system

Abstract

We provide the system. [Solution] Means for collecting user travel history, posts on communication services and related information, A means for analyzing the user's interests, preferences, or travel patterns based on the collected information, A means for generating the optimal travel plan for the user based on the analysis results, A means for interacting with the user in natural language and adjusting or confirming the aforementioned travel plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern travelers can access a variety of information sources, but it is difficult to achieve an optimal travel experience due to excessive information and a lack of options tailored to individual needs in the selection of travel plans. Also, it is an issue to seamlessly carry out the process from travel planning to reservation according to the preferences of travelers.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that collects and analyzes relevant information, primarily from a user's travel history and posts on communication services, and generates an optimal travel plan tailored to the user's interests and preferences. This system interacts with the user in natural language, dynamically adjusts the plan, and provides booking and purchase links, thereby providing consistent support from travel planning to execution.

[0006] A "user" refers to an individual or group that uses the system to generate or adjust travel plans.

[0007] "Travel history" refers to records of places a user has visited or trips they have taken in the past.

[0008] "Posts on communication services" refers to content such as comments, images, and videos that users publish on social networking services or other online platforms.

[0009] "Means of collecting information" refers to software or hardware for accessing and storing users' digital data in an analyzable format.

[0010] "Means of analysis" refers to algorithms and processing devices used to extract useful patterns and user characteristics from collected information.

[0011] "Means for generating travel plans" refers to calculation processes and applications for creating travel schedules and suggestions tailored to the user's characteristics.

[0012] "Means of natural language interaction" refers to an interface that allows users and computer systems to exchange information using normal human language. [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 proposes personalized travel plans to travelers. This system analyzes the user's travel history, posts on communication services, and the interests and preferences based on them to generate an optimal travel plan.

[0035] First, users access the system through an application or web interface. Users enter registration information to use the system and grant access to their travel history and communication service data. This allows the system to collect data about the user's interests and preferences.

[0036] Next, the server analyzes the collected data, including the user's travel history and posts on communication services. Natural language processing and machine learning algorithms are used to analyze this data. This reveals past travel trends and topics of interest.

[0037] Based on the collected data, the server generates a travel plan tailored to the user's characteristics. This plan includes suggestions for destinations, recommendations for tourist spots, potential accommodations, and budget estimates. This plan is customized to the user's individual needs and constraints.

[0038] The generated travel plan is presented to the user via their device. The user can interact with the system using natural language to view details of the travel plan and request changes. Based on user feedback, the server dynamically adjusts the plan and re-presents the updated plan.

[0039] Finally, once the user confirms their travel plan, the server provides links for booking the trip and purchasing tickets. This allows the user to quickly and efficiently prepare for their trip.

[0040] For example, if a user has previously visited historical European cities and made many posts about art galleries and museums on communication services, the server can then suggest a route that visits new historical European cities and major art galleries based on that information. In this way, the present invention can significantly improve the user experience by providing travel plans that are more closely tailored to the user's preferences.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users access the system through an application or web interface and create an account. During this process, they configure settings to allow access to their travel history and posted data on communication services.

[0044] Step 2:

[0045] The server collects data, including travel history and posts on communication services, based on user permission. This data is obtained in a privacy-conscious manner through APIs or data import tools.

[0046] Step 3:

[0047] The server analyzes the collected data using natural language processing and machine learning algorithms. This identifies the user's past interests and travel patterns, and generates this information as a digital profile.

[0048] Step 4:

[0049] The server generates a travel plan, including suitable destinations, tourist attractions, and accommodations, based on the analysis results. This plan is customized according to the user's characteristics.

[0050] Step 5:

[0051] The device presents the generated travel plan to the user via an interactive interface. The user can provide feedback through the interface regarding specific preferences and constraints.

[0052] Step 6:

[0053] Users interact with the system using natural language to request confirmation and modification of plan details. The server dynamically updates the plan based on this feedback.

[0054] Step 7:

[0055] The user finalizes their travel plan, and the server then generates and provides links for booking the trip and purchasing tickets.

[0056] Step 8:

[0057] Users use the provided links to make necessary reservations and purchases and prepare for their trip. The device displays the confirmed travel itinerary and required information to support the user experience.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] Conventional travel plan creation systems were unable to fully utilize users' past travel history or information posted on electronic communication services, making it difficult to automatically suggest optimal travel plans based on individual user preferences and actual travel trends. As a result, travelers had to manually research and adjust their plans each time, which was time-consuming and laborious.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting the user's past travel history, information transmitted on electronic communication services and related information, means for analyzing the user's preferences, tastes, or travel tendencies, and means for creating a travel plan optimized for the user using a generative artificial intelligence model. This makes it possible for the user to quickly and easily obtain an optimal travel plan that reflects their preferences and past tendencies without having to conduct detailed research.

[0063] A "user" is an individual or group that uses this system to plan travel, provides information to the system, and returns feedback.

[0064] "Travel history" refers to records of geographical locations a user has visited in the past and related activities, and is data used to analyze travel patterns.

[0065] "Electronic communication services" refer to platforms that allow users to transmit and share information on the internet, and include posted data and communication history.

[0066] "Transmitted information" refers to digital content such as text, images, and videos that users publish or share on electronic communication services.

[0067] "Preferences" refer to a user's interests, concerns, and tastes, and in particular, they indicate tendencies regarding the regions they want to visit and the activities they want to experience when traveling.

[0068] An "artificial intelligence model" is a computer program that learns from data, analyzes the user's past trends to make predictions, and generates plans and suggestions that are suitable for the user.

[0069] "Two-way communication" is a process in which a user and a system communicate with each other by sending and receiving information through natural language.

[0070] A "travel plan" is a comprehensive travel plan that includes the itinerary and destinations, tailored to the user's specific purposes and preferences.

[0071] In an embodiment for carrying out this invention, the system includes the following elements:

[0072] First, users establish access to the system using an application or web interface. Users grant the system permission to access their past travel history and outgoing information from electronic communication services. This information constitutes a dataset necessary to understand the user's preferences and travel tastes.

[0073] Next, the server collects this data and performs the actual analysis. For the analysis, spaCy is used as the natural language processing library, and TENSORFLOW® is used as the machine learning algorithm platform. This identifies patterns of preferences based on the user's past behavior. For example, it extracts cities that the user frequently visits and activities that interest them.

[0074] Based on the analysis results, the server utilizes a generative artificial intelligence model to generate a travel plan optimized for the user. This plan includes potential destinations, accommodations, and budget predictions, and is customized according to the user's individual needs. For example, if a user is interested in medieval history, the system might suggest a plan to tour historical cities in Europe.

[0075] The generated travel plan is presented to the user via the terminal. The user can interact with this plan using natural language on the interface, reviewing details and requesting modifications as needed. An example of a prompt might be, "Create a travel plan that includes visits to historical cities and museums, based on the user's past travel history and communication service posts."

[0076] This process allows users to efficiently obtain travel plans tailored to their preferences, resulting in a fast-paced and personalized travel experience.

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

[0078] Step 1:

[0079] Users first access the system through an application or web interface. As input, they provide permission to access their past travel history and outgoing information on electronic communication services. This information allows the system to build a user profile and collect data. As output, they obtain a state where they have been granted access rights for data collection.

[0080] Step 2:

[0081] The server collects data received from users and stores it in a database. The inputs here are the user's movement history and outgoing information. The data stored in the database is analyzed using a natural language processing library (e.g., spaCy) to tokenize text data and extract key phrases. The output is an analysis result indicating the user's preferences and tastes.

[0082] Step 3:

[0083] Based on the analysis results from Step 2, the server creates a generative AI model using a machine learning platform (e.g., TensorFlow) to generate a travel plan optimized for the user's preferences. The input includes the analyzed user's interests and past behavior data. Based on this data, it lists potential destinations and accommodations. The output is a customized travel plan.

[0084] Step 4:

[0085] The terminal displays the travel plan received from the server to the user. The input here is a pre-generated travel plan. The user can review the displayed plan and provide feedback to the system using natural language. The output includes the user's reviewed travel plan and any change requests.

[0086] Step 5:

[0087] The server dynamically adjusts the travel plan based on the user feedback received in step 4. The input is user feedback data. The generating AI model is reused to update the plan and generate a new plan. The output is an updated travel plan that reflects the user's requests.

[0088] Step 6:

[0089] Once the user confirms their plan, the server provides links for booking or purchasing based on the finalized travel plan. The input is the confirmed travel plan. A link to a booking site is generated and provided to the user. The output presents the user with a booking URL or purchase link, enabling quick booking.

[0090] (Application Example 1)

[0091] 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."

[0092] Modern travelers seek travel experiences tailored to their individual interests and preferences within limited time and budget constraints, but traditional travel planning services struggle to adequately meet these needs. Furthermore, it's difficult for users to experience a travel plan in a virtual environment before actually traveling, leaving uncertainty about whether they will be satisfied with the plan.

[0093] 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.

[0094] In this invention, the server includes means for acquiring information on the user's past behavior, records on an information sharing service, and related information; means for analyzing the user's characteristics based on the acquired information; means for generating a visit plan optimized for the user based on the analysis results; means for interacting with the user in natural language and adjusting or finalizing the visit plan; and means for visually reproducing the visit plan in a virtual reality environment. This makes it possible for the user to virtually experience the travel plan before actually traveling, providing an optimal travel experience tailored to individual needs.

[0095] "User's past behavior information" refers to the user's past activity history, including travel history and information posted on communication services.

