system

Abstract

Provide a system. 【Solution means】 Means for receiving a user's face image, Means for generating a visual artificial agent based on the face image, Means for synthesizing the visual artificial agent into existing destination visual information, Means for providing the synthesized visual information to the user, Means for collecting visual information data according to the user's intention display, Means for generating an access plan based on the collected data, Means for presenting the access plan to the user, Means for a user terminal operating on a portable information processing device, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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] It is required to enable a user who cannot decide a travel destination to specifically imagine a travel destination of interest before actually visiting. In addition, there is a lack of means for more accurately grasping the user's preferences and interests and easily proposing a travel plan based on them. Thus, it is an issue to assist in the decision-making of travel plans.

Means for Solving the Problems

[0005] This invention provides a means for generating an avatar that reproduces the user's characteristics using the user's facial image as input data, and then synthesizing it with images of travel destinations around the world. The generated images are then presented to the user, and the user's selection of images of interest is collected. Based on the collected data, a machine learning model is used to analyze the user's preferences, thereby creating and proposing an optimal travel plan for the user, and thus solving the above-mentioned problem.

[0006] A "user" is an individual who uses the system to provide their facial image and receive travel information.

[0007] A "face image" is visual information that captures the features of a user's face, and it is the data that forms the basis for avatar generation.

[0008] An "avatar" is a digital representation generated from a user's facial image, reproducing the user's characteristics.

[0009] "Travel destination images" are photographs or image data of real landscapes and tourist destinations around the world.

[0010] "Synthesis" is the process of combining an avatar with images of the travel destination, generating a single, unified image.

[0011] A "machine learning model" is an algorithm used to analyze user choices and model specific data patterns.

[0012] A "travel plan" is a specific travel proposal presented to the user, including destinations, length of stay, sightseeing spots, transportation, etc. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system that generates individual avatars based on facial images provided by users, and then combines these avatars with images of landscapes and tourist attractions of travel destinations, thereby providing users with a visual experience as if they had actually visited those places. By introducing this system, users can have the opportunity to discover new travel destinations and receive detailed travel plans for places that interest them.

[0035] The server first receives a facial image sent from the user's device. Next, it uses AI technology to analyze the user's facial features based on this image and generates an avatar. The server then uses this avatar data to superimpose it onto a background of a location selected from an existing database of travel destination images.

[0036] This composite image is delivered to the user's device. The user reviews each image, in which their avatar is superimposed onto different travel destinations, and marks the images they are interested in. The device then sends the user's selection information to the server.

[0037] The server analyzes user preferences based on collected user selection information. Machine learning models are used for this analysis, comparing the user's choices with data from similar past users. Based on this analysis, the server generates a travel plan tailored to the user. This plan includes specific destination information, duration of stay, and mode of transportation. The created travel plan is finally presented to the user via their device.

[0038] This system allows users to plan their trips after seeing an actual image of their destination, resulting in a more satisfying travel experience. This technology will also be used as a foundation for providing personalized services based on users' preferences and tastes.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uploads their facial image to the system using a device. The device receives input from the user and sends the facial image to the server.

[0042] Step 2:

[0043] The server analyzes the received facial image and uses an AI algorithm to model the user's features. This generates an avatar. This generation process includes techniques to extract points of facial features and convert them into a digital representation.

[0044] Step 3:

[0045] The server uses the generated avatar to composite it onto a background selected from a database of travel destination images. This background selection includes tourist hotspots and landmarks. This compositing process creates images that make it appear as if the user's avatar is visiting various locations.

[0046] Step 4:

[0047] The server sends synthesized travel images to the user's device. The images delivered are different each day, designed to pique the user's interest in new locations.

[0048] Step 5:

[0049] Users review the images they receive on their devices and check the travel destination images that interest them. Users then select places they would like to actually visit.

[0050] Step 6:

[0051] The terminal sends the user's selection information back to the server. The server stores the received selection information in its database.

[0052] Step 7:

[0053] The server uses machine learning models within its engine to analyze the user's interests and preferences. This generates a travel plan optimized for the user, which includes detailed information about the regions deemed of interest and a travel schedule.

[0054] Step 8:

[0055] The server sends the generated travel plan to the terminal and presents it to the user. The user can review the provided plan and make reservations or inquiries as needed.

[0056] (Example 1)

[0057] 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."

[0058] Modern travel planning is diverse, making it difficult to reflect individual user preferences. Furthermore, users often lack a concrete image of their destination before actually visiting, potentially leading to lower post-trip satisfaction. Additionally, efficiently processing large amounts of information during the information gathering and user preference analysis processes for travel planning is challenging.

[0059] 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.

[0060] In this invention, the server includes means for receiving a user's facial image and generating an avatar using AI technology, means for compositing the avatar with a background image using image processing software, and means for analyzing selection information using a machine learning model and generating a travel plan using a generation AI model. This makes it possible to provide personalized travel plans that reflect the user's preferences, allowing the user to have a concrete image of their travel destination before traveling and enabling a highly satisfying travel experience.

[0061] "Means for receiving user facial images" refers to communication technologies or protocols for acquiring facial image data sent from a user's terminal.

[0062] "A method for generating avatars using AI technology" refers to an artificial intelligence algorithm that analyzes a user's facial image and creates a digital character that reflects its features.

[0063] "Methods of compositing using image processing software" refers to programs that seamlessly integrate a generated avatar with a selected background image to create a visually consistent composite image.

[0064] "Means of sending to the user's terminal" refers to network technology used to transfer data from a server to a user's terminal.

[0065] "Methods for analyzing selection information using machine learning models" refer to machine learning algorithms that analyze user selection data, detect similar patterns, and identify user preferences.

[0066] "A method for generating travel plans using a generative AI model" refers to artificial intelligence technology that automatically creates the optimal travel plan based on the results of user preference analysis.

[0067] A "background image" is image data that shows the scenery or tourist attractions of a travel destination, and is a visual element that forms the basis of a composite image.

[0068] "User selection information" refers to data on the preferences a user has shown towards composite images that they are interested in, and serves as basic information for preference analysis.

[0069] A "travel plan" is a document or data set of plans regarding destinations, length of stay, and means of transportation, suggested based on the user's preferences.

[0070] This invention implements a system that generates individual avatars based on facial images provided by the user, and then combines these avatars with background images of various travel destinations to propose personalized travel plans to the user.

[0071] The server first receives a facial image sent from the user's device. After receiving the image, the image recognition process primarily uses software such as a "facial recognition library," which analyzes the facial features using AI technology. This analysis generates an avatar that reflects the user's features. A "generative AI model," such as "StyleGAN," may be used for generation.

[0072] Next, the server uses "image processing software" to composite the generated avatar with the background image of the selected travel destination. In this process, a script using common image editing tools such as "Adobe Photoshop" adjusts the size, brightness, and color tone of the avatar to match the background, creating a harmonious composite image.

[0073] Once the composite image is complete, the server sends the image to the user's device. The user can then view the avatar superimposed onto various travel destination background images and select the images that interest them.

[0074] The server receives user selection information and analyzes user preferences using machine learning models. "Scikit-learn" and "TENSORFLOW®" are used for the analysis, providing data to generate the most suitable travel plan for the user. The travel plan, generated using an AI model, includes destinations, length of stay, and transportation methods tailored to the user's preferences.

[0075] Finally, the generated travel plan is presented to the user's device. The user can review it and make concrete travel plans. Therefore, this invention makes it possible to provide users with a highly satisfying travel experience.

[0076] An example of a prompt message is, "Generate a travel plan to Hiroshima for a user who has shown interest in an image with Shukkei-en Garden in the background." In this way, by generating an appropriate travel plan based on the user's selection, travel suggestions tailored to individual preferences can be achieved.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uploads a facial image to the device. This input facial image is digital data that reflects the user's identity. The device initiates communication to send this image data to the server.

[0080] Step 2:

[0081] The server acquires the facial image received from the terminal. The received image data is used as input for analyzing facial features using a facial recognition library. Specifically, landmark detection of the face is performed, each feature point is extracted as numerical data, and this is supplied to the feature analysis process of the AI ​​model.

[0082] Step 3:

[0083] The server uses AI technology to generate an avatar based on facial feature data. During this process, the generated digital character is algorithmically adjusted to resemble the user's image in shape and color. The output is the user's avatar image.

[0084] Step 4:

[0085] The server uses image processing software to composite the generated avatar image with a background image. The input consists of an avatar image and several pre-prepared background images of travel destinations. During image compositing, adjustments such as the avatar's size and position are performed by an automated script, and the output is a composite image.

[0086] Step 5:

[0087] The server sends the generated composite images to the user's device. The user views the received composite images through their device and examines each one. They then select the composite images they are interested in and save the selection information on their device.

[0088] Step 6:

[0089] The terminal sends user selection information to the server. The server receives this selection information as input data for analysis and starts the analysis using a machine learning model. Specifically, data calculations are performed to detect preference patterns based on the user's preference data, and analysis results regarding preferences are obtained as output.

[0090] Step 7:

[0091] The server utilizes a generative AI model and, using the results of a preference analysis as input, generates an optimal travel plan for the user. In this generation process, prompt examples are used to suggest travel destinations, duration, and modes of transportation that match the user's preferences, and the output is data for the travel plan.

[0092] Step 8:

[0093] The server sends the generated travel plan to the user's device. The user can then review the travel plan on their device and use it to plan their actual trip. This allows users to enjoy a personalized travel experience tailored to their individual preferences.

[0094] (Application Example 1)

[0095] 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."

[0096] Modern travel planning is time-consuming and laborious, and it's difficult to provide information tailored to individual user preferences. Furthermore, there's a lack of efficient systems for users to easily discover new destinations and visually experience them. This makes it difficult to provide personalized travel experiences, potentially leading to lower user satisfaction.

[0097] 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.

[0098] In this invention, the server includes means for receiving a user's facial image, means for generating a visual artificial surrogate based on the facial image, and means for synthesizing the visual artificial surrogate with existing destination visual information. This allows the user to have the experience of virtually visiting a new travel destination and to intuitively discover new travel destinations. It also facilitates the suggestion of visit plans based on the user's selection trends, making it possible to provide a highly personalized travel experience.

[0099] "Means for receiving user facial images" refers to methods for acquiring digital image data of a user's face provided via a terminal or the internet.

[0100] "Means for generating visual artificial surrogates" refer to the technologies and processes necessary to analyze a user's facial features and generate a digital avatar.

[0101] "Means of synthesizing with destination visual information" refers to a technical process that combines a generated artificial surrogate with images or videos of the destination, providing the user with a visual experience that makes them feel as if they are actually there.

[0102] "Means of collecting visual information data in response to user expressions" refers to methods of collecting information that users have shown interest in or selected, in digital format.

[0103] "Means for generating visit plans" refers to the process of creating a customized travel schedule based on the user's choices and preferences.

[0104] "Means for user terminals operating on portable information processing devices" refers to methods for running software or applications on portable devices such as smartphones and tablets.

[0105] To realize this invention, a server, a user terminal, and related software are utilized. The server receives facial images from the user's portable information processing device. Based on the received facial images, facial features are analyzed using AI technologies such as TensorFlow to generate a visual avatar. This avatar generation is important for providing a personalized visual experience.

[0106] The server synthesizes the generated artificial avatar with the user's selected destination visual information. Image editing software such as Photoshop or GIMP may be used for this synthesis process. As a result, the user can have a visual experience as if their avatar were actually at the travel destination.

[0107] On the user's terminal, the user can view the presented visual information and select images that interest them. The selection information is then sent back to the server, which uses this data to analyze the user's preferences using a machine learning model.

[0108] The server then generates a visit plan based on the user's preferences. This plan includes customized information such as the length of stay and mode of transportation. The generated plan is presented through an application on the user's portable information device. This process is often implemented using software frameworks such as Python or Flask.

[0109] As a concrete example, if a user selects a beach in Okinawa, a composite image of an avatar standing on that beach will be generated. If the user likes the plan after seeing this image and wants to visit the location, a specific travel plan will be presented. The travel plan will include "recommended spots and activities for sightseeing in Okinawa over 3 nights and 4 days."

[0110] An example of a prompt message is: "Analyze the user's facial features and generate a personalized avatar. Then, create a program that combines this avatar with a beach scene of Okinawa selected by the user to provide a visual experience."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives facial images from the user's terminal. The input is digital facial image data, and once received, it converts and saves it in an appropriate format for use in the next step.

[0114] Step 2:

[0115] The server generates a visual surrogate based on the received facial image. This involves a process that uses TensorFlow to analyze the user's facial features and generate a digital avatar. The input is processed facial image data, and the output is avatar data with the user's features.

[0116] Step 3:

[0117] The server synthesizes the generated artificial avatar with existing destination visual information. This process uses image processing software such as Photoshop or GIMP to integrate the avatar and background image. The input is avatar data and a selected landscape image, and the output is the synthesized visual information.

