where visitors gather for sightseeing purposes.

[0619] "People gathering situation" refers to information that shows the number and density of people gathered at a tourist destination at a specific time.

[0620] "Data processing means" refers to devices and methods for collecting and analyzing various types of information to generate useful information.

[0621] A "guidance suggestion method" refers to a system or algorithm that suggests the optimal route and visit time to a user based on collected data.

[0622] "Virtual reality" refers to visual information generated using computer technology and integrated with the real environment.

[0623] "User's mobile device" refers to portable information and communication devices such as smartphones and tablets.

[0624] "Location data acquisition means" refers to technologies and devices that use GPS or similar systems to determine the user's current location.

[0625] "External information" refers to data obtained from outside the system, including sensor data and social network data.

[0626] To implement this invention, the user first uses a dedicated application on their mobile device. The user selects a tourist destination they wish to visit and sends that information to the server. The server collects external information from various sensors installed in the tourist destination area and from social networking platforms, and uses this information to analyze the crowd situation. Data analysis software such as Python or R can be used for data processing.

[0627] Next, the server uses the analyzed data to calculate the optimal route and visit time to guide users to avoid crowded areas. AI algorithms are used for these calculations, and machine learning libraries such as TensorFlow and PyTorch can be utilized.

[0628] The information selected by the guidance suggestion system is displayed as virtual reality on the user's mobile device using augmented reality (AR). AR development platforms such as Unity and ARKit are used for this. Based on this, the user can visually confirm the route from their current location to their destination.

[0629] As a concrete example, when a user plans a "day tour of the city" by operating the interface, the system suggests avoiding crowded museums in the morning and relaxing at a cafe, then displays a route to visit tourist attractions from midday onwards.

[0630] Examples of prompts to input into a generative AI model:

[0631] "Propose a tourist information system that provides congestion predictions and optimal routes when a user selects a tourist destination."

[0632] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0633] Step 1:

[0634] The user launches the application on their mobile device and selects a tourist destination they wish to visit. The input consists of the user's selected tourist destination information and their current location, which is then sent from the device to the server. The output is the user information received by the server.

[0635] Step 2:

[0636] The server collects external information from sensors installed in tourist areas and from social media, and analyzes the crowd situation. The input consists of real-time data from sensors and social media data. This data is analyzed using Python, R, or other languages ​​to predict congestion. The output is crowd prediction information for each area of ​​the tourist destination.

[0637] Step 3:

[0638] The server calculates the optimal route and visit time to avoid congestion based on the analysis results. The inputs are crowd prediction information and user selection information. An AI algorithm is used to generate the optimal route to the destination. The output is alternative route guidance information.

[0639] Step 4:

[0640] The server formats the generated guidance information as AR content. The input is alternative route guidance information, which is visualized using tools such as ARKit or Unity. The output is formatted AR data.

[0641] Step 5:

[0642] The device uses the received AR data and, while taking the user's current location into consideration, displays virtual reality overlaid on the camera image. The input consists of formatted AR data and the user's location information. The output is a virtual reality representation visually presented to the user.

[0643] 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.

[0644] As an embodiment of this invention, a tourist information system incorporating an emotion engine is described to provide tourists with an immersive sightseeing experience. Users can use this system on smartphones or wearable devices.

[0645] First, the user launches the application on their device and enters the tourist destinations they want to visit and the experiences they wish to have. Based on this information, the device uses GPS to determine the user's current location and sends that information to the server.

[0646] Next, the server collects sensor data and social network data from tourist destinations and analyzes congestion levels in real time. This includes a process that uses AI and statistical analysis to predict congestion and understand pedestrian flow in each area.

[0647] Furthermore, an emotion engine built into the user's device uses cameras and other sensors to measure the user's emotional state from their facial expressions and actions, acquiring this data in real time. The emotion engine understands the user's stress level and preferred situations, and provides feedback to the system.

[0648] The server combines acquired sentiment data and congestion data to generate optimal route suggestions. These suggestions include providing personalized guidance, such as locations where the user can relax more or routes that avoid unexpected congestion. For example, if a user is feeling stressed in a particular area, the sentiment engine's data will suggest moving to a different area or taking a short break.

[0649] Ultimately, the device receives information transmitted from the server in an augmented reality format and displays it overlaid on the user's smartphone camera. This allows the user to receive a personalized sightseeing experience on the spot, increasing their satisfaction with the experience.

[0650] This system enables flexible guidance that takes into account the emotions of visitors in tourist destinations, thereby providing a deeper and more valuable experience. This embodiment contributes to the creation of new added value in the tourism sector.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user launches an app on their smartphone or wearable device, selects a tourist destination, and enters their purpose of visit and desired experiences. The device obtains this information and their current location via GPS and sends it to the server.