[0096] An "information sharing service" is an online platform used by users to exchange and share information with other people.

[0097] A "virtual reality environment" is an artificial environment created using computer technology, a virtual space in which users can have an experience similar to that of the real world.

[0098] A "travel plan" is a personalized travel plan that includes the places a traveler will visit and the activities they will experience within a specific period of time.

[0099] "Communicating in natural language" means interacting with a computer system and communicating using the language that humans normally use.

[0100] "Analyzing user characteristics" means analyzing and understanding user interests and preferences based on collected data.

[0101] "Visual representation" means using visual techniques to display or reproduce things in a way that resembles reality.

[0102] This invention relates to a system that provides a visit plan tailored to the individual needs of the user and allows the user to experience that plan in a virtual reality environment. Specific embodiments thereof are described below.

[0103] Users access this system using smart glasses or head-mounted displays. The device retrieves the user's past behavioral information and records from information-sharing services and sends them to the server. Based on the collected information, the server analyzes the user's characteristics using natural language processing and machine learning algorithms. This analysis allows for a detailed understanding of the user's interests and preferences.

[0104] Next, the server generates a visit plan optimized for the user based on the analysis results. This plan is tailored to the user's specific interests and past travel history and includes destination suggestions and simulations of tourist attractions.

[0105] The server converts the generated travel plan into a virtual reality environment and visually recreates it. This allows the user to experience the destination as if they were virtually visiting it and to review the details of the plan. For example, if a user has a travel plan to Paris, it can realistically provide a VR experience that includes the Eiffel Tower and the Louvre Museum.

[0106] Users can interact with the system via their terminal using natural language to adjust or finalize their visit plans. The server receives user change requests as needed and dynamically updates the plan.

[0107] Furthermore, once the plan is finalized, the server presents the user with instructions for booking and purchasing. This allows the user to smoothly proceed with travel preparations.

[0108] As a concrete example, an example of a prompt message to a generative AI model would be: "If the user wants a VR trip to Paris, generate a personalized VR experience that includes the Eiffel Tower and the Louvre Museum."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device acquires the user's past behavioral information and records from information-sharing services. Specifically, it collects travel history and social media posting data to the extent permitted by the user. This information is then sent to the server and is treated as input data. The output is the collection of that behavioral information.

[0112] Step 2:

[0113] The server analyzes the user behavior information it receives. Using natural language processing tools (e.g., spaCy and NLTK) and machine learning algorithms (e.g., TensorFlow and PyTorch), it converts this information into data and extracts the user's interests and preferences. The input is the behavior information collected in the previous stage, and the output is a profile of the analyzed user characteristics and interests.

[0114] Step 3:

[0115] The server generates a user-optimized travel plan based on the analysis results. This plan includes recommended sightseeing spots and activities. The input is the user's characteristic profile, and the output is a personalized travel plan for that user.

[0116] Step 4:

[0117] The server prepares to visually reproduce the generated travel plan in a virtual reality environment. Specifically, it uses a VR development environment such as Unity 3D to build a virtual simulation of the travel destination. The input is the travel plan, and the output is VR simulation data based on that plan.

[0118] Step 5:

[0119] Users experience a virtual reality environment through their device and confirm or adjust their visit plan by interacting with the server in natural language. Input is user feedback and adjustment requests, and output is the adjusted visit plan.

[0120] Step 6:

[0121] After the final visit plan is confirmed, the server prepares to provide the user with a route for booking and purchasing travel. Specifically, it generates links and ticket purchase options and displays them on the user's device. The input is the confirmed visit plan, and the output is information such as booking links necessary for arrangements.

[0122] 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.

[0123] This invention is a travel planning system that combines a user's travel history and posts on communication services with an emotion engine that recognizes the user's emotions. Its purpose is to provide the most suitable travel plan while considering the user's emotions.

[0124] The system begins with the user accessing it through an application or web portal. The user grants the system access to travel history and communication service data, thereby making all the necessary data for sentiment analysis available.

[0125] The server collects users' travel history and posts on communication services and analyzes them using a proprietary algorithm. The collected data is then used with natural language processing and machine learning techniques to provide insights into users' behavioral patterns and interests.

[0126] Furthermore, the server uses an emotion engine to recognize the user's emotional state based on information extracted from communication service posts and user interactions. This allows for a more accurate identification of the mood and preferences the user desires for their travel plans. As an example of the results of emotion analysis, if the user is tired, a travel plan emphasizing relaxation will be provided, while if they express active emotions, a plan including active activities will be suggested.

[0127] The analysis results are used to generate a travel plan that includes the optimal destination, accommodation, and sightseeing activities for the user. This plan is presented to the user via their device, and the user can provide feedback through the interface regarding more specific requests or changes.

[0128] The server leverages feedback to dynamically update and adjust travel plans. It then finalizes the travel plan and simultaneously provides users with necessary booking and purchase links via their device. In this way, the system, combined with an emotion engine, delivers a personalized travel experience based on the user's emotions and needs.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users access the application or web portal, enter their profile information, and register. This includes granting permission to access their travel history and posts on communication services, after which the system is ready to be used.

[0132] Step 2:

[0133] The server securely collects users' travel history and posts from communication services using APIs and data import tools. The data is then organized into an analyzable format.

[0134] Step 3:

[0135] The server processes the collected dataset using a natural language processing algorithm to analyze the user's past interests and preferences. The results are stored in a database as a profile.

[0136] Step 4:

[0137] The server uses an emotion engine to determine a user's emotions from their posts and interactions on the communication service. This involves analyzing the tone of the text and keyword patterns to assess their emotions.

[0138] Step 5:

[0139] The server generates a travel plan best suited to the user based on analyzed preference and sentiment data. This plan includes destination selection, activity suggestions, and accommodation arrangements.

[0140] Step 6:

[0141] The device presents the generated travel plan through a user interface. The user can review the provided plan, provide feedback in natural language, or specify further requests.

[0142] Step 7:

[0143] Based on user feedback, the server dynamically readjusts the travel plan, taking into account new emotional states and incorporating specific requests.

[0144] Step 8:

[0145] The user confirms the travel plan they deem optimal. Once the confirmation instruction is sent from the device to the server, the server generates and provides a link for booking or purchase. This ensures a smooth travel booking process.

[0146] (Example 2)

[0147] 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".

[0148] Conventional travel planning systems have the drawback of being unable to adequately consider users' emotions and dynamic needs, resulting in the provision of only uniform travel plans. Therefore, there is a need to provide appropriate and flexible travel plans based on each user's individual emotional state and preferences.

[0149] 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.

[0150] In this invention, the server includes means for collecting user data, means for analyzing the collected data using natural language processing and learning algorithms to recognize the user's emotional state, and means for generating an optimal travel plan based on the user's emotional state and behavioral history. This makes it possible to provide personalized travel plans that respond to the user's dynamic requests.

[0151] "User data" refers to information related to a user, such as their travel history or posts on communication services.

[0152] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0153] A "learning algorithm" refers to a technique that learns patterns and rules from given data and uses that knowledge to make predictions and classifications on new data.

[0154] "Emotional state" refers to the user's psychological state and emotional tendencies.

[0155] A "travel plan" is a suggestion that includes the optimal destination and activities for the user's trip, and is based on the user's individual needs and feelings.

[0156] This invention is a system for individually optimizing a user's travel experience and is implemented in the following specific way.

[0157] Users access the system via an application or web portal. Users input data into the system by granting permission for access to data collected from their travel history and communication services. This data is stored in a cloud-based database and used for subsequent analysis.

[0158] The server collects user-authorized travel history and posts on communication services. This data collection utilizes API-based data retrieval and database access technologies. The specific software used is the Python programming language and its related libraries, enabling effective data aggregation from various platforms.

[0159] The collected data is analyzed using natural language processing (NLP) and learning algorithms. NLP techniques are implemented using Google® Cloud Natural Language API and open-source natural language processing libraries. This process extracts sentiment scores and areas of interest from user posts to recognize the user's emotional state.

[0160] Subsequently, the server generates an optimal travel plan based on the user's emotional state and past behavioral history. A generative AI model is used for plan generation. This model utilizes prompt statements to determine appropriate destinations and activities from multiple options. The following prompt statements are used as specific examples:

[0161] The user's recent emotion was identified as "needs relaxation." Please suggest a good hot spring resort.

[0162] The terminal presents the generated travel plan to the user and provides an interface for the user to provide feedback on the plan. Based on the user's feedback, the system dynamically updates the plan and provides the user with the final plan and booking link.

[0163] In this way, the system personalizes travel plans according to the user's emotions and needs, and provides support for appropriate booking and purchase.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users access the system via an application or web portal and grant permission to access travel history and communication service data. Inputs are user authentication information and access permission data, while output is a state ready for data collection. Specifically, users authenticate on a login screen and confirm access to relevant data within the application.

[0167] Step 2:

[0168] The server begins collecting data with the user's permission. The input is the user's travel and communication history data, and the output is the collected dataset. This stage involves specific actions such as interacting with an external communication service platform via an API to retrieve past travel history from the database.

[0169] Step 3:

[0170] The server uses the collected data to execute natural language processing and learning algorithms. The input is the collected dataset, and the output is an analysis result indicating the user's emotional state and interests. Specifically, it extracts emotional keywords from the dataset using NLP techniques, and then uses an emotion engine to evaluate the user's emotional state.