[0118] Step 4:

[0119] The user terminal displays synthesized visual information to the user. The user reviews this visual information and makes selections based on the information that interests them. The input is the synthesized image, and the output is the user's selection information.

[0120] Step 5:

[0121] The server receives user selection information and analyzes preferences using a machine learning model. The input is user selection data, and by analyzing this data, it obtains preference information necessary to generate a travel plan suitable for the user.

[0122] Step 6:

[0123] The server generates personalized travel plans based on analyzed preferences. These plans include destinations and modes of transportation, and are created using Python or the Flask framework. The input is preference information, and the output is a detailed travel plan.

[0124] Step 7:

[0125] The server sends the generated travel plan to the user's terminal and presents it to the user. The user can review the displayed plan and use it to help make a travel decision. The input is travel plan data, and the output is the final travel plan information presented.

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

[0127] This invention is a system that generates individual avatars based on facial images provided by the user, combines them with images of the travel destination, and recognizes the user's emotions to provide a more personalized travel experience. The introduction of an emotion engine makes it possible to create avatar expressions and suggest travel plans that reflect the user's emotional state.

[0128] The server first receives a facial image sent from the user's device. The received image is analyzed using an AI algorithm to determine its facial feature points and generate an avatar that replicates the user's characteristics. At this time, an emotion engine is used to identify the user's emotions during the image analysis and dynamically adjust the avatar's facial expression.

[0129] The generated avatar is combined with a selected travel destination image to create a composite image that makes it appear as if the user has visited that place. The composite image is delivered to the user's device, and the user can experience the image in a way that reflects their own emotional state through an emotion engine.

[0130] When a user selects an image of interest, that selection information is sent from the device to the server. The server uses an emotion engine to store and analyze the selection information, including the user's emotional state. Based on the results of this emotion analysis, a machine learning model analyzes the user's preferences more precisely and creates a travel plan accordingly.

[0131] The generated travel plan takes into account the user's current emotional state, suggesting destinations, schedules, and travel experiences that will resonate more emotionally. The server sends the completed travel plan to the user's device, allowing them to make reservations and check detailed information based on it.

[0132] For example, if a user provides a facial image while relaxing at home, the emotion engine can detect this "relaxed" state and display a calmer expression on the avatar's face, or suggest a travel plan that prioritizes relaxation. In this way, users can experience a more relatable and emotionally satisfying travel plan that reflects their emotions.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uploads their facial image to the system using a device. The device receives the user's input and sends the facial image to the server.

[0136] Step 2:

[0137] The server applies an AI algorithm to analyze the received facial image. It extracts facial feature points and generates an avatar. During this process, the server uses an emotion engine to identify the user's emotional state and adjusts the avatar's facial expressions and posture accordingly.

[0138] Step 3:

[0139] The server combines the generated avatar with existing travel destination images. This combined image is adjusted so that the avatar's facial expressions change according to its emotional state.

[0140] Step 4:

[0141] The server selects the synthesized travel images and sends them to the user's device. The user can then view their avatar in various locations.

[0142] Step 5:

[0143] Users select images that interest them from those displayed on their device and provide selection information along with the corresponding emotions.

[0144] Step 6:

[0145] The device sends information about the images and emotions selected by the user to the server.

[0146] Step 7:

[0147] The server uses an emotion engine and machine learning models to analyze the received selection information and emotional state. Based on this, it automatically generates travel plans that are suitable for the user's preferences and emotions.

[0148] Step 8:

[0149] The server sends the generated travel plan to the user's device and presents it to the user. The user can then use this plan to create a more detailed travel plan.

[0150] (Example 2)

[0151] 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".

[0152] In modern travel planning, providing a personalized experience that accurately reflects a user's individual preferences and emotions is a challenging task. Traditional travel planning tends to offer uniform suggestions based on general information, and there is a need for individualized differentiation that exceeds user expectations.

[0153] 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.

[0154] In this invention, the server includes a device for receiving images of the user, a device for generating a virtual person based on the images, and a device for compositing the virtual person onto an arbitrary landscape image. This makes it possible to suggest travel experiences based on the user's individual emotions and preferences.

[0155] A "user" refers to a person who uses this system to create their own individual travel experience.

[0156] "Image" refers to visual information that represents a user, landscape, or other subject in digital format.

[0157] A "virtual character" refers to a digital avatar generated based on the user's characteristics.

[0158] "Landscape images" refer to visual information that shows a specific location, such as a travel destination.

[0159] "Image synthesis" refers to the process of combining multiple images to create a single, unified image.

[0160] "Emotion analysis" refers to a method of identifying a user's emotional state from their facial image or other data.

[0161] "Information storage" refers to the process of saving user choices and emotional data in a database or similar system.

[0162] "Itinerary" refers to a travel plan that includes the schedule and destinations planned for the user.

[0163] An "artificial intelligence model" refers to an algorithm that analyzes data and predicts user preferences and behavior.

[0164] This invention is a system for users to create personalized travel experiences. Specific embodiments are shown below.

[0165] The server first receives a facial image sent by the user using their device. This facial image is then processed using AI algorithms to analyze the user's individual characteristics. Python, OpenCV, TensorFlow, and other machine learning libraries are used for this processing.

[0166] The server uses these techniques to generate a virtual person, or avatar, that resembles the user. During this process, an emotion analysis engine is used to identify the user's current emotions and reflect them in the avatar's facial expressions. General-purpose emotion analysis tools and libraries are likely to be used for this process.

[0167] The server then composites the generated avatar image onto the selected landscape image. This compositing process utilizes scripts within image editing software such as Photoshop or GIMP. This creates a composite image that makes it appear as if the user is actually visiting the location.

[0168] The device receives the completed composite image and presents it to the user. The user can then enjoy a new experience that reflects their emotions while viewing the composite image on the screen.

[0169] Furthermore, users select items that interest them from the displayed composite images and send this selection information to the server via their device. The server stores the received selection information and uses a machine learning model to analyze the user's preferences based on this data. Through this process, a personalized travel plan that responds to the user's emotions is created.

[0170] One concrete example of this system is a feature where, based on a facial image provided when the user is relaxed, the emotion analysis engine detects a "relaxed" state and gives the avatar a calm expression. This makes it possible to suggest travel plans that prioritize relaxation. An example of a prompt message would be, "Generate an avatar that resembles a user in a relaxed state, and present it with images of relaxing travel destinations."

[0171] In this way, the system provides a travel experience that takes into account the user's emotions and preferences.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user takes a facial image using a device and sends it to the image processing system. The input is the user's facial image, and the output is image data received on the server side. This image is then ready for analysis in subsequent processing.

[0175] Step 2:

[0176] The server analyzes the received facial image using an AI algorithm. The input is the received facial image, and the output is facial feature information. Specifically, it analyzes facial feature points using OpenCV, processes that data using a framework such as TensorFlow, and quantifies the user's facial features. This information is used for avatar generation.

[0177] Step 3:

[0178] The server uses an AI model to generate a virtual character (avatar) that resembles the user, based on the analyzed facial feature information. The output is the generated avatar image. This process results in a digital character that reflects the user's individuality.

[0179] Step 4:

[0180] The server performs emotion analysis during avatar generation and obtains emotion data based on the user's facial image. The input is the user's facial image and its analysis information, and the output is information about the user's emotional state. This information is reflected in the avatar as facial expressions by the emotion analysis engine.

[0181] Step 5:

[0182] The server combines the generated avatar image with a selected landscape image. The input is the avatar image and the landscape image, and the output is a composite image combining the two images. An automated processing tool in image editing software is used for this compositing.

[0183] Step 6:

[0184] The device receives the synthesized image from the server and displays it to the user. The input is the synthesized image sent by the server, and the output is the image presented to the user as visual information. This allows the user to create a virtual visualization of their travel experience that reflects their emotions.

[0185] Step 7:

[0186] Users select images that interest them from a collection of composite images and send this information to the server via their device. The input is the user's image selection information, and the output is the selection data sent to the server. This selection behavior reveals the user's preferences.

[0187] Step 8:

[0188] The server uses a machine learning model to perform preference analysis based on the information selected by the user. The output is the analyzed user preference data. This allows for the accumulation of data based on the user's emotions and past choices.

[0189] Step 9:

[0190] The server uses the analyzed preference data to generate a travel itinerary tailored to the user's emotions and sends it to the terminal. The input is preference analysis data, and the output is user-specific travel itinerary information. As a result, a travel plan tailored to the user's needs is provided.

[0191] (Application Example 2)

[0192] 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".

[0193] Current technology suffers from a lack of individuality and emotional reflection when users experience virtual travel. There is a need to provide avatars and travel plans that reflect each user's individual expressions and emotions to create a more personalized experience.

[0194] 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.

[0195] In this invention, the server includes means for receiving user facial data, means for generating individual virtual characters based on the facial data, means for presenting the synthesized visual data to the user, means for detecting the user's emotional state and dynamically adjusting the virtual character's facial expressions based on the corresponding emotion, and means for adjusting the travel plan according to the user's emotional state and suggesting destinations that resonate with the user's emotions. This enables a personalized virtual travel experience based on the user's emotions and preferences.

[0196] A "user" is an individual who uses the system to have a virtual travel experience.

[0197] "Appearance data" refers to image data that includes the user's facial features.

[0198] A "virtual character" is a digital character generated based on the user's facial data.

[0199] "Visiting destination visual data" refers to image or video data of the location that is the subject of the virtual trip.

[0200] "Synthesis" is the process of creating a single image by combining a virtual person with visual data of a visited location.

[0201] "Emotional state" refers to the user's current mental state, and is information judged from their facial expressions and behavior.

[0202] A "travel plan" is a schedule of destinations and activities suggested based on the user's emotions and preferences.

[0203] The server first receives facial data transmitted from the user's terminal. The received data can be processed using OpenCV, an image processing software, to analyze facial feature points. This analysis generates a virtual character based on the user's facial data. For generating the virtual character, 3D modeling tools such as Blender or Unity are used to create a character with dynamic, emotionally expressive facial expressions.

[0204] Next, the server uses an emotion engine to identify the user's emotional state from their facial data. In this process, an emotion recognition model, pre-trained using TensorFlow, infers the emotion from the user's facial expressions.

[0205] The generated virtual character is combined with visual data of the destination selected by the user. This combination is performed in real time and displayed on the user's device. Real-time rendering functions provided by Unity or Blender can be used to combine the virtual character with the background.

[0206] Based on the items the user has shown interest in, the server that provides travel plans uses machine learning models to analyze the user's selection patterns. In this process, the user's past selection history and emotional state are used as input to suggest more personalized destinations and schedules.

[0207] For example, if a user accesses this system while relaxing at home on a holiday, the server will detect a "relaxed" mood. A travel plan suited to relaxation will be suggested, including destinations surrounded by nature, and the virtual character's facial expression will be synthesized to reflect a calm state.

[0208] An example of a prompt message is: "Provide a user's facial image, analyze their emotions in real time to generate a personalized avatar, and then composite that avatar onto the background of the selected travel destination and display it."

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server receives facial data sent from the user's terminal. The input is the user's facial image data, which is held for the next processing step.

[0212] Step 2:

[0213] The server analyzes the received facial data using OpenCV to identify facial feature points. The input is the facial image received in step 1, and the output is the identified feature point data. Based on this feature point data, the server performs skeletal mapping of a virtual person and prepares to generate individual virtual characters.

[0214] Step 3:

[0215] The server uses TensorFlow to analyze the facial expression portion of the facial data and infer the user's emotional state. The input is the facial feature point data obtained in step 2, and the output is the identified emotional state data. This emotional data is used to dynamically adjust the facial expressions of the virtual character.

[0216] Step 4:

[0217] The server generates virtual characters using Blender or Unity and combines them with the visual data of the visited location. The input is the facial feature point data obtained in step 2 and the emotion data obtained in step 3, and the output is the combined visual data. In addition to generating virtual characters, this combination process also performs real-time rendering and processes the data into a format that can be immediately displayed to the user.

[0218] Step 5:

[0219] The server sends the synthesized visual data to the user's device, allowing the user to view it in real time. The input is the synthesized visual data from step 4, and the output is the travel experience image displayed on the user's device.

[0220] Step 6:

[0221] The user selects items of interest from the suggested visual data, and this selection information is sent back to the server. The input is the user's selection data, and the next steps are executed based on this selection.

[0222] Step 7:

[0223] The server generates a travel plan using a machine learning model based on the selected data and past selection history. The input is the selected data obtained from step 6, and the output is updated or newly generated travel plan data. This travel plan is adjusted to take into account the user's emotional state and preferences.