[0654] Step 2:

[0655] The server receives information from users and collects real-time data from tourist destinations. This data includes sensor data, traffic data, and social media trend information, and is used to understand congestion levels.

[0656] Step 3:

[0657] The server uses AI algorithms to analyze collected real-time data and predict congestion at each tourist spot. This prediction evaluates peak congestion times and pedestrian flow to forecast future conditions.

[0658] Step 4:

[0659] The device's built-in emotion engine analyzes the user's emotional state from their facial expressions, heart rate, and voice. This emotional data is used to assess the user's current emotional comfort level and stress level.

[0660] Step 5:

[0661] The server receives data from the emotion engine and analyzes it along with congestion levels. Based on the user's emotional state, the server re-evaluates personalized routes and destinations to generate an optimized sightseeing plan.

[0662] Step 6:

[0663] The server generates a personalized sightseeing plan and sends it to the user's device. This plan includes recommended routes and activity information tailored to the user's preferences.

[0664] Step 7:

[0665] The device presents the received information to the user as augmented reality (AR) visualization data. This allows the user to proceed with sightseeing while viewing real-time guidance displayed on video.

[0666] Step 8:

[0667] As users progress through the tour following the guide, their device constantly operates an emotion engine, sending feedback to the server in real time. This dynamically adjusts the tour plan, improving the user experience.

[0668] (Example 2)

[0669] 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".

[0670] Conventional tourist information systems typically suggest routes that take into account the congestion levels of tourist destinations, but they have struggled to provide an optimized tourist experience that takes into account the emotional state and preferences of individual users. As a result, users may not be able to have a relaxing tourist experience, and the quality of the experience does not improve.

[0671] 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.

[0672] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations in real time, emotion analysis means for measuring the user's emotional state and optimizing the tourist experience, and route suggestion means for calculating and proposing personalized routes. This makes it possible to propose guidelines that take the user's emotions into consideration, and to provide a tourist experience tailored to individual needs.

[0673] An "information processing means" is a component that has the function of collecting and analyzing multiple data points in a tourist destination in real time to understand the congestion situation.

[0674] "Emotion analysis means" refers to a component that analyzes emotional information acquired through the user's device and generates information to provide the user with the optimal tourist experience.

[0675] A "route suggestion means" is a component that has the function of calculating and suggesting a route and destination optimized for the user based on acquired congestion status and sentiment information.

[0676] "Display means" refers to a component that has the function of displaying information generated from the route suggestion means on the user's terminal in augmented reality format and providing visual guidance.

[0677] A "generative AI model" is a model that applies artificial intelligence technology to generate user-friendly suggestions and prompts from data.

[0678] One embodiment of this invention is a system that provides users with personalized tourism experiences. This system consists of a user's device, a server, and multiple software components.

[0679] First, the user launches a dedicated application on their smartphone or wearable device and enters the tourist destinations they want to visit and the experiences they desire. This device is equipped with GPS to obtain location information.

[0680] Next, the device acquires the user's input information and GPS location data and sends it to the server. The device uses the Google Maps API to provide accurate location information. The device is also equipped with sensors such as a camera and microphone to collect data for sentiment analysis.

[0681] The server possesses powerful information processing capabilities to collect external data related to tourist destinations and analyze congestion levels and user-related information in real time. This includes sensor data and social network data, which are analyzed using machine learning algorithms. In this process, the server utilizes Apache Hadoop and the Python Pandas library.

[0682] Next, the emotion analysis system analyzes the user's facial expressions and voice in real time through the user's device to understand the user's emotional state. Here, an emotion analysis API is used to measure the user's stress level and emotions.

[0683] The generative AI model uses this data to generate routes and sightseeing plans suitable for the user and creates prompt messages. An example of a prompt message is, "Please tell me about quiet places in Kyoto."

[0684] Finally, the device receives optimal route information sent from the server and displays it in the user's field of view using augmented reality technology. This is achieved using ARKit or ARCore. As a result, users receive real-time optimized sightseeing guidance and can have a more personalized and better sightseeing experience.

[0685] As a concrete example, if a user experiences stress in a certain area of ​​Tokyo, the system uses that data to immediately suggest moving to another area where they can relax. The user can visually confirm their next action through the device, allowing them to continue a comfortable sightseeing experience. Thus, the present invention aims to improve the quality of the sightseeing experience by providing a flexible system that takes into account the user's emotions and circumstances.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] Users launch a dedicated application on their smartphone or wearable device and input the tourist destinations they wish to visit and the experiences they desire. This input includes location information and details of the desired experiences. As output, the user's current location information, along with the input information, is obtained through location services.