[0171] Step 4:

[0172] The server generates a travel plan based on the analysis results. The input is the user's emotional state and behavioral history, and analyzed interests. The output is a plan that includes a suitable travel destination, accommodation, and activities for the user. Here, a generative AI model is used to create prompts that recommend appropriate travel destinations and activities and to construct the plan.

[0173] Step 5:

[0174] The terminal presents the generated travel plan to the user. The input is the travel plan from the server, and the output is the plan information displayed via the user interface. Specifically, the user can review the plan displayed on the screen and provide real-time feedback on the plan.

[0175] Step 6:

[0176] The server receives user feedback and dynamically updates the travel plan. The input is user feedback, and the output is the updated travel plan. Based on the feedback information, the plan content is adjusted, and if necessary, it is regenerated using a new AI model.

[0177] Step 7:

[0178] The system provides users with their finalized travel plans and booking links via their device. The input is the finalized travel plan, and the output is booking information and customizable links. Specifically, the confirmed travel plan is sent to the user via email or application notification, making it easy for them to make necessary arrangements.

[0179] (Application Example 2)

[0180] 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".

[0181] Modern consumers struggle to find suitable products and services amidst a vast amount of information. At the same time, they demand personalized recommendations tailored to their individual emotions and preferences. In particular, the lack of product recommendations based on consumers' real-time emotions and interests in physical stores can lead to decreased satisfaction with the shopping experience. There is a need for systems that address these challenges.

[0182] 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.

[0183] In this invention, the server includes means for collecting the user's travel history, posts on communication services, and related information; means for analyzing the user's interests, preferences, or travel patterns based on the collected information; and means for extracting the user's visual or auditory information, analyzing their emotions in real time, and recommending products. This enables personalized product recommendations and travel plan provision tailored to the user's emotional state.

[0184] A "user" is a consumer who uses the system to receive travel planning and product recommendations.

[0185] "Travel history" refers to records of destinations and accommodations that a user has visited in the past.

[0186] "Posts on communication services" refer to messages and comments that users publish on social media or other online platforms.

[0187] "Means of collecting information" refers to the technologies and processes used to obtain users' travel history and posts on communication services.

[0188] "Means of analysis" refers to technologies used to analyze users' interests and preferences based on collected information.

[0189] "Methods for generating travel plans" refers to the process of formulating a travel plan suitable for the user based on analysis results.

[0190] "Means of interacting, adjusting, or confirming in natural language" refers to functions that communicate with users using natural language to modify or confirm travel plans.

[0191] "Means for extracting visual or auditory information and analyzing emotions in real time" refers to technologies that identify emotions in real time from what a user sees or the sounds they make.

[0192] "A means of recommending products" is a process of suggesting appropriate products based on the user's emotional state.

[0193] This invention provides a system for user travel planning and product recommendations. The system consists of three components: the user, the server, and the terminal.

[0194] First, the user accesses the system via a device. This device, such as smart glasses or a smartphone, is worn or carried by the user and is equipped with a camera to capture visual information and a microphone to acquire audio information. The user provides permission for the system to begin processing data by sharing their travel history and posts on communication services.

[0195] Next, the server collects travel history and posting data from communication services provided by the user. Furthermore, the server is equipped with a sentiment analysis engine and analyzes this data using natural language processing APIs (e.g., Google Cloud NLP, Amazon Comprehend). The server leverages machine learning algorithms to identify the user's emotional state and interests.

[0196] Based on this analysis, the server generates an optimal travel plan for the user. Furthermore, it recommends appropriate products to the device in real time based on the user's emotional state. This personalizes the user's shopping experience and travel planning, making it more satisfying.

[0197] Furthermore, users can receive feedback from the server via their devices and submit their opinions on travel plans and product recommendations. The server uses this feedback to dynamically adjust the plans and recommendations.

[0198] As a concrete example, when a user is browsing products in a physical store, the server analyzes visual and audio data and detects an emotion such as "I want to relax." In this case, a suggestion such as "Here are some recommended aromatherapy oils" would be displayed on the device's screen.

[0199] An example of a prompt message might be, "The user is in a state where they want to relax emotionally. Please recommend the best product from the store for this state." This allows the user to intelligently select products and services that match their emotional state.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server collects travel history and communication service posting data that users have authorized. Input includes information about past travel destinations and social media posts provided by the user. This data is stored in cloud storage and processed into a structured format. The output generates the dataset necessary for analysis.

[0203] Step 2:

[0204] The server uses a generative AI model and natural language processing API to analyze the collected data and identify the user's emotional state and interests. The input is the dataset generated in step 1, which the sentiment analysis engine uses as its basis. Data processing involves tokenization of the text data and sentiment scoring, and the output generates a profile of the user's emotional state and interests.

[0205] Step 3:

[0206] The server generates optimal travel plans and product recommendations for the user based on the emotional state and interest profiles created in Step 2. Inputs include the user's emotional state, past interests, and travel history. A recommendation algorithm is used for data calculation to determine the best options. The output consists of specific travel plans and product lists.

[0207] Step 4:

[0208] The terminal displays travel plans and product recommendations sent from the server to the user. This allows the user to receive suggestions in real time. The input is the plan and recommendation list generated in step 3, and the output is the information displayed on the user's visual display. The terminal uses audio and visual information as its interface.

[0209] Step 5:

[0210] The user sends feedback to the server about the information provided through the terminal. The input is the user's feedback, which includes plan adjustments and new requests. The output is the user's new requests sent to the server.

[0211] Step 6:

[0212] The server receives user feedback and dynamically updates travel plans and product recommendations. The input is the feedback received in step 5. As an update, the plan generation algorithm is run again, and new plans and recommendation lists are generated in the output. This ensures that users always receive the best service based on their needs.

[0213] 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.

[0214] 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 those described above. 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 shown 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is a system that proposes personalized travel plans to travelers. This system analyzes the user's travel history, posts on communication services, and the interests and preferences based on them to generate an optimal travel plan.

[0230] First, users access the system through an application or web interface. Users enter registration information to use the system and grant access to their travel history and communication service data. This allows the system to collect data about the user's interests and preferences.

[0231] Next, the server analyzes the collected data, including the user's travel history and posts on communication services. Natural language processing and machine learning algorithms are used to analyze this data. This reveals past travel trends and topics of interest.

[0232] Based on the collected data, the server generates a travel plan tailored to the user's characteristics. This plan includes suggestions for destinations, recommendations for tourist spots, potential accommodations, and budget estimates. This plan is customized to the user's individual needs and constraints.

[0233] The generated travel plan is presented to the user via their device. The user can interact with the system using natural language to view details of the travel plan and request changes. Based on user feedback, the server dynamically adjusts the plan and re-presents the updated plan.

[0234] Finally, once the user confirms their travel plan, the server provides links for booking the trip and purchasing tickets. This allows the user to quickly and efficiently prepare for their trip.

[0235] For example, if a user has previously visited historical European cities and made many posts about art galleries and museums on communication services, the server can then suggest a route that visits new historical European cities and major art galleries based on that information. In this way, the present invention can significantly improve the user experience by providing travel plans that are more closely tailored to the user's preferences.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] Users access the system through an application or web interface and create an account. During this process, they configure settings to allow access to their travel history and posted data on communication services.

[0239] Step 2:

[0240] The server collects data, including travel history and posts on communication services, based on user permission. This data is obtained in a privacy-conscious manner through APIs or data import tools.

[0241] Step 3:

[0242] The server analyzes the collected data using natural language processing and machine learning algorithms. This identifies the user's past interests and travel patterns, and generates this information as a digital profile.

[0243] Step 4:

[0244] The server generates a travel plan, including suitable destinations, tourist attractions, and accommodations, based on the analysis results. This plan is customized according to the user's characteristics.

[0245] Step 5:

[0246] The device presents the generated travel plan to the user via an interactive interface. The user can provide feedback through the interface regarding specific preferences and constraints.

[0247] Step 6:

[0248] Users interact with the system using natural language to request confirmation and modification of plan details. The server dynamically updates the plan based on this feedback.

[0249] Step 7:

[0250] The user finalizes their travel plan, and the server then generates and provides links for booking the trip and purchasing tickets.

[0251] Step 8:

[0252] Users use the provided links to make necessary reservations and purchases and prepare for their trip. The device displays the confirmed travel itinerary and required information to support the user experience.

[0253] (Example 1)

[0254] 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."

[0255] Conventional travel plan creation systems were unable to fully utilize users' past travel history or information posted on electronic communication services, making it difficult to automatically suggest optimal travel plans based on individual user preferences and actual travel trends. As a result, travelers had to manually research and adjust their plans each time, which was time-consuming and laborious.

[0256] 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.

[0257] In this invention, the server includes means for collecting the user's past travel history, information transmitted on electronic communication services and related information, means for analyzing the user's preferences, tastes, or travel tendencies, and means for creating a travel plan optimized for the user using a generative artificial intelligence model. This makes it possible for the user to quickly and easily obtain an optimal travel plan that reflects their preferences and past tendencies without having to conduct detailed research.

[0258] A "user" is an individual or group that uses this system to plan travel, provides information to the system, and returns feedback.

[0259] "Travel history" refers to records of geographical locations a user has visited in the past and related activities, and is data used to analyze travel patterns.

[0260] "Electronic communication services" refer to platforms that allow users to transmit and share information on the internet, and include posted data and communication history.

[0261] "Transmitted information" refers to digital content such as text, images, and videos that users publish or share on electronic communication services.