[0224] Step 8:

[0225] The server presents the generated travel plan to the user, allowing the user to consider their next steps. The input is the travel plan data from step 7, and the output is the detailed travel plan displayed on the user's terminal.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

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

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] This invention is a system that generates individual avatars based on facial images provided by users, and then combines these avatars with images of landscapes and tourist attractions of travel destinations, thereby providing users with a visual experience as if they had actually visited those places. By introducing this system, users can have the opportunity to discover new travel destinations and receive detailed travel plans for places that interest them.

[0243] The server first receives a facial image sent from the user's device. Next, it uses AI technology to analyze the user's facial features based on this image and generates an avatar. The server then uses this avatar data to superimpose it onto a background of a location selected from an existing database of travel destination images.

[0244] This composite image is delivered to the user's device. The user reviews each image, in which their avatar is superimposed onto different travel destinations, and marks the images they are interested in. The device then sends the user's selection information to the server.

[0245] The server analyzes user preferences based on collected user selection information. Machine learning models are used for this analysis, comparing the user's choices with data from similar past users. Based on this analysis, the server generates a travel plan tailored to the user. This plan includes specific destination information, duration of stay, and mode of transportation. The created travel plan is finally presented to the user via their device.

[0246] This system allows users to plan their trips after seeing an actual image of their destination, resulting in a more satisfying travel experience. This technology will also be used as a foundation for providing personalized services based on users' preferences and tastes.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The user uploads their facial image to the system using a device. The device receives input from the user and sends the facial image to the server.

[0250] Step 2:

[0251] The server analyzes the received facial image and uses an AI algorithm to model the user's features. This generates an avatar. This generation process includes techniques to extract points of facial features and convert them into a digital representation.

[0252] Step 3:

[0253] The server uses the generated avatar to composite it onto a background selected from a database of travel destination images. This background selection includes tourist hotspots and landmarks. This compositing process creates images that make it appear as if the user's avatar is visiting various locations.

[0254] Step 4:

[0255] The server sends synthesized travel images to the user's device. The images delivered are different each day, designed to pique the user's interest in new locations.

[0256] Step 5:

[0257] Users review the images they receive on their devices and check the travel destination images that interest them. Users then select places they would like to actually visit.

[0258] Step 6:

[0259] The terminal sends the user's selection information back to the server. The server stores the received selection information in its database.

[0260] Step 7:

[0261] The server uses machine learning models within its engine to analyze the user's interests and preferences. This generates a travel plan optimized for the user, which includes detailed information about the regions deemed of interest and a travel schedule.

[0262] Step 8:

[0263] The server sends the generated travel plan to the terminal and presents it to the user. The user can review the provided plan and make reservations or inquiries as needed.

[0264] (Example 1)

[0265] 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."

[0266] Modern travel planning is diverse, making it difficult to reflect individual user preferences. Furthermore, users often lack a concrete image of their destination before actually visiting, potentially leading to lower post-trip satisfaction. Additionally, efficiently processing large amounts of information during the information gathering and user preference analysis processes for travel planning is challenging.

[0267] 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.

[0268] In this invention, the server includes means for receiving a user's facial image and generating an avatar using AI technology, means for compositing the avatar with a background image using image processing software, and means for analyzing selection information using a machine learning model and generating a travel plan using a generation AI model. This makes it possible to provide personalized travel plans that reflect the user's preferences, allowing the user to have a concrete image of their travel destination before traveling and enabling a highly satisfying travel experience.

[0269] "Means for receiving user facial images" refers to communication technologies or protocols for acquiring facial image data sent from a user's terminal.

[0270] "A method for generating avatars using AI technology" refers to an artificial intelligence algorithm that analyzes a user's facial image and creates a digital character that reflects its features.

[0271] "Methods of compositing using image processing software" refers to programs that seamlessly integrate a generated avatar with a selected background image to create a visually consistent composite image.

[0272] "Means of sending to the user's terminal" refers to network technology used to transfer data from a server to a user's terminal.

[0273] "Methods for analyzing selection information using machine learning models" refer to machine learning algorithms that analyze user selection data, detect similar patterns, and identify user preferences.

[0274] "A method for generating travel plans using a generative AI model" refers to artificial intelligence technology that automatically creates the optimal travel plan based on the results of user preference analysis.

[0275] A "background image" is image data that shows the scenery or tourist attractions of a travel destination, and is a visual element that forms the basis of a composite image.

[0276] "User selection information" refers to data on the preferences a user has shown towards composite images that they are interested in, and serves as basic information for preference analysis.

[0277] A "travel plan" is a document or data set of plans regarding destinations, length of stay, and means of transportation, suggested based on the user's preferences.

[0278] This invention implements a system that generates individual avatars based on facial images provided by the user, and then combines these avatars with background images of various travel destinations to propose personalized travel plans to the user.

[0279] The server first receives the face image sent from the user's terminal. After receiving it, in the process of image recognition, mainly software such as a "face recognition library" is used to analyze the features of the face using AI technology. Through this analysis, an avatar reflecting the user's characteristics is generated. For the generation, a "generative AI model", such as "StyleGAN", may be used.

[0280] Next, the server uses "image processing software" to synthesize the generated avatar with the background image of the selected travel destination. In this process, a script using a general image editing tool such as "Adobe Photoshop" adjusts the size, brightness, and color tone of the avatar to match the background, creating a harmonious composite image.

[0281] When the composite image is completed, the server sends the image to the user's terminal. The user checks the avatars synthesized with the background images of various travel destinations and makes a selection for the images they are interested in.

[0282] The server receives the user's selection information and analyzes the user's preferences using a machine learning model. "Scikit-learn" and "TensorFlow" are used for the analysis, providing data for generating the travel plan most suitable for the user. The travel plan utilizes the generative AI model and includes destinations to visit, stay periods, transportation means, etc., according to the user's preferences.

[0283] Finally, the generated travel plan is presented to the user's terminal. The user can check this and make a specific travel plan. Therefore, with this invention, it becomes possible to provide a highly satisfactory travel experience for the user.

[0284] As an example of a prompt sentence, there is "Please generate a travel plan to Hiroshima for a user who showed interest in an image with a miniature garden of the travel destination as the background." In this way, by generating an appropriate travel plan based on the user's selection, travel proposals according to individual preferences are realized.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user uploads a face image to the terminal. This input face image is digital data that reflects the user's identity. The terminal starts communication to send this image data to the server.

[0288] Step 2:

[0289] The server acquires the face image received from the terminal. The received image data is used as input for analyzing the features of the face using a face recognition library. Specifically, landmark detection of the face is performed, each feature point is extracted as numerical data, and this is supplied to the feature analysis process of the AI model.

[0290] Step 3:

[0291] The server uses AI technology to generate an avatar based on the face feature data. In this process, the generated digital character is adjusted by an algorithm so that it has a shape and color similar to the user's image. As an output, the user's avatar image is obtained.

[0292] Step 4: <00XXXXXX>

[0293] The server uses image processing software to composite the generated avatar image with a background image. As inputs, the avatar image and a plurality of background images of pre-prepared travel destinations are provided. In image composition, adjustments such as the size and arrangement of the avatar are performed by an automated script, and as an output, a composite image is generated.

[0294] Step 5:

[0295] The server sends the generated composite images to the user's device. The user views the received composite images through their device and examines each one. They then select the composite images they are interested in and save the selection information on their device.

[0296] Step 6:

[0297] The terminal sends user selection information to the server. The server receives this selection information as input data for analysis and starts the analysis using a machine learning model. Specifically, data calculations are performed to detect preference patterns based on the user's preference data, and analysis results regarding preferences are obtained as output.

[0298] Step 7:

[0299] The server utilizes a generative AI model and, using the results of a preference analysis as input, generates an optimal travel plan for the user. In this generation process, prompt examples are used to suggest travel destinations, duration, and modes of transportation that match the user's preferences, and the output is data for the travel plan.

[0300] Step 8:

[0301] The server sends the generated travel plan to the user's device. The user can then review the travel plan on their device and use it to plan their actual trip. This allows users to enjoy a personalized travel experience tailored to their individual preferences.

[0302] (Application Example 1)

[0303] 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."

[0304] Modern travel planning is time-consuming and laborious, and it is difficult to provide information tailored to individual users' preferences. Additionally, there is an issue that there is no efficient system for users to easily discover new travel destinations and visually experience those locations. As a result, it is difficult to offer individualized travel experiences, which may lead to low user satisfaction.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for receiving a user's face image, means for generating a visual artificial agent based on the face image, and means for synthesizing the visual artificial agent with existing destination visual information. As a result, the user can obtain an experience of virtually visiting a new travel destination and can intuitively discover new travel destinations. Also, it becomes easier to propose a visit plan based on the user's selection trends, and it is possible to provide a highly personalized travel experience.

[0307] The "means for receiving a user's face image" is a method for acquiring digital image data of a face provided by a user via a terminal or the Internet.

[0308] The "means for generating a visual artificial agent" is the technology and process necessary for analyzing a user's facial features and generating a digital avatar.

[0309] The "means for synthesizing with destination visual information" is a technical process of combining the generated artificial agent with an image or video of a destination, and is for providing a visual experience as if the user were at that location.

[0310] The "means for collecting data of visual information according to a user's intention indication" is a method of collecting information that the user has shown interest in or selected in digital form.

[0311] "Means for generating visit plans" refers to the process of creating a customized travel schedule based on the user's choices and preferences.

[0312] "Means for user terminals operating on portable information processing devices" refers to methods for running software or applications on portable devices such as smartphones and tablets.

[0313] To realize this invention, a server, a user terminal, and related software are utilized. The server receives facial images from the user's portable information processing device. Based on the received facial images, facial features are analyzed using AI technologies such as TensorFlow to generate a visual avatar. This avatar generation is important for providing a personalized visual experience.

[0314] The server synthesizes the generated artificial avatar with the user's selected destination visual information. Image editing software such as Photoshop or GIMP may be used for this synthesis process. As a result, the user can have a visual experience as if their avatar were actually at the travel destination.

[0315] On the user's terminal, the user can view the presented visual information and select images that interest them. The selection information is then sent back to the server, which uses this data to analyze the user's preferences using a machine learning model.

[0316] The server then generates a visit plan based on the user's preferences. This plan includes customized information such as the length of stay and mode of transportation. The generated plan is presented through an application on the user's portable information device. This process is often implemented using software frameworks such as Python or Flask.

[0317] As a concrete example, if a user selects a beach in Okinawa, a composite image of an avatar standing on that beach will be generated. If the user likes the plan after seeing this image and wants to visit the location, a specific travel plan will be presented. The travel plan will include "recommended spots and activities for sightseeing in Okinawa over 3 nights and 4 days."

[0318] An example of a prompt message is: "Analyze the user's facial features and generate a personalized avatar. Then, create a program that combines this avatar with a beach scene of Okinawa selected by the user to provide a visual experience."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server receives facial images from the user's terminal. The input is digital facial image data, and once received, it converts and saves it in an appropriate format for use in the next step.

[0322] Step 2:

[0323] The server generates a visual surrogate based on the received facial image. This involves a process that uses TensorFlow to analyze the user's facial features and generate a digital avatar. The input is processed facial image data, and the output is avatar data with the user's features.

[0324] Step 3:

[0325] The server synthesizes the generated artificial avatar with existing destination visual information. This process uses image processing software such as Photoshop or GIMP to integrate the avatar and background image. The input is avatar data and a selected landscape image, and the output is the synthesized visual information.

[0326] Step 4:

[0327] The user terminal displays synthesized visual information to the user. The user reviews this visual information and makes selections based on the information that interests them. The input is the synthesized image, and the output is the user's selection information.

[0328] Step 5:

[0329] The server receives user selection information and analyzes preferences using a machine learning model. The input is user selection data, and by analyzing this data, it obtains preference information necessary to generate a travel plan suitable for the user.

[0330] Step 6:

[0331] The server generates personalized travel plans based on analyzed preferences. These plans include destinations and modes of transportation, and are created using Python or the Flask framework. The input is preference information, and the output is a detailed travel plan.

[0332] Step 7:

[0333] The server sends the generated travel plan to the user's terminal and presents it to the user. The user can review the displayed plan and use it to help make a travel decision. The input is travel plan data, and the output is the final travel plan information presented.

[0334] 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.

[0335] This invention is a system that generates individual avatars based on facial images provided by the user, combines them with images of the travel destination, and recognizes the user's emotions to provide a more personalized travel experience. The introduction of an emotion engine makes it possible to create avatar expressions and suggest travel plans that reflect the user's emotional state.

[0336] The server first receives a facial image sent from the user's device. The received image is analyzed using an AI algorithm to determine its facial feature points and generate an avatar that replicates the user's characteristics. At this time, an emotion engine is used to identify the user's emotions during the image analysis and dynamically adjust the avatar's facial expression.

[0337] The generated avatar is combined with a selected travel destination image to create a composite image that makes it appear as if the user has visited that place. The composite image is delivered to the user's device, and the user can experience the image in a way that reflects their own emotional state through an emotion engine.