[0689] Step 2:

[0690] The device sends user-entered information and GPS location data to the server. In this step, the Google Maps API is used to process the precise location data and provide the server with structured data as output.

[0691] Step 3:

[0692] The server collects and analyzes external data related to tourist destinations based on data received from terminals. This data processing includes sensor data and social network data. AI algorithms and statistical analysis tools are used to predict congestion levels and user area characteristics. Analysis results are generated as output.

[0693] Step 4:

[0694] The device's built-in emotion analysis system collects user emotion data through the camera and microphone. Specifically, it analyzes the user's facial expressions and tone of voice to infer their emotional state. An emotion recognition API is used for this process, and user emotion information is generated as output.

[0695] Step 5:

[0696] The server integrates emotional data with analyzed external data and uses a generative AI model to create personalized sightseeing routes and experience plans. Data processing involves complex calculations that consider user emotions and congestion levels. The final output is a route suggestion and prompt message best suited to the user. An example of a prompt message might be, "Please tell me about quiet places in Kyoto."

[0697] Step 6:

[0698] The device receives output information from the server and displays it in the user's field of view using augmented reality (AR) technology. Specifically, ARKit and ARCore are utilized, and the user receives visual guidance through the device. As output, AR display information is provided overlaid on the user's device. As a result, the user can receive real-time optimized tourist information.

[0699] (Application Example 2)

[0700] 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".

[0701] Traditional tourism systems and food delivery services have struggled to provide personalized suggestions based on users' emotional states and real-time congestion levels. This can lead to users not receiving the expected experience and experiencing decreased satisfaction. Furthermore, visiting crowded areas or stores can cause stress. Therefore, there is a need for a system that can simultaneously avoid congestion and provide the optimal experience while considering the user's emotional state.

[0702] 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.

[0703] In this invention, the server includes information processing means for analyzing the congestion status of tourist destinations and restaurants in real time, emotion recognition means for analyzing the emotional state of users, and customization suggestion means for proposing the most suitable stores and experiences to users. This makes it possible to provide individually optimized tourist and dining experiences based on the emotional state of users and congestion information.

[0704] "Information processing means" refers to devices or software that perform calculations to acquire and analyze the congestion status of tourist destinations and restaurants in real time.

[0705] A "route suggestion means" is a device or algorithm that calculates and suggests alternative routes to help users avoid congestion based on acquired congestion information.

[0706] "Emotion recognition means" refers to devices or software that have the function of understanding a user's emotional state by analyzing the user's facial expressions and movements.

[0707] A "customized suggestion method" refers to a device or system that individually suggests optimal dining facilities or tourist experiences based on user sentiment data and real-time information.

[0708] "Display means" refers to functions or devices for visually displaying proposed information as augmented reality on a user device.

[0709] "Location information acquisition means" refers to devices or technologies that have the function of identifying the user's current location and providing it to other systems.

[0710] "External information" refers to information collected from outside the system, such as sensor data and evaluation data on online platforms.

[0711] The system implementing this invention analyzes the congestion status of tourist destinations and restaurants in real time and provides an optimal experience that takes into account the user's emotional state. The system consists of information processing means, route suggestion means, emotion recognition means, customization suggestion means, display means, and location information acquisition means.

[0712] The server acquires sensor data from tourist destinations and restaurants, as well as rating data from social networks, in real time, and analyzes congestion levels using information processing tools. The server processes this data using AI tools and statistical analysis software (e.g., TensorFlow, SciPy).

[0713] Users acquire emotional data by using the cameras of their smartphones or wearable devices as a means of emotion recognition, analyzing their facial expressions and movements. This is where an emotion engine (e.g., Affectiva) comes into play.

[0714] The on-device display provides route suggestions and customized experiences received from the server in augmented reality format, presenting the user with the optimal route and experience. This allows users to avoid congestion and enjoy a personalized experience.

[0715] For example, a user might launch an application, and the emotion engine analyzes their facial expression, determining that they are seeking relaxation. In that case, the server might suggest a less crowded restaurant and recommend relaxing drinks and food.

[0716] Examples of prompts for a generative AI model are as follows:

[0717] "Please provide image data of the user's face. Next, use an emotion engine to predict what the user currently wants (e.g., relax, refresh), and based on that prediction, identify available restaurants in the current area and suggest dishes best suited for relaxation."

[0718] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0719] Step 1:

[0720] The user initiates the application on the device. The user inputs desired tourist destinations and experiences. This information, along with location data, is sent to the server by the device. In this process, input is the desired destinations and experiences, and output is the transmission of data to the server.