[0262] "Preferences" refer to a user's interests, concerns, and tastes, and in particular, they indicate tendencies regarding the regions they want to visit and the activities they want to experience when traveling.

[0263] An "artificial intelligence model" is a computer program that learns from data, analyzes the user's past trends to make predictions, and generates plans and suggestions that are suitable for the user.

[0264] "Two-way communication" is a process in which a user and a system communicate with each other by sending and receiving information through natural language.

[0265] A "travel plan" is a comprehensive travel plan that includes the itinerary and destinations, tailored to the user's specific purposes and preferences.

[0266] In an embodiment for carrying out this invention, the system includes the following elements:

[0267] First, users establish access to the system using an application or web interface. Users grant the system permission to access their past travel history and outgoing information from electronic communication services. This information constitutes a dataset necessary to understand the user's preferences and travel tastes.

[0268] Next, the server collects this data and performs the actual analysis. For the analysis, spaCy is used as the natural language processing library, and TensorFlow is used as the platform for machine learning algorithms. This identifies patterns of preferences based on the user's past behavior. For example, it extracts cities that the user frequently visits and activities that interest them.

[0269] Based on the analysis results, the server utilizes a generative artificial intelligence model to generate a travel plan optimized for the user. This plan includes potential destinations, accommodations, and budget predictions, and is customized according to the user's individual needs. For example, if a user is interested in medieval history, the system might suggest a plan to tour historical cities in Europe.

[0270] The generated travel plan is presented to the user via the terminal. The user can interact with this plan using natural language on the interface, reviewing details and requesting modifications as needed. An example of a prompt might be, "Create a travel plan that includes visits to historical cities and museums, based on the user's past travel history and communication service posts."

[0271] This process allows users to efficiently obtain travel plans tailored to their preferences, resulting in a fast-paced and personalized travel experience.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] Users first access the system through an application or web interface. As input, they provide permission to access their past travel history and outgoing information on electronic communication services. This information allows the system to build a user profile and collect data. As output, they obtain a state where they have been granted access rights for data collection.

[0275] Step 2:

[0276] The server collects data received from users and stores it in a database. The inputs here are the user's movement history and outgoing information. The data stored in the database is analyzed using a natural language processing library (e.g., spaCy) to tokenize text data and extract key phrases. The output is an analysis result indicating the user's preferences and tastes.

[0277] Step 3:

[0278] Based on the analysis results from Step 2, the server creates a generative AI model using a machine learning platform (e.g., TensorFlow) to generate a travel plan optimized for the user's preferences. The input includes the analyzed user's interests and past behavior data. Based on this data, it lists potential destinations and accommodations. The output is a customized travel plan.

[0279] Step 4:

[0280] The terminal displays the travel plan received from the server to the user. The input here is a pre-generated travel plan. The user can review the displayed plan and provide feedback to the system using natural language. The output includes the user's reviewed travel plan and any change requests.

[0281] Step 5:

[0282] Based on the user feedback received in step 4, the server dynamically adjusts the travel plan. The input is the feedback data from the user. It reuses the generative AI model to update the plan and generate a new plan proposal. The output is an updated travel plan that reflects the user's requirements.

[0283] Step 6:

[0284] When the user finalizes the plan, the server provides links for arrangements or purchases based on the finally finalized travel plan. The input is the finalized travel plan. It generates a link to the reservation site and provides it to the user. The output is that reservation URLs and purchase links are presented to the user, enabling quick reservations.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] Modern travelers seek travel experiences based on their individual interests and preferences within limited time and budget, but traditional travel planning services have difficulty fully meeting these needs. Also, it is difficult to experience the travel plan in a virtual environment before the actual trip, and there is a problem that it is unclear whether the user is satisfied with the plan.

[0288] 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.

[0289] In this invention, the server includes means for acquiring information on the user's past behavior, records on an information sharing service, and related information; means for analyzing the user's characteristics based on the acquired information; means for generating a visit plan optimized for the user based on the analysis results; means for interacting with the user in natural language and adjusting or finalizing the visit plan; and means for visually reproducing the visit plan in a virtual reality environment. This makes it possible for the user to virtually experience the travel plan before actually traveling, providing an optimal travel experience tailored to individual needs.

[0290] "User's past behavior information" refers to the user's past activity history, including travel history and information posted on communication services.

[0291] An "information sharing service" is an online platform used by users to exchange and share information with other people.

[0292] A "virtual reality environment" is an artificial environment created using computer technology, a virtual space in which users can have an experience similar to that of the real world.

[0293] A "travel plan" is a personalized travel plan that includes the places a traveler will visit and the activities they will experience within a specific period of time.

[0294] "Communicating in natural language" means interacting with a computer system and communicating using the language that humans normally use.

[0295] "Analyzing user characteristics" means analyzing and understanding user interests and preferences based on collected data.

[0296] "Visual representation" means using visual techniques to display or reproduce things in a way that resembles reality.

[0297] This invention relates to a system that provides a visit plan tailored to the individual needs of the user and allows the user to experience that plan in a virtual reality environment. Specific embodiments thereof are described below.

[0298] Users access this system using smart glasses or head-mounted displays. The device retrieves the user's past behavioral information and records from information-sharing services and sends them to the server. Based on the collected information, the server analyzes the user's characteristics using natural language processing and machine learning algorithms. This analysis allows for a detailed understanding of the user's interests and preferences.

[0299] Next, the server generates a visit plan optimized for the user based on the analysis results. This plan is tailored to the user's specific interests and past travel history and includes destination suggestions and simulations of tourist attractions.

[0300] The server converts the generated travel plan into a virtual reality environment and visually recreates it. This allows the user to experience the destination as if they were virtually visiting it and to review the details of the plan. For example, if a user has a travel plan to Paris, it can realistically provide a VR experience that includes the Eiffel Tower and the Louvre Museum.

[0301] Users can interact with the system via their terminal using natural language to adjust or finalize their visit plans. The server receives user change requests as needed and dynamically updates the plan.

[0302] Furthermore, once the plan is finalized, the server presents the user with instructions for booking and purchasing. This allows the user to smoothly proceed with travel preparations.

[0303] As a concrete example, an example of a prompt message to a generative AI model would be: "If the user wants a VR trip to Paris, generate a personalized VR experience that includes the Eiffel Tower and the Louvre Museum."

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The terminal acquires the user's past behavior information and records on the information sharing service. Specifically, it collects travel history and social media posting data within the scope permitted by the user. This information is then transmitted to the server and is treated as input data. The output is the set of the collected behavior information.

[0307] Step 2:

[0308] The server analyzes the received user behavior information. Using natural language processing tools (e.g., spaCy or NLTK) and machine learning algorithms (e.g., TensorFlow or PyTorch), this information is made into data to extract the user's interests and preferences. The input is the behavior information collected in the previous step, and the output is the analyzed user characteristics and interest profile.

[0309] Step 3:

[0310] Based on the analysis result, the server generates an optimized visit plan for the user. This plan includes recommended tourist attractions and activities. The input is the user's characteristic profile, and the output is a personalized travel plan for the user.

[0311] Step 4:

[0312] The server prepares to visually reproduce the generated visit plan in a virtual reality environment. As a specific operation, it uses a VR development environment such as Unity 3D to construct a virtual simulation of the travel destination. The input is the travel plan, and the output is VR simulation data along with that plan.

[0313] Step 5:

[0314] Users experience a virtual reality environment through their device and confirm or adjust their visit plan by interacting with the server in natural language. Input is user feedback and adjustment requests, and output is the adjusted visit plan.

[0315] Step 6:

[0316] After the final visit plan is confirmed, the server prepares to provide the user with a route for booking and purchasing travel. Specifically, it generates links and ticket purchase options and displays them on the user's device. The input is the confirmed visit plan, and the output is information such as booking links necessary for arrangements.

[0317] 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.

[0318] This invention is a travel planning system that combines a user's travel history and posts on communication services with an emotion engine that recognizes the user's emotions. Its purpose is to provide the most suitable travel plan while considering the user's emotions.

[0319] The system begins with the user accessing it through an application or web portal. The user grants the system access to travel history and communication service data, thereby making all the necessary data for sentiment analysis available.

[0320] The server collects users' travel history and posts on communication services and analyzes them using a proprietary algorithm. The collected data is then used with natural language processing and machine learning techniques to provide insights into users' behavioral patterns and interests.

[0321] Furthermore, the server uses an emotion engine to recognize the user's emotional state based on information extracted from communication service posts and user interactions. This allows for a more accurate identification of the mood and preferences the user desires for their travel plans. As an example of the results of emotion analysis, if the user is tired, a travel plan emphasizing relaxation will be provided, while if they express active emotions, a plan including active activities will be suggested.

[0322] The analysis results are used to generate a travel plan that includes the optimal destination, accommodation, and sightseeing activities for the user. This plan is presented to the user via their device, and the user can provide feedback through the interface regarding more specific requests or changes.

[0323] The server leverages feedback to dynamically update and adjust travel plans. It then finalizes the travel plan and simultaneously provides users with necessary booking and purchase links via their device. In this way, the system, combined with an emotion engine, delivers a personalized travel experience based on the user's emotions and needs.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users access the application or web portal, enter their profile information, and register. This includes granting permission to access their travel history and posts on communication services, after which the system is ready to be used.

[0327] Step 2:

[0328] The server securely collects users' travel history and posts from communication services using APIs and data import tools. The data is then organized into an analyzable format.