[0338] When a user selects an image of interest, that selection information is sent from the device to the server. The server uses an emotion engine to store and analyze the selection information, including the user's emotional state. Based on the results of this emotion analysis, a machine learning model analyzes the user's preferences more precisely and creates a travel plan accordingly.

[0339] The generated travel plan takes into account the user's current emotional state, suggesting destinations, schedules, and travel experiences that will resonate more emotionally. The server sends the completed travel plan to the user's device, allowing them to make reservations and check detailed information based on it.

[0340] For example, if a user provides a facial image while relaxing at home, the emotion engine can detect this "relaxed" state and display a calmer expression on the avatar's face, or suggest a travel plan that prioritizes relaxation. In this way, users can experience a more relatable and emotionally satisfying travel plan that reflects their emotions.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user uploads their facial image to the system using a device. The device receives the user's input and sends the facial image to the server.

[0344] Step 2:

[0345] The server applies an AI algorithm to analyze the received facial image. It extracts facial feature points and generates an avatar. During this process, the server uses an emotion engine to identify the user's emotional state and adjusts the avatar's facial expressions and posture accordingly.

[0346] Step 3:

[0347] The server combines the generated avatar with existing travel destination images. This combined image is adjusted so that the avatar's facial expressions change according to its emotional state.

[0348] Step 4:

[0349] The server selects the synthesized travel images and sends them to the user's device. The user can then view their avatar in various locations.

[0350] Step 5:

[0351] Users select images that interest them from those displayed on their device and provide selection information along with the corresponding emotions.

[0352] Step 6:

[0353] The device sends information about the images and emotions selected by the user to the server.

[0354] Step 7:

[0355] The server uses an emotion engine and machine learning models to analyze the received selection information and emotional state. Based on this, it automatically generates travel plans that are suitable for the user's preferences and emotions.

[0356] Step 8:

[0357] The server sends the generated travel plan to the user's device and presents it to the user. The user can then use this plan to create a more detailed travel plan.

[0358] (Example 2)

[0359] 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".

[0360] In modern travel planning, providing a personalized experience that accurately reflects a user's individual preferences and emotions is a challenging task. Traditional travel planning tends to offer uniform suggestions based on general information, and there is a need for individualized differentiation that exceeds user expectations.

[0361] 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.

[0362] In this invention, the server includes a device for receiving images of the user, a device for generating a virtual person based on the images, and a device for compositing the virtual person onto an arbitrary landscape image. This makes it possible to suggest travel experiences based on the user's individual emotions and preferences.

[0363] A "user" refers to a person who uses this system to create their own individual travel experience.

[0364] "Image" refers to visual information that represents a user, landscape, or other subject in digital format.

[0365] A "virtual character" refers to a digital avatar generated based on the user's characteristics.

[0366] "Landscape images" refer to visual information that shows a specific location, such as a travel destination.

[0367] "Image synthesis" refers to the process of combining multiple images to create a single, unified image.

[0368] "Emotion analysis" refers to a method of identifying a user's emotional state from their facial image or other data.

[0369] "Information storage" refers to the process of saving user choices and emotional data in a database or similar system.

[0370] "Itinerary" refers to a travel plan that includes the schedule and destinations planned for the user.

[0371] An "artificial intelligence model" refers to an algorithm that analyzes data and predicts user preferences and behavior.

[0372] This invention is a system for users to create personalized travel experiences. Specific embodiments are shown below.

[0373] The server first receives a facial image sent by the user using their device. This facial image is then processed using AI algorithms to analyze the user's individual characteristics. Python, OpenCV, TensorFlow, and other machine learning libraries are used for this processing.

[0374] The server uses these techniques to generate a virtual person, or avatar, that resembles the user. During this process, an emotion analysis engine is used to identify the user's current emotions and reflect them in the avatar's facial expressions. General-purpose emotion analysis tools and libraries are likely to be used for this process.

[0375] The server then composites the generated avatar image onto the selected landscape image. This compositing process utilizes scripts within image editing software such as Photoshop or GIMP. This creates a composite image that makes it appear as if the user is actually visiting the location.

[0376] The device receives the completed composite image and presents it to the user. The user can then enjoy a new experience that reflects their emotions while viewing the composite image on the screen.

[0377] Furthermore, users select items that interest them from the displayed composite images and send this selection information to the server via their device. The server stores the received selection information and uses a machine learning model to analyze the user's preferences based on this data. Through this process, a personalized travel plan that responds to the user's emotions is created.

[0378] One concrete example of this system is a feature where, based on a facial image provided when the user is relaxed, the emotion analysis engine detects a "relaxed" state and gives the avatar a calm expression. This makes it possible to suggest travel plans that prioritize relaxation. An example of a prompt message would be, "Generate an avatar that resembles a user in a relaxed state, and present it with images of relaxing travel destinations."

[0379] In this way, the system provides a travel experience that takes into account the user's emotions and preferences.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user takes a facial image using a device and sends it to the image processing system. The input is the user's facial image, and the output is image data received on the server side. This image is then ready for analysis in subsequent processing.

[0383] Step 2:

[0384] The server analyzes the received facial image using an AI algorithm. The input is the received facial image, and the output is facial feature information. Specifically, it analyzes facial feature points using OpenCV, processes that data using a framework such as TensorFlow, and quantifies the user's facial features. This information is used for avatar generation.

[0385] Step 3:

[0386] The server uses an AI model to generate a virtual character (avatar) that resembles the user, based on the analyzed facial feature information. The output is the generated avatar image. This process results in a digital character that reflects the user's individuality.

[0387] Step 4:

[0388] The server performs emotion analysis during avatar generation and obtains emotion data based on the user's facial image. The input is the user's facial image and its analysis information, and the output is information about the user's emotional state. This information is reflected in the avatar as facial expressions by the emotion analysis engine.

[0389] Step 5:

[0390] The server combines the generated avatar image with a selected landscape image. The input is the avatar image and the landscape image, and the output is a composite image combining the two images. An automated processing tool in image editing software is used for this compositing.

[0391] Step 6:

[0392] The device receives the synthesized image from the server and displays it to the user. The input is the synthesized image sent by the server, and the output is the image presented to the user as visual information. This allows the user to create a virtual visualization of their travel experience that reflects their emotions.

[0393] Step 7:

[0394] Users select images that interest them from a collection of composite images and send this information to the server via their device. The input is the user's image selection information, and the output is the selection data sent to the server. This selection behavior reveals the user's preferences.

[0395] Step 8:

[0396] The server uses a machine learning model to perform preference analysis based on the information selected by the user. The output is the analyzed user preference data. This allows for the accumulation of data based on the user's emotions and past choices.

[0397] Step 9:

[0398] The server uses the analyzed preference data to generate a travel itinerary tailored to the user's emotions and sends it to the terminal. The input is preference analysis data, and the output is user-specific travel itinerary information. As a result, a travel plan tailored to the user's needs is provided.

[0399] (Application Example 2)

[0400] 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 as the "terminal".

[0401] Current technology suffers from a lack of individuality and emotional reflection when users experience virtual travel. There is a need to provide avatars and travel plans that reflect each user's individual expressions and emotions to create a more personalized experience.

[0402] 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.

[0403] In this invention, the server includes means for receiving user facial data, means for generating individual virtual characters based on the facial data, means for presenting the synthesized visual data to the user, means for detecting the user's emotional state and dynamically adjusting the virtual character's facial expressions based on the corresponding emotion, and means for adjusting the travel plan according to the user's emotional state and suggesting destinations that resonate with the user's emotions. This enables a personalized virtual travel experience based on the user's emotions and preferences.

[0404] A "user" is an individual who uses the system to have a virtual travel experience.

[0405] "Appearance data" refers to image data that includes the user's facial features.

[0406] A "virtual character" is a digital character generated based on the user's facial data.

[0407] "Visiting destination visual data" refers to image or video data of the location that is the subject of the virtual trip.

[0408] "Synthesis" is the process of creating a single image by combining a virtual person with visual data of a visited location.

[0409] "Emotional state" refers to the user's current mental state, and is information judged from their facial expressions and behavior.

[0410] A "travel plan" is a schedule of destinations and activities suggested based on the user's emotions and preferences.

[0411] The server first receives facial data transmitted from the user's terminal. The received data can be processed using OpenCV, an image processing software, to analyze facial feature points. This analysis generates a virtual character based on the user's facial data. For generating the virtual character, 3D modeling tools such as Blender or Unity are used to create a character with dynamic, emotionally expressive facial expressions.

[0412] Next, the server uses an emotion engine to identify the user's emotional state from their facial data. In this process, an emotion recognition model, pre-trained using TensorFlow, infers the emotion from the user's facial expressions.

[0413] The generated virtual character is combined with visual data of the destination selected by the user. This combination is performed in real time and displayed on the user's device. Real-time rendering functions provided by Unity or Blender can be used to combine the virtual character with the background.

[0414] Based on the items the user has shown interest in, the server that provides travel plans uses machine learning models to analyze the user's selection patterns. In this process, the user's past selection history and emotional state are used as input to suggest more personalized destinations and schedules.

[0415] For example, if a user accesses this system while relaxing at home on a holiday, the server will detect a "relaxed" mood. A travel plan suited to relaxation will be suggested, including destinations surrounded by nature, and the virtual character's facial expression will be synthesized to reflect a calm state.

[0416] An example of a prompt message is: "Provide a user's facial image, analyze their emotions in real time to generate a personalized avatar, and then composite that avatar onto the background of the selected travel destination and display it."

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The server receives facial data sent from the user's terminal. The input is the user's facial image data, which is held for the next processing step.

[0420] Step 2:

[0421] The server analyzes the received facial data using OpenCV to identify facial feature points. The input is the facial image received in step 1, and the output is the identified feature point data. Based on this feature point data, the server performs skeletal mapping of a virtual person and prepares to generate individual virtual characters.

[0422] Step 3:

[0423] The server uses TensorFlow to analyze the facial expression portion of the facial data and infer the user's emotional state. The input is the facial feature point data obtained in step 2, and the output is the identified emotional state data. This emotional data is used to dynamically adjust the facial expressions of the virtual character.

[0424] Step 4:

[0425] The server generates virtual characters using Blender or Unity and combines them with the visual data of the visited location. The input is the facial feature point data obtained in step 2 and the emotion data obtained in step 3, and the output is the combined visual data. In addition to generating virtual characters, this combination process also performs real-time rendering and processes the data into a format that can be immediately displayed to the user.

[0426] Step 5:

[0427] The server sends the synthesized visual data to the user's device, allowing the user to view it in real time. The input is the synthesized visual data from step 4, and the output is the travel experience image displayed on the user's device.

[0428] Step 6:

[0429] The user selects items of interest from the suggested visual data, and this selection information is sent back to the server. The input is the user's selection data, and the next steps are executed based on this selection.

[0430] Step 7:

[0431] The server generates a travel plan using a machine learning model based on the selected data and past selection history. The input is the selected data obtained from step 6, and the output is updated or newly generated travel plan data. This travel plan is adjusted to take into account the user's emotional state and preferences.

[0432] Step 8:

[0433] The server presents the generated travel plan to the user, allowing the user to consider their next course of action. The input is the travel plan data from step 7, and the output is the detailed travel plan displayed on the user's terminal.

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

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

[0439] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] This invention is a system that generates individual avatars based on facial images provided by users, and then combines these avatars with images of landscapes and tourist attractions of travel destinations, thereby providing users with a visual experience as if they had actually visited those places. By introducing this system, users can have the opportunity to discover new travel destinations and receive detailed travel plans for places that interest them.

[0451] The server first receives a facial image sent from the user's device. Next, it uses AI technology to analyze the user's facial features based on this image and generates an avatar. The server then uses this avatar data to superimpose it onto a background of a location selected from an existing database of travel destination images.

[0452] This composite image is delivered to the user's device. The user reviews each image, in which their avatar is superimposed onto different travel destinations, and marks the images they are interested in. The device then sends the user's selection information to the server.

[0453] The server analyzes user preferences based on collected user selection information. Machine learning models are used for this analysis, comparing the user's choices with data from similar past users. Based on this analysis, the server generates a travel plan tailored to the user. This plan includes specific destination information, duration of stay, and mode of transportation. The created travel plan is finally presented to the user via their device.

[0454] This system allows users to plan their trips after seeing an actual image of their destination, resulting in a more satisfying travel experience. This technology will also be used as a foundation for providing personalized services based on users' preferences and tastes.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The user uploads their facial image to the system using a device. The device receives input from the user and sends the facial image to the server.

[0458] Step 2:

[0459] The server analyzes the received facial image and uses an AI algorithm to model the user's features. This generates an avatar. This generation process includes techniques to extract points of facial features and convert them into a digital representation.