[0721] Step 2:

[0722] The server uses location information acquisition means to determine the user's current location. The identified location information, along with sensor data from tourist attractions and restaurants, and rating data from social networks, are acquired in real time. This data is analyzed by information processing means to evaluate congestion levels in real time. Inputs are location data and external data, and output is real-time congestion information.

[0723] Step 3:

[0724] The device utilizes emotion recognition to capture the user's facial expressions and actions with a camera, and analyzes the user's emotional state using an emotion engine. This process converts image data into emotion data and sends it to a server. The input is image data, and the output is emotional state data.

[0725] Step 4:

[0726] The server integrates sentiment data and congestion data, and uses route suggestion and customization suggestion means to calculate the optimal alternative route and experience for the user. AI and statistical analysis software are used to process this data and generate personalized suggestions for each user. The input is sentiment data and congestion data, and the output is suggestion information.

[0727] Step 5:

[0728] The device receives suggestion information from the server and displays it to the user in augmented reality format. The device provides visually rich information to enhance the user experience and support specific actions during travel. The input is suggestion information, and the output is an augmented reality display.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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."

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0750] The following is further disclosed regarding the embodiments described above.

[0751] (Claim 1)

[0752] Information processing tools for analyzing the congestion status of tourist destinations in real time,

[0753] A route suggestion means that calculates an alternative route to avoid congestion based on the information obtained by the aforementioned information processing means,

[0754] A display means that displays information from the route suggestion means as augmented reality on the user's terminal,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, further comprising means for acquiring location information to identify the user's current location.

[0758] (Claim 3)

[0759] The system according to claim 1, wherein the information processing means has a function of predicting congestion using external data including sensor data and social network data.

[0760] "Example 1"

[0761] (Claim 1)

[0762] Information processing tools for analyzing the congestion status of tourist destinations in real time,

[0763] A route suggestion means calculates the optimal alternative route and time of visit to avoid congestion based on the information obtained by the aforementioned information processing means,

[0764] A display means that displays information from the route suggestion means as augmented reality on the user's device,

[0765] A data analysis method that collects external data, performs analysis using a generated AI model, and predicts congestion,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising means for acquiring location information to identify the user's current location, and means for overlaying information onto real-world video in real time.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the data analysis means has the function of predicting congestion using sensor data and online platform data and utilizing a generated AI model.

[0771] "Application Example 1"

[0772] (Claim 1)

[0773] A data processing method for analyzing the temporal aspects of human gatherings in tourist areas,

[0774] A guidance suggestion means that calculates options to avoid the set based on the information collected by the aforementioned data processing means,

[0775] An output means that displays information from the aforementioned guidance suggestion means as virtual reality on the user's mobile device,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, further comprising means for acquiring location data to identify the user's current location.

[0779] (Claim 3)

[0780] The system according to claim 1, wherein the data processing means has a function of estimating the group situation using external information including measurement device data and online communication data.

[0781] "Example 2 of combining an emotion engine"

[0782] (Claim 1)

[0783] Information processing tools for analyzing the congestion status of tourist destinations in real time,

[0784] Based on the information obtained by the aforementioned information processing means, an emotion analysis means measures the user's emotional state and optimizes the tourism experience.

[0785] A route suggestion means that combines the data acquired by the emotion analysis means and the information processing means to calculate and propose an individualized route,

[0786] A display means that displays information from the route suggestion means as augmented reality on the user's terminal,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, comprising the function of identifying the user's current location and acquiring emotional information based on external factors using the device's sensors.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the information processing means includes a generative AI model that uses external data to predict congestion levels and generates prompts suitable for the user.

[0792] "Application example 2 when combining with an emotional engine"

[0793] (Claim 1)

[0794] Information processing tools for analyzing the congestion status of tourist destinations and restaurants in real time,

[0795] A route suggestion means that calculates an alternative route to avoid congestion based on the information obtained by the aforementioned information processing means,

[0796] An emotion recognition method for analyzing the user's emotional state,

[0797] A customization suggestion means that proposes the most suitable store or experience to the user based on data from the aforementioned emotion recognition means,

[0798] A display means that displays information from the route suggestion means and the customization suggestion means as augmented reality on the user device,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising means for acquiring location information to determine the user's current location.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the information processing means has a function of predicting congestion status using external information including sensor data and evaluation data on a network. [Explanation of Symbols]

[0804] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing method for analyzing the temporal aspects of human gatherings in tourist areas, A guidance suggestion means that calculates options to avoid the set based on the information collected by the aforementioned data processing means, An output means that displays information from the aforementioned guidance suggestion means as virtual reality on the user's mobile device, A system that includes this.

2. The system according to claim 1, further comprising means for acquiring location data to identify the user's current location.

3. The system according to claim 1, wherein the data processing means has a function of estimating the group situation using external information including measurement device data and online communication data.

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