[0329] Step 3:

[0330] The server processes the collected dataset using a natural language processing algorithm to analyze the user's past interests and preferences. The results are stored in a database as a profile.

[0331] Step 4:

[0332] The server uses an emotion engine to determine a user's emotions from their posts and interactions on the communication service. This involves analyzing the tone of the text and keyword patterns to assess their emotions.

[0333] Step 5:

[0334] The server generates a travel plan best suited to the user based on analyzed preference and sentiment data. This plan includes destination selection, activity suggestions, and accommodation arrangements.

[0335] Step 6:

[0336] The device presents the generated travel plan through a user interface. The user can review the provided plan, provide feedback in natural language, or specify further requests.

[0337] Step 7:

[0338] Based on user feedback, the server dynamically readjusts the travel plan, taking into account new emotional states and incorporating specific requests.

[0339] Step 8:

[0340] The user confirms the travel plan they deem optimal. Once the confirmation instruction is sent from the device to the server, the server generates and provides a link for booking or purchase. This ensures a smooth travel booking process.

[0341] (Example 2)

[0342] 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".

[0343] Conventional travel planning systems have the drawback of being unable to adequately consider users' emotions and dynamic needs, resulting in the provision of only uniform travel plans. Therefore, there is a need to provide appropriate and flexible travel plans based on each user's individual emotional state and preferences.

[0344] 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.

[0345] In this invention, the server includes means for collecting user data, means for analyzing the collected data using natural language processing and learning algorithms to recognize the user's emotional state, and means for generating an optimal travel plan based on the user's emotional state and behavioral history. This makes it possible to provide personalized travel plans that respond to the user's dynamic requests.

[0346] "User data" refers to information related to a user, such as their travel history or posts on communication services.

[0347] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0348] A "learning algorithm" refers to a technique that learns patterns and rules from given data and uses that knowledge to make predictions and classifications on new data.

[0349] "Emotional state" refers to the user's psychological state and emotional tendencies.

[0350] A "travel plan" is a suggestion that includes the optimal destination and activities for the user's trip, and is based on the user's individual needs and feelings.

[0351] This invention is a system for individually optimizing a user's travel experience and is implemented in the following specific way.

[0352] Users access the system via an application or web portal. Users input data into the system by granting permission for access to data collected from their travel history and communication services. This data is stored in a cloud-based database and used for subsequent analysis.

[0353] The server collects user-authorized travel history and posts on communication services. This data collection utilizes API-based data retrieval and database access technologies. The specific software used is the Python programming language and its related libraries, enabling effective data aggregation from various platforms.

[0354] The collected data is analyzed using natural language processing (NLP) and learning algorithms. NLP techniques are implemented using the Google Cloud Natural Language API and open-source natural language processing libraries. This process extracts sentiment scores and areas of interest from user posts to recognize the user's emotional state.

[0355] Subsequently, the server generates an optimal travel plan based on the user's emotional state and past behavioral history. A generative AI model is used for plan generation. This model utilizes prompt statements to determine appropriate destinations and activities from multiple options. The following prompt statements are used as specific examples:

[0356] The user's recent emotion was identified as "needs relaxation." Please suggest a good hot spring resort.

[0357] The terminal presents the generated travel plan to the user and provides an interface for the user to provide feedback on the plan. Based on the user's feedback, the system dynamically updates the plan and provides the user with the final plan and booking link.

[0358] In this way, the system personalizes travel plans according to the user's emotions and needs, and provides support for appropriate booking and purchase.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] Users access the system via an application or web portal and grant permission to access travel history and communication service data. Inputs are user authentication information and access permission data, while output is a state ready for data collection. Specifically, users authenticate on a login screen and confirm access to relevant data within the application.

[0362] Step 2:

[0363] The server begins collecting data with the user's permission. The input is the user's travel and communication history data, and the output is the collected dataset. This stage involves specific actions such as interacting with an external communication service platform via an API to retrieve past travel history from the database.

[0364] Step 3:

[0365] The server uses the collected data to execute natural language processing and learning algorithms. The input is the collected dataset, and the output is an analysis result indicating the user's emotional state and interests. Specifically, it extracts emotional keywords from the dataset using NLP techniques, and then uses an emotion engine to evaluate the user's emotional state.

[0366] Step 4:

[0367] The server generates a travel plan based on the analysis results. The input is the user's emotional state and behavioral history, and analyzed interests. The output is a plan that includes a suitable travel destination, accommodation, and activities for the user. Here, a generative AI model is used to create prompts that recommend appropriate travel destinations and activities and to construct the plan.

[0368] Step 5:

[0369] The terminal presents the generated travel plan to the user. The input is the travel plan from the server, and the output is the plan information displayed via the user interface. Specifically, the user can review the plan displayed on the screen and provide real-time feedback on the plan.

[0370] Step 6:

[0371] The server receives user feedback and dynamically updates the travel plan. The input is user feedback, and the output is the updated travel plan. Based on the feedback information, the plan content is adjusted, and if necessary, it is regenerated using a new AI model.

[0372] Step 7:

[0373] The system provides users with their finalized travel plans and booking links via their device. The input is the finalized travel plan, and the output is booking information and customizable links. Specifically, the confirmed travel plan is sent to the user via email or application notification, making it easy for them to make necessary arrangements.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0376] Modern consumers struggle to find suitable products and services amidst a vast amount of information. At the same time, they demand personalized recommendations tailored to their individual emotions and preferences. In particular, the lack of product recommendations based on consumers' real-time emotions and interests in physical stores can lead to decreased satisfaction with the shopping experience. There is a need for systems that address these challenges.

[0377] 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.

[0378] In this invention, the server includes means for collecting the user's travel history, posts on communication services, and related information; means for analyzing the user's interests, preferences, or travel patterns based on the collected information; and means for extracting the user's visual or auditory information, analyzing their emotions in real time, and recommending products. This enables personalized product recommendations and travel plan provision tailored to the user's emotional state.

[0379] A "user" is a consumer who uses the system to receive travel planning and product recommendations.

[0380] "Travel history" refers to records of destinations and accommodations that a user has visited in the past.

[0381] "Posts on communication services" refer to messages and comments that users publish on social media or other online platforms.

[0382] "Means of collecting information" refers to the technologies and processes used to obtain users' travel history and posts on communication services.

[0383] "Means of analysis" refers to technologies used to analyze users' interests and preferences based on collected information.

[0384] "Methods for generating travel plans" refers to the process of formulating a travel plan suitable for the user based on analysis results.

[0385] "Means of interacting, adjusting, or confirming in natural language" refers to functions that communicate with users using natural language to modify or confirm travel plans.

[0386] "Means for extracting visual or auditory information and analyzing emotions in real time" refers to technologies that identify emotions in real time from what a user sees or the sounds they make.

[0387] "A means of recommending products" is a process of suggesting appropriate products based on the user's emotional state.

[0388] This invention provides a system for user travel planning and product recommendations. The system consists of three components: the user, the server, and the terminal.

[0389] First, the user accesses the system via a device. This device, such as smart glasses or a smartphone, is worn or carried by the user and is equipped with a camera to capture visual information and a microphone to acquire audio information. The user provides permission for the system to begin processing data by sharing their travel history and posts on communication services.

[0390] Next, the server collects travel history and posting data from communication services provided by the user. Furthermore, the server is equipped with a sentiment analysis engine and analyzes this data using natural language processing APIs (e.g., Google Cloud NLP, Amazon Comprehend). The server leverages machine learning algorithms to identify the user's emotional state and interests.

[0391] Based on this analysis, the server generates an optimal travel plan for the user. Furthermore, it recommends appropriate products to the device in real time based on the user's emotional state. This personalizes the user's shopping experience and travel planning, making it more satisfying.

[0392] Furthermore, users can receive feedback from the server via their devices and submit their opinions on travel plans and product recommendations. The server uses this feedback to dynamically adjust the plans and recommendations.

[0393] As a concrete example, when a user is browsing products in a physical store, the server analyzes visual and audio data and detects an emotion such as "I want to relax." In this case, a suggestion such as "Here are some recommended aromatherapy oils" would be displayed on the device's screen.

[0394] An example of a prompt message might be, "The user is in a state where they want to relax emotionally. Please recommend the best product from the store for this state." This allows the user to intelligently select products and services that match their emotional state.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The server collects travel history and communication service posting data that users have authorized. Input includes information about past travel destinations and social media posts provided by the user. This data is stored in cloud storage and processed into a structured format. The output generates the dataset necessary for analysis.

[0398] Step 2:

[0399] The server uses a generative AI model and natural language processing API to analyze the collected data and identify the user's emotional state and interests. The input is the dataset generated in step 1, which the sentiment analysis engine uses as its basis. Data processing involves tokenization of the text data and sentiment scoring, and the output generates a profile of the user's emotional state and interests.

[0400] Step 3:

[0401] The server generates optimal travel plans and product recommendations for the user based on the emotional state and interest profiles created in Step 2. Inputs include the user's emotional state, past interests, and travel history. A recommendation algorithm is used for data calculation to determine the best options. The output consists of specific travel plans and product lists.

[0402] Step 4:

[0403] The terminal displays travel plans and product recommendations sent from the server to the user. This allows the user to receive suggestions in real time. The input is the plan and recommendation list generated in step 3, and the output is the information displayed on the user's visual display. The terminal uses audio and visual information as its interface.