[0460] Step 3:

[0461] The server uses the generated avatar to composite it onto a background selected from a database of travel destination images. This background selection includes tourist hotspots and landmarks. This compositing process creates images that make it appear as if the user's avatar is visiting various locations.

[0462] Step 4:

[0463] The server sends synthesized travel images to the user's device. The images delivered are different each day, designed to pique the user's interest in new locations.

[0464] Step 5:

[0465] Users review the images they receive on their devices and check the travel destination images that interest them. Users then select places they would like to actually visit.

[0466] Step 6:

[0467] The terminal sends the user's selection information back to the server. The server stores the received selection information in its database.

[0468] Step 7:

[0469] The server uses machine learning models within its engine to analyze the user's interests and preferences. This generates a travel plan optimized for the user, which includes detailed information about the regions deemed of interest and a travel schedule.

[0470] Step 8:

[0471] The server sends the generated travel plan to the terminal and presents it to the user. The user can review the provided plan and make reservations or inquiries as needed.

[0472] (Example 1)

[0473] 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."

[0474] Modern travel planning is diverse, making it difficult to reflect individual user preferences. Furthermore, users often lack a concrete image of their destination before actually visiting, potentially leading to lower post-trip satisfaction. Additionally, efficiently processing large amounts of information during the information gathering and user preference analysis processes for travel planning is challenging.

[0475] 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.

[0476] In this invention, the server includes means for receiving a user's facial image and generating an avatar using AI technology, means for compositing the avatar with a background image using image processing software, and means for analyzing selection information using a machine learning model and generating a travel plan using a generation AI model. This makes it possible to provide personalized travel plans that reflect the user's preferences, allowing the user to have a concrete image of their travel destination before traveling and enabling a highly satisfying travel experience.

[0477] "Means for receiving user facial images" refers to communication technologies or protocols for acquiring facial image data sent from a user's terminal.

[0478] "A method for generating avatars using AI technology" refers to an artificial intelligence algorithm that analyzes a user's facial image and creates a digital character that reflects its features.

[0479] "Methods of compositing using image processing software" refers to programs that seamlessly integrate a generated avatar with a selected background image to create a visually consistent composite image.

[0480] "Means of sending to the user's terminal" refers to network technology used to transfer data from a server to a user's terminal.

[0481] "Methods for analyzing selection information using machine learning models" refer to machine learning algorithms that analyze user selection data, detect similar patterns, and identify user preferences.

[0482] "A method for generating travel plans using a generative AI model" refers to artificial intelligence technology that automatically creates the optimal travel plan based on the results of user preference analysis.

[0483] A "background image" is image data that shows the scenery or tourist attractions of a travel destination, and is a visual element that forms the basis of a composite image.

[0484] "User selection information" refers to data on the preferences a user has shown towards composite images that they are interested in, and serves as basic information for preference analysis.

[0485] A "travel plan" is a document or data set of plans regarding destinations, length of stay, and means of transportation, suggested based on the user's preferences.

[0486] This invention implements a system that generates individual avatars based on facial images provided by the user, and then combines these avatars with background images of various travel destinations to propose personalized travel plans to the user.

[0487] The server first receives a facial image sent from the user's device. After receiving the image, the image recognition process primarily uses software such as a "facial recognition library," which analyzes the facial features using AI technology. This analysis generates an avatar that reflects the user's features. A "generative AI model," such as "StyleGAN," may be used for generation.

[0488] Next, the server uses "image processing software" to composite the generated avatar with the background image of the selected travel destination. In this process, a script using common image editing tools such as "Adobe Photoshop" adjusts the size, brightness, and color tone of the avatar to match the background, creating a harmonious composite image.

[0489] Once the composite image is complete, the server sends the image to the user's device. The user can then view the avatar superimposed onto various travel destination background images and select the images that interest them.

[0490] The server receives user selection information and analyzes user preferences using a machine learning model. The analysis utilizes tools such as "Scikit-learn" and "TensorFlow," providing data to generate the most suitable travel plan for the user. The travel plan, generated using an AI model, includes destinations, length of stay, and transportation methods tailored to the user's preferences.

[0491] Finally, the generated travel plan is presented to the user's device. The user can review it and make concrete travel plans. Therefore, this invention makes it possible to provide users with a highly satisfying travel experience.

[0492] An example of a prompt message is, "Generate a travel plan to Hiroshima for a user who has shown interest in an image with Shukkei-en Garden in the background." In this way, by generating an appropriate travel plan based on the user's selection, travel suggestions tailored to individual preferences can be achieved.

[0493] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0494] Step 1:

[0495] The user uploads a facial image to the device. This input facial image is digital data that reflects the user's identity. The device initiates communication to send this image data to the server.

[0496] Step 2:

[0497] The server acquires the facial image received from the terminal. The received image data is used as input for analyzing facial features using a facial recognition library. Specifically, landmark detection of the face is performed, each feature point is extracted as numerical data, and this is supplied to the feature analysis process of the AI ​​model.

[0498] Step 3:

[0499] The server uses AI technology to generate an avatar based on facial feature data. During this process, the generated digital character is algorithmically adjusted to resemble the user's image in shape and color. The output is the user's avatar image.

[0500] Step 4:

[0501] The server uses image processing software to composite the generated avatar image with a background image. The input consists of an avatar image and several pre-prepared background images of travel destinations. During image compositing, adjustments such as the avatar's size and position are performed by an automated script, and the output is a composite image.

[0502] Step 5:

[0503] The server sends the generated composite images to the user's device. The user views the received composite images through their device and examines each one. They then select the composite images they are interested in and save the selection information on their device.

[0504] Step 6:

[0505] The terminal sends user selection information to the server. The server receives this selection information as input data for analysis and starts the analysis using a machine learning model. Specifically, data calculations are performed to detect preference patterns based on the user's preference data, and analysis results regarding preferences are obtained as output.

[0506] Step 7:

[0507] The server utilizes a generative AI model and, using the results of a preference analysis as input, generates an optimal travel plan for the user. In this generation process, prompt examples are used to suggest travel destinations, duration, and modes of transportation that match the user's preferences, and the output is data for the travel plan.

[0508] Step 8:

[0509] The server sends the generated travel plan to the user's device. The user can then review the travel plan on their device and use it to plan their actual trip. This allows users to enjoy a personalized travel experience tailored to their individual preferences.

[0510] (Application Example 1)

[0511] 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."

[0512] Modern travel planning is time-consuming and laborious, and it's difficult to provide information tailored to individual user preferences. Furthermore, there's a lack of efficient systems for users to easily discover new destinations and visually experience them. This makes it difficult to provide personalized travel experiences, potentially leading to lower user satisfaction.

[0513] 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.

[0514] In this invention, the server includes means for receiving a user's facial image, means for generating a visual artificial surrogate based on the facial image, and means for synthesizing the visual artificial surrogate with existing destination visual information. This allows the user to have the experience of virtually visiting a new travel destination and to intuitively discover new travel destinations. It also facilitates the suggestion of visit plans based on the user's selection trends, making it possible to provide a highly personalized travel experience.

[0515] "Means for receiving user facial images" refers to methods for acquiring digital image data of a user's face provided via a terminal or the internet.

[0516] "Means for generating visual artificial surrogates" refer to the technologies and processes necessary to analyze a user's facial features and generate a digital avatar.

[0517] "Means of synthesizing with destination visual information" refers to a technical process that combines a generated artificial surrogate with images or videos of the destination, providing the user with a visual experience that makes them feel as if they are actually there.

[0518] "Means of collecting visual information data in response to user expressions" refers to methods of collecting information that users have shown interest in or selected, in digital format.

[0519] "Means for generating visit plans" refers to the process of creating a customized travel schedule based on the user's choices and preferences.

[0520] "Means for user terminals operating on portable information processing devices" refers to methods for running software or applications on portable devices such as smartphones and tablets.

[0521] To realize this invention, a server, a user terminal, and related software are utilized. The server receives facial images from the user's portable information processing device. Based on the received facial images, facial features are analyzed using AI technologies such as TensorFlow to generate a visual avatar. This avatar generation is important for providing a personalized visual experience.

[0522] The server synthesizes the generated artificial avatar with the user's selected destination visual information. Image editing software such as Photoshop or GIMP may be used for this synthesis process. As a result, the user can have a visual experience as if their avatar were actually at the travel destination.

[0523] On the user's terminal, the user can view the presented visual information and select images that interest them. The selection information is then sent back to the server, which uses this data to analyze the user's preferences using a machine learning model.

[0524] The server then generates a visit plan based on the user's preferences. This plan includes customized information such as the length of stay and mode of transportation. The generated plan is presented through an application on the user's portable information device. This process is often implemented using software frameworks such as Python or Flask.

[0525] As a concrete example, if a user selects a beach in Okinawa, a composite image of an avatar standing on that beach will be generated. If the user likes the plan after seeing this image and wants to visit the location, a specific travel plan will be presented. The travel plan will include "recommended spots and activities for sightseeing in Okinawa over 3 nights and 4 days."

[0526] An example of a prompt message is: "Analyze the user's facial features and generate a personalized avatar. Then, create a program that combines this avatar with a beach scene of Okinawa chosen by the user to provide a visual experience."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The server receives facial images from the user's terminal. The input is digital facial image data, and once received, it converts and saves it in an appropriate format for use in the next step.

[0530] Step 2:

[0531] The server generates a visual surrogate based on the received facial image. This involves a process that uses TensorFlow to analyze the user's facial features and generate a digital avatar. The input is processed facial image data, and the output is avatar data with the user's features.

[0532] Step 3:

[0533] The server synthesizes the generated artificial avatar with existing destination visual information. This process uses image processing software such as Photoshop or GIMP to integrate the avatar and background image. The input is avatar data and a selected landscape image, and the output is the synthesized visual information.

[0534] Step 4:

[0535] The user terminal displays synthesized visual information to the user. The user reviews this visual information and makes selections based on the information that interests them. The input is the synthesized image, and the output is the user's selection information.

[0536] Step 5:

[0537] The server receives user selection information and analyzes preferences using a machine learning model. The input is user selection data, and by analyzing this data, it obtains preference information necessary to generate a travel plan suitable for the user.

[0538] Step 6:

[0539] The server generates personalized travel plans based on analyzed preferences. These plans include destinations and modes of transportation, and are created using Python or the Flask framework. The input is preference information, and the output is a detailed travel plan.

[0540] Step 7:

[0541] The server sends the generated travel plan to the user's terminal and presents it to the user. The user can review the displayed plan and use it to help make a travel decision. The input is travel plan data, and the output is the final travel plan information presented.

[0542] 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.

[0543] This invention is a system that generates individual avatars based on facial images provided by the user, combines them with images of the travel destination, and recognizes the user's emotions to provide a more personalized travel experience. The introduction of an emotion engine makes it possible to create avatar expressions and suggest travel plans that reflect the user's emotional state.

[0544] The server first receives a facial image sent from the user's device. The received image is analyzed using an AI algorithm to determine its facial feature points and generate an avatar that replicates the user's characteristics. At this time, an emotion engine is used to identify the user's emotions during the image analysis and dynamically adjust the avatar's facial expression.

[0545] The generated avatar is combined with a selected travel destination image to create a composite image that makes it appear as if the user has visited that place. The composite image is delivered to the user's device, and the user can experience the image in a way that reflects their own emotional state through an emotion engine.

[0546] When a user selects an image of interest, that selection information is sent from the device to the server. The server uses an emotion engine to store and analyze the selection information, including the user's emotional state. Based on the results of this emotion analysis, a machine learning model analyzes the user's preferences more precisely and creates a travel plan accordingly.

[0547] The generated travel plan takes into account the user's current emotional state, suggesting destinations, schedules, and travel experiences that will resonate more emotionally. The server sends the completed travel plan to the user's device, allowing them to make reservations and check detailed information based on it.

[0548] For example, if a user provides a facial image while relaxing at home, the emotion engine can detect this "relaxed" state and display a calmer expression on the avatar's face, or suggest a travel plan that prioritizes relaxation. In this way, users can experience a more relatable and emotionally satisfying travel plan that reflects their emotions.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The user uploads their facial image to the system using a device. The device receives the user's input and sends the facial image to the server.

[0552] Step 2:

[0553] The server applies an AI algorithm to analyze the received facial image. It extracts facial feature points and generates an avatar. During this process, the server uses an emotion engine to identify the user's emotional state and adjusts the avatar's facial expressions and posture accordingly.

[0554] Step 3:

[0555] The server combines the generated avatar with existing travel destination images. This combined image is adjusted so that the avatar's facial expressions change according to its emotional state.

[0556] Step 4:

[0557] The server selects the synthesized travel images and sends them to the user's device. The user can then view their avatar in various locations.

[0558] Step 5:

[0559] Users select images that interest them from those displayed on their device and provide selection information along with the corresponding emotions.

[0560] Step 6:

[0561] The device sends information about the images and emotions selected by the user to the server.