[0404] Step 5:

[0405] The user sends feedback to the server about the information provided through the terminal. The input is the user's feedback, which includes plan adjustments and new requests. The output is the user's new requests sent to the server.

[0406] Step 6:

[0407] The server receives user feedback and dynamically updates travel plans and product recommendations. The input is the feedback received in step 5. As an update, the plan generation algorithm is run again, and new plans and recommendation lists are generated in the output. This ensures that users always receive the best service based on their needs.

[0408] 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.

[0409] 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 those described above. 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 shown 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is a system that proposes personalized travel plans to travelers. This system analyzes the user's travel history, posts on communication services, and the interests and preferences based on them to generate an optimal travel plan.

[0425] First, users access the system through an application or web interface. Users enter registration information to use the system and grant access to their travel history and communication service data. This allows the system to collect data about the user's interests and preferences.

[0426] Next, the server analyzes the collected data, including the user's travel history and posts on communication services. Natural language processing and machine learning algorithms are used to analyze this data. This reveals past travel trends and topics of interest.

[0427] Based on the collected data, the server generates a travel plan tailored to the user's characteristics. This plan includes suggestions for destinations, recommendations for tourist spots, potential accommodations, and budget estimates. This plan is customized to the user's individual needs and constraints.

[0428] The generated travel plan is presented to the user via their device. The user can interact with the system using natural language to view details of the travel plan and request changes. Based on user feedback, the server dynamically adjusts the plan and re-presents the updated plan.

[0429] Finally, once the user confirms their travel plan, the server provides links for booking the trip and purchasing tickets. This allows the user to quickly and efficiently prepare for their trip.

[0430] For example, if a user has previously visited historical European cities and made many posts about art galleries and museums on communication services, the server can then suggest a route that visits new historical European cities and major art galleries based on that information. In this way, the present invention can significantly improve the user experience by providing travel plans that are more closely tailored to the user's preferences.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] Users access the system through an application or web interface and create an account. During this process, they configure settings to allow access to their travel history and posted data on communication services.

[0434] Step 2:

[0435] The server collects data, including travel history and posts on communication services, based on user permission. This data is obtained in a privacy-conscious manner through APIs or data import tools.

[0436] Step 3:

[0437] The server analyzes the collected data using natural language processing and machine learning algorithms. This identifies the user's past interests and travel patterns, and generates this information as a digital profile.

[0438] Step 4:

[0439] The server generates a travel plan, including suitable destinations, tourist attractions, and accommodations, based on the analysis results. This plan is customized according to the user's characteristics.

[0440] Step 5:

[0441] The device presents the generated travel plan to the user via an interactive interface. The user can provide feedback through the interface regarding specific preferences and constraints.

[0442] Step 6:

[0443] Users interact with the system using natural language to request confirmation and modification of plan details. The server dynamically updates the plan based on this feedback.

[0444] Step 7:

[0445] The user finalizes their travel plan, and the server then generates and provides links for booking the trip and purchasing tickets.

[0446] Step 8:

[0447] Users use the provided links to make necessary reservations and purchases and prepare for their trip. The device displays the confirmed travel itinerary and required information to support the user experience.

[0448] (Example 1)

[0449] 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."

[0450] Conventional travel plan creation systems were unable to fully utilize users' past travel history or information posted on electronic communication services, making it difficult to automatically suggest optimal travel plans based on individual user preferences and actual travel trends. As a result, travelers had to manually research and adjust their plans each time, which was time-consuming and laborious.

[0451] 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.

[0452] In this invention, the server includes means for collecting the user's past travel history, information transmitted on electronic communication services and related information, means for analyzing the user's preferences, tastes, or travel tendencies, and means for creating a travel plan optimized for the user using a generative artificial intelligence model. This makes it possible for the user to quickly and easily obtain an optimal travel plan that reflects their preferences and past tendencies without having to conduct detailed research.

[0453] A "user" is an individual or group that uses this system to plan travel, provides information to the system, and returns feedback.

[0454] "Travel history" refers to records of geographical locations a user has visited in the past and related activities, and is data used to analyze travel patterns.

[0455] "Electronic communication services" refer to platforms that allow users to transmit and share information on the internet, and include posted data and communication history.

[0456] "Transmitted information" refers to digital content such as text, images, and videos that users publish or share on electronic communication services.

[0457] "Preferences" refer to a user's interests, concerns, and tastes, and in particular, they indicate tendencies regarding the regions they want to visit and the activities they want to experience when traveling.

[0458] An "artificial intelligence model" is a computer program that learns from data, analyzes the user's past trends to make predictions, and generates plans and suggestions that are suitable for the user.

[0459] "Two-way communication" is a process in which a user and a system communicate with each other by sending and receiving information through natural language.

[0460] A "travel plan" is a comprehensive travel plan that includes the itinerary and destinations, tailored to the user's specific purposes and preferences.

[0461] In an embodiment for carrying out this invention, the system includes the following elements:

[0462] First, users establish access to the system using an application or web interface. Users grant the system permission to access their past travel history and outgoing information from electronic communication services. This information constitutes a dataset necessary to understand the user's preferences and travel tastes.

[0463] Next, the server collects this data and performs the actual analysis. For the analysis, spaCy is used as the natural language processing library, and TensorFlow is used as the platform for machine learning algorithms. This identifies patterns of preferences based on the user's past behavior. For example, it extracts cities that the user frequently visits and activities that interest them.

[0464] Based on the analysis results, the server utilizes a generative artificial intelligence model to generate a travel plan optimized for the user. This plan includes potential destinations, accommodations, and budget predictions, and is customized according to the user's individual needs. For example, if a user is interested in medieval history, the system might suggest a plan to tour historical cities in Europe.

[0465] The generated travel plan is presented to the user via the terminal. The user can interact with this plan using natural language on the interface, reviewing details and requesting modifications as needed. An example of a prompt might be, "Create a travel plan that includes visits to historical cities and museums, based on the user's past travel history and communication service posts."

[0466] This process allows users to efficiently obtain travel plans tailored to their preferences, resulting in a fast-paced and personalized travel experience.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] Users first access the system through an application or web interface. As input, they provide permission to access their past travel history and outgoing information on electronic communication services. This information allows the system to build a user profile and collect data. As output, they obtain a state where they have been granted access rights for data collection.

[0470] Step 2:

[0471] The server collects data received from users and stores it in a database. The inputs here are the user's movement history and outgoing information. The data stored in the database is analyzed using a natural language processing library (e.g., spaCy) to tokenize text data and extract key phrases. The output is an analysis result indicating the user's preferences and tastes.

[0472] Step 3:

[0473] Based on the analysis results from Step 2, the server creates a generative AI model using a machine learning platform (e.g., TensorFlow) to generate a travel plan optimized for the user's preferences. The input includes the analyzed user's interests and past behavior data. Based on this data, it lists potential destinations and accommodations. The output is a customized travel plan.

[0474] Step 4:

[0475] The terminal displays the travel plan received from the server to the user. The input here is a pre-generated travel plan. The user can review the displayed plan and provide feedback to the system using natural language. The output includes the user's reviewed travel plan and any change requests.

[0476] Step 5:

[0477] The server dynamically adjusts the travel plan based on the user feedback received in step 4. The input is user feedback data. The generating AI model is reused to update the plan and generate a new plan. The output is an updated travel plan that reflects the user's requests.

[0478] Step 6:

[0479] Once the user confirms their plan, the server provides links for booking or purchasing based on the finalized travel plan. The input is the confirmed travel plan. A link to a booking site is generated and provided to the user. The output presents the user with a booking URL or purchase link, enabling quick booking.

[0480] (Application Example 1)

[0481] 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."

[0482] Modern travelers seek travel experiences tailored to their individual interests and preferences within limited time and budget constraints, but traditional travel planning services struggle to adequately meet these needs. Furthermore, it's difficult for users to experience a travel plan in a virtual environment before actually traveling, leaving uncertainty about whether they will be satisfied with the plan.

[0483] 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.

[0484] In this invention, the server includes means for acquiring information on the user's past behavior, records on an information sharing service, and related information; means for analyzing the user's characteristics based on the acquired information; means for generating a visit plan optimized for the user based on the analysis results; means for interacting with the user in natural language and adjusting or finalizing the visit plan; and means for visually reproducing the visit plan in a virtual reality environment. This makes it possible for the user to virtually experience the travel plan before actually traveling, providing an optimal travel experience tailored to individual needs.

[0485] "User's past behavior information" refers to the user's past activity history, including travel history and information posted on communication services.

[0486] An "information sharing service" is an online platform used by users to exchange and share information with other people.

[0487] A "virtual reality environment" is an artificial environment created using computer technology, a virtual space in which users can have an experience similar to that of the real world.

[0488] A "travel plan" is a personalized travel plan that includes the places a traveler will visit and the activities they will experience within a specific period of time.

[0489] "Communicating in natural language" means interacting with a computer system and communicating using the language that humans normally use.

[0490] "Analyzing user characteristics" means analyzing and understanding user interests and preferences based on collected data.

[0491] "Visual representation" means using visual techniques to display or reproduce things in a way that resembles reality.

[0492] This invention relates to a system that provides a visit plan tailored to the individual needs of the user and allows the user to experience that plan in a virtual reality environment. Specific embodiments thereof are described below.

[0493] Users access this system using smart glasses or head-mounted displays. The device retrieves the user's past behavioral information and records from information-sharing services and sends them to the server. Based on the collected information, the server analyzes the user's characteristics using natural language processing and machine learning algorithms. This analysis allows for a detailed understanding of the user's interests and preferences.

[0494] Next, the server generates a visit plan optimized for the user based on the analysis results. This plan is tailored to the user's specific interests and past travel history and includes destination suggestions and simulations of tourist attractions.

[0495] The server converts the generated travel plan into a virtual reality environment and visually recreates it. This allows the user to experience the destination as if they were virtually visiting it and to review the details of the plan. For example, if a user has a travel plan to Paris, it can realistically provide a VR experience that includes the Eiffel Tower and the Louvre Museum.

[0496] Users can interact with the system via their terminal using natural language to adjust or finalize their visit plans. The server receives user change requests as needed and dynamically updates the plan.

[0497] Furthermore, once the plan is finalized, the server presents the user with instructions for booking and purchasing. This allows the user to smoothly proceed with travel preparations.

[0498] As a concrete example, an example of a prompt message to a generative AI model would be: "If the user wants a VR trip to Paris, generate a personalized VR experience that includes the Eiffel Tower and the Louvre Museum."

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The device acquires the user's past behavioral information and records from information-sharing services. Specifically, it collects travel history and social media posting data to the extent permitted by the user. This information is then sent to the server and is treated as input data. The output is the collection of that behavioral information.

[0502] Step 2:

[0503] The server analyzes the user behavior information it receives. Using natural language processing tools (e.g., spaCy and NLTK) and machine learning algorithms (e.g., TensorFlow and PyTorch), it converts this information into data and extracts the user's interests and preferences. The input is the behavior information collected in the previous stage, and the output is a profile of the analyzed user characteristics and interests.

[0504] Step 3:

[0505] The server generates a user-optimized travel plan based on the analysis results. This plan includes recommended sightseeing spots and activities. The input is the user's characteristic profile, and the output is a personalized travel plan for that user.

[0506] Step 4:

[0507] The server prepares to visually reproduce the generated travel plan in a virtual reality environment. Specifically, it uses a VR development environment such as Unity 3D to build a virtual simulation of the travel destination. The input is the travel plan, and the output is VR simulation data based on that plan.

[0508] Step 5:

[0509] Users experience a virtual reality environment through their device and confirm or adjust their visit plan by interacting with the server in natural language. Input is user feedback and adjustment requests, and output is the adjusted visit plan.

[0510] Step 6:

[0511] After the final visit plan is confirmed, the server prepares to provide the user with a route for booking and purchasing travel. Specifically, it generates links and ticket purchase options and displays them on the user's device. The input is the confirmed visit plan, and the output is information such as booking links necessary for arrangements.

[0512] 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.

[0513] This invention is a travel planning system that combines a user's travel history and posts on communication services with an emotion engine that recognizes the user's emotions. Its purpose is to provide the most suitable travel plan while considering the user's emotions.

[0514] The system begins with the user accessing it through an application or web portal. The user grants the system access to travel history and communication service data, thereby making all the necessary data for sentiment analysis available.

[0515] The server collects users' travel history and posts on communication services and analyzes them using a proprietary algorithm. The collected data is then used with natural language processing and machine learning techniques to provide insights into users' behavioral patterns and interests.

[0516] Furthermore, the server uses an emotion engine to recognize the user's emotional state based on information extracted from communication service posts and user interactions. This allows for a more accurate identification of the mood and preferences the user desires for their travel plans. As an example of the results of emotion analysis, if the user is tired, a travel plan emphasizing relaxation will be provided, while if they express active emotions, a plan including active activities will be suggested.

[0517] The analysis results are used to generate a travel plan that includes the optimal destination, accommodation, and sightseeing activities for the user. This plan is presented to the user via their device, and the user can provide feedback through the interface regarding more specific requests or changes.

[0518] The server leverages feedback to dynamically update and adjust travel plans. It then finalizes the travel plan and simultaneously provides users with necessary booking and purchase links via their device. In this way, the system, combined with an emotion engine, delivers a personalized travel experience based on the user's emotions and needs.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] Users access the application or web portal, enter their profile information, and register. This includes granting permission to access their travel history and posts on communication services, after which the system is ready to be used.

[0522] Step 2:

[0523] The server securely collects users' travel history and posts from communication services using APIs and data import tools. The data is then organized into an analyzable format.

[0524] Step 3:

[0525] The server processes the collected dataset using a natural language processing algorithm to analyze the user's past interests and preferences. The results are stored in a database as a profile.

[0526] Step 4:

[0527] The server uses an emotion engine to determine a user's emotions from their posts and interactions on the communication service. This involves analyzing the tone of the text and keyword patterns to assess their emotions.

[0528] Step 5:

[0529] The server generates a travel plan best suited to the user based on analyzed preference and sentiment data. This plan includes destination selection, activity suggestions, and accommodation arrangements.

[0530] Step 6:

[0531] The device presents the generated travel plan through a user interface. The user can review the provided plan, provide feedback in natural language, or specify further requests.

[0532] Step 7:

[0533] Based on user feedback, the server dynamically readjusts the travel plan, taking into account new emotional states and incorporating specific requests.

[0534] Step 8:

[0535] The user confirms the travel plan they deem optimal. Once the confirmation instruction is sent from the device to the server, the server generates and provides a link for booking or purchase. This ensures a smooth travel booking process.

[0536] (Example 2)

[0537] 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."

[0538] Conventional travel planning systems have the drawback of being unable to adequately consider users' emotions and dynamic needs, resulting in the provision of only uniform travel plans. Therefore, there is a need to provide appropriate and flexible travel plans based on each user's individual emotional state and preferences.

[0539] 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.

[0540] In this invention, the server includes means for collecting user data, means for analyzing the collected data using natural language processing and learning algorithms to recognize the user's emotional state, and means for generating an optimal travel plan based on the user's emotional state and behavioral history. This makes it possible to provide personalized travel plans that respond to the user's dynamic requests.

[0541] "User data" refers to information related to a user, such as their travel history or posts on communication services.

[0542] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0543] A "learning algorithm" refers to a technique that learns patterns and rules from given data and uses that knowledge to make predictions and classifications on new data.

[0544] "Emotional state" refers to the user's psychological state and emotional tendencies.

[0545] A "travel plan" is a suggestion that includes the optimal destination and activities for the user's trip, and is based on the user's individual needs and feelings.

[0546] This invention is a system for individually optimizing a user's travel experience and is implemented in the following specific way.

[0547] Users access the system via an application or web portal. Users input data into the system by granting permission for access to data collected from their travel history and communication services. This data is stored in a cloud-based database and used for subsequent analysis.

[0548] The server collects user-authorized travel history and posts on communication services. This data collection utilizes API-based data retrieval and database access technologies. The specific software used is the Python programming language and its related libraries, enabling effective data aggregation from various platforms.

[0549] The collected data is analyzed using natural language processing (NLP) and learning algorithms. NLP techniques are implemented using the Google Cloud Natural Language API and open-source natural language processing libraries. This process extracts sentiment scores and areas of interest from user posts to recognize the user's emotional state.

[0550] Subsequently, the server generates an optimal travel plan based on the user's emotional state and past behavioral history. A generative AI model is used for plan generation. This model utilizes prompt statements to determine appropriate destinations and activities from multiple options. The following prompt statements are used as specific examples:

[0551] The user's recent emotion was identified as "needs relaxation." Please suggest a good hot spring resort.

[0552] The terminal presents the generated travel plan to the user and provides an interface for the user to provide feedback on the plan. Based on the user's feedback, the system dynamically updates the plan and provides the user with the final plan and booking link.

[0553] In this way, the system personalizes travel plans according to the user's emotions and needs, and provides support for appropriate booking and purchase.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] Users access the system via an application or web portal and grant permission to access travel history and communication service data. Inputs are user authentication information and access permission data, while output is a state ready for data collection. Specifically, users authenticate on a login screen and confirm access to relevant data within the application.

[0557] Step 2:

[0558] The server begins collecting data with the user's permission. The input is the user's travel and communication history data, and the output is the collected dataset. This stage involves specific actions such as interacting with an external communication service platform via an API to retrieve past travel history from the database.

[0559] Step 3:

[0560] The server uses the collected data to execute natural language processing and learning algorithms. The input is the collected dataset, and the output is an analysis result indicating the user's emotional state and interests. Specifically, it extracts emotional keywords from the dataset using NLP techniques, and then uses an emotion engine to evaluate the user's emotional state.

[0561] Step 4:

[0562] The server generates a travel plan based on the analysis results. The input is the user's emotional state and behavioral history, and analyzed interests. The output is a plan that includes a suitable travel destination, accommodation, and activities for the user. Here, a generative AI model is used to create prompts that recommend appropriate travel destinations and activities and to construct the plan.

[0563] Step 5:

[0564] The terminal presents the generated travel plan to the user. The input is the travel plan from the server, and the output is the plan information displayed via the user interface. Specifically, the user can review the plan displayed on the screen and provide real-time feedback on the plan.

[0565] Step 6:

[0566] The server receives user feedback and dynamically updates the travel plan. The input is user feedback, and the output is the updated travel plan. Based on the feedback information, the plan content is adjusted, and if necessary, it is regenerated using a new AI model.

[0567] Step 7:

[0568] The system provides users with their finalized travel plans and booking links via their device. The input is the finalized travel plan, and the output is booking information and customizable links. Specifically, the confirmed travel plan is sent to the user via email or application notification, making it easy for them to make necessary arrangements.

[0569] (Application Example 2)

[0570] 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."