[0562] Step 7:

[0563] The server uses an emotion engine and machine learning models to analyze the received selection information and emotional state. Based on this, it automatically generates travel plans that are suitable for the user's preferences and emotions.

[0564] Step 8:

[0565] The server sends the generated travel plan to the user's device and presents it to the user. The user can then use this plan to create a more detailed travel plan.

[0566] (Example 2)

[0567] 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."

[0568] In modern travel planning, providing a personalized experience that accurately reflects a user's individual preferences and emotions is a challenging task. Traditional travel planning tends to offer uniform suggestions based on general information, and there is a need for individualized differentiation that exceeds user expectations.

[0569] 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.

[0570] In this invention, the server includes a device for receiving images of the user, a device for generating a virtual person based on the images, and a device for compositing the virtual person onto an arbitrary landscape image. This makes it possible to suggest travel experiences based on the user's individual emotions and preferences.

[0571] A "user" refers to a person who uses this system to create their own individual travel experience.

[0572] "Image" refers to visual information that represents a user, landscape, or other subject in digital format.

[0573] A "virtual character" refers to a digital avatar generated based on the user's characteristics.

[0574] "Landscape images" refer to visual information that shows a specific location, such as a travel destination.

[0575] "Image synthesis" refers to the process of combining multiple images to create a single, unified image.

[0576] "Emotion analysis" refers to a method of identifying a user's emotional state from their facial image or other data.

[0577] "Information storage" refers to the process of saving user choices and emotional data in a database or similar system.

[0578] "Itinerary" refers to a travel plan that includes the schedule and destinations planned for the user.

[0579] An "artificial intelligence model" refers to an algorithm that analyzes data and predicts user preferences and behavior.

[0580] This invention is a system for users to create personalized travel experiences. Specific embodiments are shown below.

[0581] The server first receives a facial image sent by the user using their device. This facial image is then processed using AI algorithms to analyze the user's individual characteristics. Python, OpenCV, TensorFlow, and other machine learning libraries are used for this processing.

[0582] The server uses these techniques to generate a virtual person, or avatar, that resembles the user. During this process, an emotion analysis engine is used to identify the user's current emotions and reflect them in the avatar's facial expressions. General-purpose emotion analysis tools and libraries are likely to be used for this process.

[0583] The server then composites the generated avatar image onto the selected landscape image. This compositing process utilizes scripts within image editing software such as Photoshop or GIMP. This creates a composite image that makes it appear as if the user is actually visiting the location.

[0584] The device receives the completed composite image and presents it to the user. The user can then enjoy a new experience that reflects their emotions while viewing the composite image on the screen.

[0585] Furthermore, users select items that interest them from the displayed composite images and send this selection information to the server via their device. The server stores the received selection information and uses a machine learning model to analyze the user's preferences based on this data. Through this process, a personalized travel plan that responds to the user's emotions is created.

[0586] One concrete example of this system is a feature where, based on a facial image provided when the user is relaxed, the emotion analysis engine detects a "relaxed" state and gives the avatar a calm expression. This makes it possible to suggest travel plans that prioritize relaxation. An example of a prompt message would be, "Generate an avatar that resembles a user in a relaxed state, and present it with images of relaxing travel destinations."

[0587] In this way, the system provides a travel experience that takes into account the user's emotions and preferences.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The user takes a facial image using a device and sends it to the image processing system. The input is the user's facial image, and the output is image data received on the server side. This image is then ready for analysis in subsequent processing.

[0591] Step 2:

[0592] The server analyzes the received facial image using an AI algorithm. The input is the received facial image, and the output is facial feature information. Specifically, it analyzes facial feature points using OpenCV, processes that data using a framework such as TensorFlow, and quantifies the user's facial features. This information is used for avatar generation.

[0593] Step 3:

[0594] The server uses an AI model to generate a virtual character (avatar) that resembles the user, based on the analyzed facial feature information. The output is the generated avatar image. This process results in a digital character that reflects the user's individuality.

[0595] Step 4:

[0596] The server performs emotion analysis during avatar generation and obtains emotion data based on the user's facial image. The input is the user's facial image and its analysis information, and the output is information about the user's emotional state. This information is reflected in the avatar as facial expressions by the emotion analysis engine.

[0597] Step 5:

[0598] The server combines the generated avatar image with a selected landscape image. The input is the avatar image and the landscape image, and the output is a composite image combining the two images. An automated processing tool in image editing software is used for this compositing.

[0599] Step 6:

[0600] The device receives the synthesized image from the server and displays it to the user. The input is the synthesized image sent by the server, and the output is the image presented to the user as visual information. This allows the user to create a virtual visualization of their travel experience that reflects their emotions.

[0601] Step 7:

[0602] Users select images that interest them from a collection of composite images and send this information to the server via their device. The input is the user's image selection information, and the output is the selection data sent to the server. This selection behavior reveals the user's preferences.

[0603] Step 8:

[0604] The server uses a machine learning model to perform preference analysis based on the information selected by the user. The output is the analyzed user preference data. This allows for the accumulation of data based on the user's emotions and past choices.

[0605] Step 9:

[0606] The server uses the analyzed preference data to generate a travel itinerary tailored to the user's emotions and sends it to the terminal. The input is preference analysis data, and the output is user-specific travel itinerary information. As a result, a travel plan tailored to the user's needs is provided.

[0607] (Application Example 2)

[0608] 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."

[0609] Current technology suffers from a lack of individuality and emotional reflection when users experience virtual travel. There is a need to provide avatars and travel plans that reflect each user's individual expressions and emotions to create a more personalized experience.

[0610] 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.

[0611] In this invention, the server includes means for receiving user facial data, means for generating individual virtual characters based on the facial data, means for presenting the synthesized visual data to the user, means for detecting the user's emotional state and dynamically adjusting the virtual character's facial expressions based on the corresponding emotion, and means for adjusting the travel plan according to the user's emotional state and suggesting destinations that resonate with the user's emotions. This enables a personalized virtual travel experience based on the user's emotions and preferences.

[0612] A "user" is an individual who uses the system to have a virtual travel experience.

[0613] "Appearance data" refers to image data that includes the user's facial features.

[0614] A "virtual character" is a digital character generated based on the user's facial data.

[0615] "Visiting destination visual data" refers to image or video data of the location that is the subject of the virtual trip.

[0616] "Synthesis" is the process of creating a single image by combining a virtual person with visual data of a visited location.

[0617] "Emotional state" refers to the user's current mental state, and is information judged from their facial expressions and behavior.

[0618] A "travel plan" is a schedule of destinations and activities suggested based on the user's emotions and preferences.

[0619] The server first receives facial data transmitted from the user's terminal. The received data can be processed using OpenCV, an image processing software, to analyze facial feature points. This analysis generates a virtual character based on the user's facial data. For generating the virtual character, 3D modeling tools such as Blender or Unity are used to create a character with dynamic, emotionally expressive facial expressions.

[0620] Next, the server uses an emotion engine to identify the user's emotional state from their facial data. In this process, an emotion recognition model, pre-trained using TensorFlow, infers the emotion from the user's facial expressions.

[0621] The generated virtual character is combined with visual data of the destination selected by the user. This combination is performed in real time and displayed on the user's device. Real-time rendering functions provided by Unity or Blender can be used to combine the virtual character with the background.

[0622] Based on the items the user has shown interest in, the server that provides travel plans uses machine learning models to analyze the user's selection patterns. In this process, the user's past selection history and emotional state are used as input to suggest more personalized destinations and schedules.

[0623] For example, if a user accesses this system while relaxing at home on a holiday, the server will detect a "relaxed" mood. A travel plan suited to relaxation will be suggested, including destinations surrounded by nature, and the virtual character's facial expression will be synthesized to reflect a calm state.

[0624] An example of a prompt message is: "Provide a user's facial image, analyze their emotions in real time to generate a personalized avatar, and then composite that avatar onto the background of the selected travel destination and display it."

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The server receives facial data sent from the user's terminal. The input is the user's facial image data, which is held for the next processing step.

[0628] Step 2:

[0629] The server analyzes the received facial data using OpenCV to identify facial feature points. The input is the facial image received in step 1, and the output is the identified feature point data. Based on this feature point data, the server performs skeletal mapping of a virtual person and prepares to generate individual virtual characters.

[0630] Step 3:

[0631] The server uses TensorFlow to analyze the facial expression portion of the facial data and infer the user's emotional state. The input is the facial feature point data obtained in step 2, and the output is the identified emotional state data. This emotional data is used to dynamically adjust the facial expressions of the virtual character.

[0632] Step 4:

[0633] The server generates virtual characters using Blender or Unity and combines them with the visual data of the visited location. The input is the facial feature point data obtained in step 2 and the emotion data obtained in step 3, and the output is the combined visual data. In addition to generating virtual characters, this combination process also performs real-time rendering and processes the data into a format that can be immediately displayed to the user.

[0634] Step 5:

[0635] The server sends the synthesized visual data to the user's device, allowing the user to view it in real time. The input is the synthesized visual data from step 4, and the output is the travel experience image displayed on the user's device.

[0636] Step 6:

[0637] The user selects items of interest from the suggested visual data, and this selection information is sent back to the server. The input is the user's selection data, and the next steps are executed based on this selection.

[0638] Step 7:

[0639] The server generates a travel plan using a machine learning model based on the selected data and past selection history. The input is the selected data obtained from step 6, and the output is updated or newly generated travel plan data. This travel plan is adjusted to take into account the user's emotional state and preferences.

[0640] Step 8:

[0641] The server presents the generated travel plan to the user, allowing the user to consider their next course of action. The input is the travel plan data from step 7, and the output is the detailed travel plan displayed on the user's terminal.

[0642] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0643] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0644] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0645] [Fourth Embodiment]

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

[0647] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0648] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0649] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0650] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0651] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0652] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0653] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0654] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0655] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0656] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0657] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0658] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] This invention is a system that generates individual avatars based on facial images provided by users, and then combines these avatars with images of landscapes and tourist attractions of travel destinations, thereby providing users with a visual experience as if they had actually visited those places. By introducing this system, users can have the opportunity to discover new travel destinations and receive detailed travel plans for places that interest them.

[0660] The server first receives a facial image sent from the user's device. Next, it uses AI technology to analyze the user's facial features based on this image and generates an avatar. The server then uses this avatar data to superimpose it onto a background of a location selected from an existing database of travel destination images.

[0661] This composite image is delivered to the user's device. The user reviews each image, in which their avatar is superimposed onto different travel destinations, and marks the images they are interested in. The device then sends the user's selection information to the server.

[0662] The server analyzes user preferences based on collected user selection information. Machine learning models are used for this analysis, comparing the user's choices with data from similar past users. Based on this analysis, the server generates a travel plan tailored to the user. This plan includes specific destination information, duration of stay, and mode of transportation. The created travel plan is finally presented to the user via their device.

[0663] This system allows users to plan their trips after seeing an actual image of their destination, resulting in a more satisfying travel experience. This technology will also be used as a foundation for providing personalized services based on users' preferences and tastes.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The user uploads their facial image to the system using a device. The device receives input from the user and sends the facial image to the server.

[0667] Step 2:

[0668] The server analyzes the received facial image and uses an AI algorithm to model the user's features. This generates an avatar. This generation process includes techniques to extract points of facial features and convert them into a digital representation.

[0669] Step 3:

[0670] The server uses the generated avatar to composite it onto a background selected from a database of travel destination images. This background selection includes tourist hotspots and landmarks. This compositing process creates images that make it appear as if the user's avatar is visiting various locations.

[0671] Step 4:

[0672] The server sends synthesized travel images to the user's device. The images delivered are different each day, designed to pique the user's interest in new locations.

[0673] Step 5:

[0674] Users review the images they receive on their devices and check the travel destination images that interest them. Users then select places they would like to actually visit.

[0675] Step 6:

[0676] The terminal sends the user's selection information back to the server. The server stores the received selection information in its database.

[0677] Step 7:

[0678] The server uses machine learning models within its engine to analyze the user's interests and preferences. This generates a travel plan optimized for the user, which includes detailed information about the regions deemed of interest and a travel schedule.

[0679] Step 8:

[0680] The server sends the generated travel plan to the terminal and presents it to the user. The user can review the provided plan and make reservations or inquiries as needed.

[0681] (Example 1)

[0682] 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".

[0683] Modern travel planning is diverse, making it difficult to reflect individual user preferences. Furthermore, users often lack a concrete image of their destination before actually visiting, potentially leading to lower post-trip satisfaction. Additionally, efficiently processing large amounts of information during the information gathering and user preference analysis processes for travel planning is challenging.

[0684] 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.

[0685] In this invention, the server includes means for receiving a user's facial image and generating an avatar using AI technology, means for compositing the avatar with a background image using image processing software, and means for analyzing selection information using a machine learning model and generating a travel plan using a generation AI model. This makes it possible to provide personalized travel plans that reflect the user's preferences, allowing the user to have a concrete image of their travel destination before traveling and enabling a highly satisfying travel experience.

[0686] "Means for receiving user facial images" refers to communication technologies or protocols for acquiring facial image data sent from a user's terminal.

[0687] "A method for generating avatars using AI technology" refers to an artificial intelligence algorithm that analyzes a user's facial image and creates a digital character that reflects its features.

[0688] "Methods of compositing using image processing software" refers to programs that seamlessly integrate a generated avatar with a selected background image to create a visually consistent composite image.

[0689] "Means of sending to the user's terminal" refers to network technology used to transfer data from a server to a user's terminal.

[0690] "Methods for analyzing selection information using machine learning models" refer to machine learning algorithms that analyze user selection data, detect similar patterns, and identify user preferences.

[0691] "A method for generating travel plans using a generative AI model" refers to artificial intelligence technology that automatically creates the optimal travel plan based on the results of user preference analysis.

[0692] A "background image" is image data that shows the scenery or tourist attractions of a travel destination, and is a visual element that forms the basis of a composite image.

[0693] "User selection information" refers to data on the preferences a user has shown towards composite images that they are interested in, and serves as basic information for preference analysis.

[0694] A "travel plan" is a document or data set of plans regarding destinations, length of stay, and means of transportation, suggested based on the user's preferences.

[0695] This invention implements a system that generates individual avatars based on facial images provided by the user, and then combines these avatars with background images of various travel destinations to propose personalized travel plans to the user.

[0696] The server first receives a facial image sent from the user's device. After receiving the image, the image recognition process primarily uses software such as a "facial recognition library," which analyzes the facial features using AI technology. This analysis generates an avatar that reflects the user's features. A "generative AI model," such as "StyleGAN," may be used for generation.

[0697] Next, the server uses "image processing software" to composite the generated avatar with the background image of the selected travel destination. In this process, a script using common image editing tools such as "Adobe Photoshop" adjusts the size, brightness, and color tone of the avatar to match the background, creating a harmonious composite image.

[0698] Once the composite image is complete, the server sends the image to the user's device. The user can then view the avatar superimposed onto various travel destination background images and select the images that interest them.

[0699] The server receives user selection information and analyzes user preferences using a machine learning model. The analysis utilizes tools such as "Scikit-learn" and "TensorFlow," providing data to generate the most suitable travel plan for the user. The travel plan, generated using an AI model, includes destinations, length of stay, and transportation methods tailored to the user's preferences.

[0700] Finally, the generated travel plan is presented to the user's device. The user can review it and make concrete travel plans. Therefore, this invention makes it possible to provide users with a highly satisfying travel experience.

[0701] An example of a prompt message is, "Generate a travel plan to Hiroshima for a user who has shown interest in an image with Shukkei-en Garden in the background." In this way, by generating an appropriate travel plan based on the user's selection, travel suggestions tailored to individual preferences can be achieved.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] The user uploads a facial image to the device. This input facial image is digital data that reflects the user's identity. The device initiates communication to send this image data to the server.

[0705] Step 2:

[0706] The server acquires the facial image received from the terminal. The received image data is used as input for analyzing facial features using a facial recognition library. Specifically, landmark detection of the face is performed, each feature point is extracted as numerical data, and this is supplied to the feature analysis process of the AI ​​model.

[0707] Step 3:

[0708] The server uses AI technology to generate an avatar based on facial feature data. During this process, the generated digital character is algorithmically adjusted to resemble the user's image in shape and color. The output is the user's avatar image.

[0709] Step 4:

[0710] The server uses image processing software to composite the generated avatar image with a background image. The input consists of an avatar image and several pre-prepared background images of travel destinations. During image compositing, adjustments such as the avatar's size and position are performed by an automated script, and the output is a composite image.

[0711] Step 5:

[0712] The server sends the generated composite images to the user's device. The user views the received composite images through their device and examines each one. They then select the composite images they are interested in and save the selection information on their device.

[0713] Step 6:

[0714] The terminal sends user selection information to the server. The server receives this selection information as input data for analysis and starts the analysis using a machine learning model. Specifically, data calculations are performed to detect preference patterns based on the user's preference data, and analysis results regarding preferences are obtained as output.

[0715] Step 7:

[0716] The server utilizes a generative AI model and, using the results of a preference analysis as input, generates an optimal travel plan for the user. In this generation process, prompt examples are used to suggest travel destinations, duration, and modes of transportation that match the user's preferences, and the output is data for the travel plan.

[0717] Step 8:

[0718] The server sends the generated travel plan to the user's device. The user can then review the travel plan on their device and use it to plan their actual trip. This allows users to enjoy a personalized travel experience tailored to their individual preferences.

[0719] (Application Example 1)

[0720] 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".

[0721] Modern travel planning is time-consuming and laborious, and it's difficult to provide information tailored to individual user preferences. Furthermore, there's a lack of efficient systems for users to easily discover new destinations and visually experience them. This makes it difficult to provide personalized travel experiences, potentially leading to lower user satisfaction.

[0722] 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.

[0723] In this invention, the server includes means for receiving a user's facial image, means for generating a visual artificial surrogate based on the facial image, and means for synthesizing the visual artificial surrogate with existing destination visual information. This allows the user to have the experience of virtually visiting a new travel destination and to intuitively discover new travel destinations. It also facilitates the suggestion of visit plans based on the user's selection trends, making it possible to provide a highly personalized travel experience.

[0724] "Means for receiving user facial images" refers to methods for acquiring digital image data of a user's face provided via a terminal or the internet.

[0725] "Means for generating visual artificial surrogates" refer to the technologies and processes necessary to analyze a user's facial features and generate a digital avatar.

[0726] "Means of synthesizing with destination visual information" refers to a technical process that combines a generated artificial surrogate with images or videos of the destination, providing the user with a visual experience that makes them feel as if they are actually there.

[0727] "Means of collecting visual information data in response to user expressions" refers to methods of collecting information that users have shown interest in or selected, in digital format.

[0728] "Means for generating visit plans" refers to the process of creating a customized travel schedule based on the user's choices and preferences.

[0729] "Means for user terminals operating on portable information processing devices" refers to methods for running software or applications on portable devices such as smartphones and tablets.

[0730] To realize this invention, a server, a user terminal, and related software are utilized. The server receives facial images from the user's portable information processing device. Based on the received facial images, facial features are analyzed using AI technologies such as TensorFlow to generate a visual avatar. This avatar generation is important for providing a personalized visual experience.

[0731] The server synthesizes the generated artificial avatar with the user's selected destination visual information. Image editing software such as Photoshop or GIMP may be used for this synthesis process. As a result, the user can have a visual experience as if their avatar were actually at the travel destination.

[0732] On the user's terminal, the user can view the presented visual information and select images that interest them. The selection information is then sent back to the server, which uses this data to analyze the user's preferences using a machine learning model.

[0733] The server then generates a visit plan based on the user's preferences. This plan includes customized information such as the length of stay and mode of transportation. The generated plan is presented through an application on the user's portable information device. This process is often implemented using software frameworks such as Python or Flask.

[0734] As a concrete example, if a user selects a beach in Okinawa, a composite image of an avatar standing on that beach will be generated. If the user likes the plan after seeing this image and wants to visit the location, a specific travel plan will be presented. The travel plan will include "recommended spots and activities for sightseeing in Okinawa over 3 nights and 4 days."

[0735] An example of a prompt message is: "Analyze the user's facial features and generate a personalized avatar. Then, create a program that combines this avatar with a beach scene of Okinawa chosen by the user to provide a visual experience."

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] The server receives facial images from the user's terminal. The input is digital facial image data, and once received, it converts and saves it in an appropriate format for use in the next step.

[0739] Step 2:

[0740] The server generates a visual surrogate based on the received facial image. This involves a process that uses TensorFlow to analyze the user's facial features and generate a digital avatar. The input is processed facial image data, and the output is avatar data with the user's features.

[0741] Step 3:

[0742] The server synthesizes the generated artificial avatar with existing destination visual information. This process uses image processing software such as Photoshop or GIMP to integrate the avatar and background image. The input is avatar data and a selected landscape image, and the output is the synthesized visual information.

[0743] Step 4:

[0744] The user terminal displays synthesized visual information to the user. The user reviews this visual information and makes selections based on the information that interests them. The input is the synthesized image, and the output is the user's selection information.

[0745] Step 5:

[0746] The server receives user selection information and analyzes preferences using a machine learning model. The input is user selection data, and by analyzing this data, it obtains preference information necessary to generate a travel plan suitable for the user.

[0747] Step 6:

[0748] The server generates personalized travel plans based on analyzed preferences. These plans include destinations and modes of transportation, and are created using Python or the Flask framework. The input is preference information, and the output is a detailed travel plan.

[0749] Step 7:

[0750] The server sends the generated travel plan to the user's terminal and presents it to the user. The user can review the displayed plan and use it to help make a travel decision. The input is travel plan data, and the output is the final travel plan information presented.

[0751] 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.

[0752] This invention is a system that generates individual avatars based on facial images provided by the user, combines them with images of the travel destination, and recognizes the user's emotions to provide a more personalized travel experience. The introduction of an emotion engine makes it possible to create avatar expressions and suggest travel plans that reflect the user's emotional state.

[0753] The server first receives a facial image sent from the user's device. The received image is analyzed using an AI algorithm to determine its facial feature points and generate an avatar that replicates the user's characteristics. At this time, an emotion engine is used to identify the user's emotions during the image analysis and dynamically adjust the avatar's facial expression.

[0754] The generated avatar is combined with a selected travel destination image to create a composite image that makes it appear as if the user has visited that place. The composite image is delivered to the user's device, and the user can experience the image in a way that reflects their own emotional state through an emotion engine.

[0755] When a user selects an image of interest, that selection information is sent from the device to the server. The server uses an emotion engine to store and analyze the selection information, including the user's emotional state. Based on the results of this emotion analysis, a machine learning model analyzes the user's preferences more precisely and creates a travel plan accordingly.

[0756] The generated travel plan takes into account the user's current emotional state, suggesting destinations, schedules, and travel experiences that will resonate more emotionally. The server sends the completed travel plan to the user's device, allowing them to make reservations and check detailed information based on it.

[0757] For example, if a user provides a facial image while relaxing at home, the emotion engine can detect this "relaxed" state and display a calmer expression on the avatar's face, or suggest a travel plan that prioritizes relaxation. In this way, users can experience a more relatable and emotionally satisfying travel plan that reflects their emotions.

[0758] The following describes the processing flow.

[0759] Step 1:

[0760] The user uploads their facial image to the system using a device. The device receives the user's input and sends the facial image to the server.

[0761] Step 2:

[0762] The server applies an AI algorithm to analyze the received facial image. It extracts facial feature points and generates an avatar. During this process, the server uses an emotion engine to identify the user's emotional state and adjusts the avatar's facial expressions and posture accordingly.

[0763] Step 3:

[0764] The server combines the generated avatar with existing travel destination images. This combined image is adjusted so that the avatar's facial expressions change according to its emotional state.

[0765] Step 4:

[0766] The server selects the synthesized travel images and sends them to the user's device. The user can then view their avatar in various locations.

[0767] Step 5:

[0768] Users select images that interest them from those displayed on their device and provide selection information along with the corresponding emotions.

[0769] Step 6:

[0770] The device sends information about the images and emotions selected by the user to the server.

[0771] Step 7:

[0772] The server uses an emotion engine and machine learning models to analyze the received selection information and emotional state. Based on this, it automatically generates travel plans that are suitable for the user's preferences and emotions.

[0773] Step 8:

[0774] The server sends the generated travel plan to the user's device and presents it to the user. The user can then use this plan to create a more detailed travel plan.

[0775] (Example 2)

[0776] 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".

[0777] In modern travel planning, providing a personalized experience that accurately reflects a user's individual preferences and emotions is a challenging task. Traditional travel planning tends to offer uniform suggestions based on general information, and there is a need for individualized differentiation that exceeds user expectations.

[0778] 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.

[0779] In this invention, the server includes a device for receiving images of the user, a device for generating a virtual person based on the images, and a device for compositing the virtual person onto an arbitrary landscape image. This makes it possible to suggest travel experiences based on the user's individual emotions and preferences.

[0780] A "user" refers to a person who uses this system to create their own individual travel experience.

[0781] "Image" refers to visual information that represents a user, landscape, or other subject in digital format.

[0782] A "virtual character" refers to a digital avatar generated based on the user's characteristics.

[0783] "Landscape images" refer to visual information that shows a specific location, such as a travel destination.

[0784] "Image synthesis" refers to the process of combining multiple images to create a single, unified image.

[0785] "Emotion analysis" refers to a method of identifying a user's emotional state from their facial image or other data.

[0786] "Information storage" refers to the process of saving user choices and emotional data in a database or similar system.

[0787] "Itinerary" refers to a travel plan that includes the schedule and destinations planned for the user.

[0788] An "artificial intelligence model" refers to an algorithm that analyzes data and predicts user preferences and behavior.

[0789] This invention is a system for users to create personalized travel experiences. Specific embodiments are shown below.

[0790] The server first receives a facial image sent by the user using their device. This facial image is then processed using AI algorithms to analyze the user's individual characteristics. Python, OpenCV, TensorFlow, and other machine learning libraries are used for this processing.

[0791] The server uses these techniques to generate a virtual person, or avatar, that resembles the user. During this process, an emotion analysis engine is used to identify the user's current emotions and reflect them in the avatar's facial expressions. General-purpose emotion analysis tools and libraries are likely to be used for this process.

[0792] The server then composites the generated avatar image onto the selected landscape image. This compositing process utilizes scripts within image editing software such as Photoshop or GIMP. This creates a composite image that makes it appear as if the user is actually visiting the location.

[0793] The device receives the completed composite image and presents it to the user. The user can then enjoy a new experience that reflects their emotions while viewing the composite image on the screen.

[0794] Furthermore, users select items that interest them from the displayed composite images and send this selection information to the server via their device. The server stores the received selection information and uses a machine learning model to analyze the user's preferences based on this data. Through this process, a personalized travel plan that responds to the user's emotions is created.

[0795] One concrete example of this system is a feature where, based on a facial image provided when the user is relaxed, the emotion analysis engine detects a "relaxed" state and gives the avatar a calm expression. This makes it possible to suggest travel plans that prioritize relaxation. An example of a prompt message would be, "Generate an avatar that resembles a user in a relaxed state, and present it with images of relaxing travel destinations."

[0796] In this way, the system provides a travel experience that takes into account the user's emotions and preferences.

[0797] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0798] Step 1:

[0799] The user takes a facial image using a device and sends it to the image processing system. The input is the user's facial image, and the output is image data received on the server side. This image is then ready for analysis in subsequent processing.

[0800] Step 2:

[0801] The server analyzes the received facial image using an AI algorithm. The input is the received facial image, and the output is facial feature information. Specifically, it analyzes facial feature points using OpenCV, processes that data using a framework such as TensorFlow, and quantifies the user's facial features. This information is used for avatar generation.

[0802] Step 3:

[0803] The server uses an AI model to generate a virtual character (avatar) that resembles the user, based on the analyzed facial feature information. The output is the generated avatar image. This process results in a digital character that reflects the user's individuality.

[0804] Step 4:

[0805] The server performs emotion analysis during avatar generation and obtains emotion data based on the user's facial image. The input is the user's facial image and its analysis information, and the output is information about the user's emotional state. This information is reflected in the avatar as facial expressions by the emotion analysis engine.

[0806] Step 5:

[0807] The server combines the generated avatar image with a selected landscape image. The input is the avatar image and the landscape image, and the output is a composite image combining the two images. An automated processing tool in image editing software is used for this compositing.

[0808] Step 6:

[0809] The device receives the synthesized image from the server and displays it to the user. The input is the synthesized image sent by the server, and the output is the image presented to the user as visual information. This allows the user to create a virtual visualization of their travel experience that reflects their emotions.

[0810] Step 7:

[0811] Users select images that interest them from a collection of composite images and send this information to the server via their device. The input is the user's image selection information, and the output is the selection data sent to the server. This selection behavior reveals the user's preferences.

[0812] Step 8:

[0813] The server uses a machine learning model to perform preference analysis based on the information selected by the user. The output is the analyzed user preference data. This allows for the accumulation of data based on the user's emotions and past choices.

[0814] Step 9:

[0815] The server uses the analyzed preference data to generate a travel itinerary tailored to the user's emotions and sends it to the terminal. The input is preference analysis data, and the output is user-specific travel itinerary information. As a result, a travel plan tailored to the user's needs is provided.

[0816] (Application Example 2)

[0817] 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".

[0818] Current technology suffers from a lack of individuality and emotional reflection when users experience virtual travel. There is a need to provide avatars and travel plans that reflect each user's individual expressions and emotions to create a more personalized experience.

[0819] 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.

[0820] In this invention, the server includes means for receiving user facial data, means for generating individual virtual characters based on the facial data, means for presenting the synthesized visual data to the user, means for detecting the user's emotional state and dynamically adjusting the virtual character's facial expressions based on the corresponding emotion, and means for adjusting the travel plan according to the user's emotional state and suggesting destinations that resonate with the user's emotions. This enables a personalized virtual travel experience based on the user's emotions and preferences.

[0821] A "user" is an individual who uses the system to have a virtual travel experience.

[0822] "Appearance data" refers to image data that includes the user's facial features.

[0823] A "virtual character" is a digital character generated based on the user's facial data.

[0824] "Visiting destination visual data" refers to image or video data of the location that is the subject of the virtual trip.

[0825] "Synthesis" is the process of creating a single image by combining a virtual person with visual data of a visited location.

[0826] "Emotional state" refers to the user's current mental state, and is information judged from their facial expressions and behavior.

[0827] A "travel plan" is a schedule of destinations and activities suggested based on the user's emotions and preferences.

[0828] The server first receives facial data transmitted from the user's terminal. The received data can be processed using OpenCV, an image processing software, to analyze facial feature points. This analysis generates a virtual character based on the user's facial data. For generating the virtual character, 3D modeling tools such as Blender or Unity are used to create a character with dynamic, emotionally expressive facial expressions.

[0829] Next, the server uses an emotion engine to identify the user's emotional state from their facial data. In this process, an emotion recognition model, pre-trained using TensorFlow, infers the emotion from the user's facial expressions.

[0830] The generated virtual character is combined with visual data of the destination selected by the user. This combination is performed in real time and displayed on the user's device. Real-time rendering functions provided by Unity or Blender can be used to combine the virtual character with the background.

[0831] Based on the items the user has shown interest in, the server that provides travel plans uses machine learning models to analyze the user's selection patterns. In this process, the user's past selection history and emotional state are used as input to suggest more personalized destinations and schedules.

[0832] For example, if a user accesses this system while relaxing at home on a holiday, the server will detect a "relaxed" mood. A travel plan suited to relaxation will be suggested, including destinations surrounded by nature, and the virtual character's facial expression will be synthesized to reflect a calm state.

[0833] An example of a prompt message is: "Provide a user's facial image, analyze their emotions in real time to generate a personalized avatar, and then composite that avatar onto the background of the selected travel destination and display it."

[0834] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0835] Step 1:

[0836] The server receives facial data sent from the user's terminal. The input is the user's facial image data, which is held for the next processing step.

[0837] Step 2:

[0838] The server analyzes the received facial data using OpenCV to identify facial feature points. The input is the facial image received in step 1, and the output is the identified feature point data. Based on this feature point data, the server performs skeletal mapping of a virtual person and prepares to generate individual virtual characters.

[0839] Step 3:

[0840] The server uses TensorFlow to analyze the facial expression portion of the facial data and infer the user's emotional state. The input is the facial feature point data obtained in step 2, and the output is the identified emotional state data. This emotional data is used to dynamically adjust the facial expressions of the virtual character.

[0841] Step 4:

[0842] The server generates virtual characters using Blender or Unity and combines them with the visual data of the visited location. The input is the facial feature point data obtained in step 2 and the emotion data obtained in step 3, and the output is the combined visual data. In addition to generating virtual characters, this combination process also performs real-time rendering and processes the data into a format that can be immediately displayed to the user.

[0843] Step 5:

[0844] The server sends the synthesized visual data to the user's device, allowing the user to view it in real time. The input is the synthesized visual data from step 4, and the output is the travel experience image displayed on the user's device.

[0845] Step 6:

[0846] The user selects items of interest from the suggested visual data, and this selection information is sent back to the server. The input is the user's selection data, and the next steps are executed based on this selection.

[0847] Step 7:

[0848] The server generates a travel plan using a machine learning model based on the selected data and past selection history. The input is the selected data obtained from step 6, and the output is updated or newly generated travel plan data. This travel plan is adjusted to take into account the user's emotional state and preferences.

[0849] Step 8:

[0850] The server presents the generated travel plan to the user, allowing the user to consider their next course of action. The input is the travel plan data from step 7, and the output is the detailed travel plan displayed on the user's terminal.

[0851] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0852] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0854] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0855] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0856] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0857] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0858] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0859] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0860] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0861] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0862] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0863] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0864] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0865] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0866] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0867] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0868] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0869] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0870] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0871] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

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

[0873] (Claim 1)

[0874] A means of receiving a user's facial image,

[0875] Means for generating an avatar based on the aforementioned facial image,

[0876] A means for compositing the aforementioned avatar onto an existing travel destination image,

[0877] A means of providing the synthesized image to the user,

[0878] A means of collecting image information according to the user's selection,

[0879] A means of generating a travel plan based on the collected information,

[0880] A means for presenting the aforementioned travel plan to the user,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the means for generating the avatar includes an algorithm for analyzing facial feature points.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the means for generating the travel plan analyzes the user's selection patterns using a machine learning model.

[0886] "Example 1"

[0887] (Claim 1)

[0888] A means of receiving a user's facial image,

[0889] A means for generating an avatar using AI technology based on the aforementioned facial image,

[0890] Means for using image processing software to composite the aforementioned avatar with a background image,

[0891] A means of sending the synthesized image to the user's device,

[0892] A means for receiving image information selected by the user,

[0893] Means for using a machine learning model to analyze the aforementioned selection information,

[0894] A means of generating a travel plan using an AI model based on the analysis results,

[0895] A means of showing the aforementioned travel plan to the user,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the means for generating the avatar incorporates an algorithm for analyzing facial features.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein the means for generating the travel plan utilizes a data model to compare user selection information and analyze user preferences.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] A means of receiving a user's facial image,

[0904] Means for generating a visual artificial surrogate based on the aforementioned facial image,

[0905] Means for synthesizing the aforementioned visual artificial surrogate with existing destination visual information,

[0906] Means for providing synthesized visual information to the user,

[0907] A means of collecting visual information data in response to the user's expression of intent,

[0908] A means for generating a visit plan based on collected data,

[0909] A means for presenting the aforementioned visit plan to the user,

[0910] Means for a user terminal operating on a portable information processing device,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the means for generating the visual artificial surrogate includes a calculation method for analyzing facial feature points.

[0914] (Claim 3)

[0915] The system according to claim 1, wherein the means for generating the visit plan analyzes the user's choice trends using machine learning techniques.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] A device that receives images of the user,

[0919] A device that generates a virtual person based on the aforementioned image,

[0920] A device for compositing the aforementioned virtual person onto an arbitrary landscape image,

[0921] A device that transmits the synthesized image to the user,

[0922] A device that analyzes the user's emotions and adjusts the facial expressions of a virtual character,

[0923] A device that stores information tailored to the user's interests based on the aforementioned sentiment analysis,

[0924] A device that generates itineraries using accumulated information,

[0925] A device for displaying the aforementioned itinerary to the user,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the device for generating the virtual person includes a method for analyzing image features.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the device that generates the itinerary uses an artificial intelligence model to evaluate the user's selection tendencies.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A means of receiving user appearance data,

[0934] Means for generating individual virtual individuals based on the aforementioned appearance data,

[0935] A means for compositing the aforementioned virtual person with existing visual data of visited locations,

[0936] A means of presenting synthesized visual data to the user,

[0937] A means of collecting visual data information according to the user's selection,

[0938] A means for generating a travel plan based on collected information,

[0939] A means of presenting a travel plan to the user,

[0940] A means for detecting the user's emotional state and dynamically adjusting the facial expressions of a virtual character based on the corresponding emotion,

[0941] A means of adjusting travel plans according to the user's emotional state and suggesting destinations that resonate with their emotions,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, wherein the means for generating the virtual person includes an algorithm for analyzing facial feature points and an emotion determination engine.

[0945] (Claim 3)

[0946] The system according to claim 1, wherein the means for generating the travel plan analyzes the user's choice patterns and emotional state using a machine learning model. [Explanation of Symbols]

[0947] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving a user's facial image, Means for generating a visual artificial surrogate based on the aforementioned facial image, Means for synthesizing the aforementioned visual artificial surrogate with existing destination visual information, Means for providing synthesized visual information to the user, A means of collecting visual information data in response to the user's expression of intent, A means for generating a visit plan based on collected data, A means for presenting the aforementioned visit plan to the user, Means for a user terminal operating on a portable information processing device, A system that includes this.

2. The system according to claim 1, wherein the means for generating the visual artificial surrogate includes a calculation method for analyzing facial feature points.

3. The system according to claim 1, wherein the means for generating the visit plan analyzes the user's selection trends using machine learning techniques.

Images