[0571] Modern consumers struggle to find suitable products and services amidst a vast amount of information. At the same time, they demand personalized recommendations tailored to their individual emotions and preferences. In particular, the lack of product recommendations based on consumers' real-time emotions and interests in physical stores can lead to decreased satisfaction with the shopping experience. There is a need for systems that address these challenges.

[0572] 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.

[0573] In this invention, the server includes means for collecting the user's travel history, posts on communication services, and related information; means for analyzing the user's interests, preferences, or travel patterns based on the collected information; and means for extracting the user's visual or auditory information, analyzing their emotions in real time, and recommending products. This enables personalized product recommendations and travel plan provision tailored to the user's emotional state.

[0574] A "user" is a consumer who uses the system to receive travel planning and product recommendations.

[0575] "Travel history" refers to records of destinations and accommodations that a user has visited in the past.

[0576] "Posts on communication services" refer to messages and comments that users publish on social media or other online platforms.

[0577] "Means of collecting information" refers to the technologies and processes used to obtain users' travel history and posts on communication services.

[0578] "Means of analysis" refers to technologies used to analyze users' interests and preferences based on collected information.

[0579] "Methods for generating travel plans" refers to the process of formulating a travel plan suitable for the user based on analysis results.

[0580] "Means of interacting, adjusting, or confirming in natural language" refers to functions that communicate with users using natural language to modify or confirm travel plans.

[0581] "Means for extracting visual or auditory information and analyzing emotions in real time" refers to technologies that identify emotions in real time from what a user sees or the sounds they make.

[0582] "A means of recommending products" is a process of suggesting appropriate products based on the user's emotional state.

[0583] This invention provides a system for user travel planning and product recommendations. The system consists of three components: the user, the server, and the terminal.

[0584] First, the user accesses the system via a device. This device, such as smart glasses or a smartphone, is worn or carried by the user and is equipped with a camera to capture visual information and a microphone to acquire audio information. The user provides permission for the system to begin processing data by sharing their travel history and posts on communication services.

[0585] Next, the server collects travel history and posting data from communication services provided by the user. Furthermore, the server is equipped with a sentiment analysis engine and analyzes this data using natural language processing APIs (e.g., Google Cloud NLP, Amazon Comprehend). The server leverages machine learning algorithms to identify the user's emotional state and interests.

[0586] Based on this analysis, the server generates an optimal travel plan for the user. Furthermore, it recommends appropriate products to the device in real time based on the user's emotional state. This personalizes the user's shopping experience and travel planning, making it more satisfying.

[0587] Furthermore, users can receive feedback from the server via their devices and submit their opinions on travel plans and product recommendations. The server uses this feedback to dynamically adjust the plans and recommendations.

[0588] As a concrete example, when a user is browsing products in a physical store, the server analyzes visual and audio data and detects an emotion such as "I want to relax." In this case, a suggestion such as "Here are some recommended aromatherapy oils" would be displayed on the device's screen.

[0589] An example of a prompt message might be, "The user is in a state where they want to relax emotionally. Please recommend the best product from the store for this state." This allows the user to intelligently select products and services that match their emotional state.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server collects travel history and communication service posting data that users have authorized. Input includes information about past travel destinations and social media posts provided by the user. This data is stored in cloud storage and processed into a structured format. The output generates the dataset necessary for analysis.

[0593] Step 2:

[0594] The server uses a generative AI model and natural language processing API to analyze the collected data and identify the user's emotional state and interests. The input is the dataset generated in step 1, which the sentiment analysis engine uses as its basis. Data processing involves tokenization of the text data and sentiment scoring, and the output generates a profile of the user's emotional state and interests.

[0595] Step 3:

[0596] The server generates optimal travel plans and product recommendations for the user based on the emotional state and interest profiles created in Step 2. Inputs include the user's emotional state, past interests, and travel history. A recommendation algorithm is used for data calculation to determine the best options. The output consists of specific travel plans and product lists.

[0597] Step 4:

[0598] The terminal displays travel plans and product recommendations sent from the server to the user. This allows the user to receive suggestions in real time. The input is the plan and recommendation list generated in step 3, and the output is the information displayed on the user's visual display. The terminal uses audio and visual information as its interface.

[0599] Step 5:

[0600] The user sends feedback to the server about the information provided through the terminal. The input is the user's feedback, which includes plan adjustments and new requests. The output is the user's new requests sent to the server.

[0601] Step 6:

[0602] The server receives user feedback and dynamically updates travel plans and product recommendations. The input is the feedback received in step 5. As an update, the plan generation algorithm is run again, and new plans and recommendation lists are generated in the output. This ensures that users always receive the best service based on their needs.

[0603] 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.

[0604] 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 those described above. 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 shown 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention is a system that proposes personalized travel plans to travelers. This system analyzes the user's travel history, posts on communication services, and the interests and preferences based on them to generate an optimal travel plan.

[0621] First, users access the system through an application or web interface. Users enter registration information to use the system and grant access to their travel history and communication service data. This allows the system to collect data about the user's interests and preferences.

[0622] Next, the server analyzes the collected data, including the user's travel history and posts on communication services. Natural language processing and machine learning algorithms are used to analyze this data. This reveals past travel trends and topics of interest.

[0623] Based on the collected data, the server generates a travel plan tailored to the user's characteristics. This plan includes suggestions for destinations, recommendations for tourist spots, potential accommodations, and budget estimates. This plan is customized to the user's individual needs and constraints.

[0624] The generated travel plan is presented to the user via their device. The user can interact with the system using natural language to view details of the travel plan and request changes. Based on user feedback, the server dynamically adjusts the plan and re-presents the updated plan.

[0625] Finally, once the user confirms their travel plan, the server provides links for booking the trip and purchasing tickets. This allows the user to quickly and efficiently prepare for their trip.

[0626] For example, if a user has previously visited historical European cities and made many posts about art galleries and museums on communication services, the server can then suggest a route that visits new historical European cities and major art galleries based on that information. In this way, the present invention can significantly improve the user experience by providing travel plans that are more closely tailored to the user's preferences.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] Users access the system through an application or web interface and create an account. During this process, they configure settings to allow access to their travel history and posted data on communication services.

[0630] Step 2:

[0631] The server collects data, including travel history and posts on communication services, based on user permission. This data is obtained in a privacy-conscious manner through APIs or data import tools.

[0632] Step 3:

[0633] The server analyzes the collected data using natural language processing and machine learning algorithms. This identifies the user's past interests and travel patterns, and generates this information as a digital profile.

[0634] Step 4:

[0635] The server generates a travel plan, including suitable destinations, tourist attractions, and accommodations, based on the analysis results. This plan is customized according to the user's characteristics.

[0636] Step 5:

[0637] The device presents the generated travel plan to the user via an interactive interface. The user can provide feedback through the interface regarding specific preferences and constraints.

[0638] Step 6:

[0639] Users interact with the system using natural language to request confirmation and modification of plan details. The server dynamically updates the plan based on this feedback.

[0640] Step 7:

[0641] The user finalizes their travel plan, and the server then generates and provides links for booking the trip and purchasing tickets.

[0642] Step 8:

[0643] Users use the provided links to make necessary reservations and purchases and prepare for their trip. The device displays the confirmed travel itinerary and required information to support the user experience.

[0644] (Example 1)

[0645] 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".

[0646] Conventional travel plan creation systems were unable to fully utilize users' past travel history or information posted on electronic communication services, making it difficult to automatically suggest optimal travel plans based on individual user preferences and actual travel trends. As a result, travelers had to manually research and adjust their plans each time, which was time-consuming and laborious.

[0647] 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.

[0648] In this invention, the server includes means for collecting the user's past travel history, information transmitted on electronic communication services and related information, means for analyzing the user's preferences, tastes, or travel tendencies, and means for creating a travel plan optimized for the user using a generative artificial intelligence model. This makes it possible for the user to quickly and easily obtain an optimal travel plan that reflects their preferences and past tendencies without having to conduct detailed research.

[0649] A "user" is an individual or group that uses this system to plan travel, provides information to the system, and returns feedback.

[0650] "Travel history" refers to records of geographical locations a user has visited in the past and related activities, and is data used to analyze travel patterns.

[0651] "Electronic communication services" refer to platforms that allow users to transmit and share information on the internet, and include posted data and communication history.

[0652] "Transmitted information" refers to digital content such as text, images, and videos that users publish or share on electronic communication services.

[0653] "Preferences" refer to a user's interests, concerns, and tastes, and in particular, they indicate tendencies regarding the regions they want to visit and the activities they want to experience when traveling.

[0654] An "artificial intelligence model" is a computer program that learns from data, analyzes the user's past trends to make predictions, and generates plans and suggestions that are suitable for the user.

[0655] "Two-way communication" is a process in which a user and a system communicate with each other by sending and receiving information through natural language.

[0656] A "travel plan" is a comprehensive travel plan that includes the itinerary and destinations, tailored to the user's specific purposes and preferences.

[0657] In an embodiment for carrying out this invention, the system includes the following elements:

[0658] First, users establish access to the system using an application or web interface. Users grant the system permission to access their past travel history and outgoing information from electronic communication services. This information constitutes a dataset necessary to understand the user's preferences and travel tastes.

[0659] Next, the server collects this data and performs the actual analysis. For the analysis, spaCy is used as the natural language processing library, and TensorFlow is used as the platform for machine learning algorithms. This identifies patterns of preferences based on the user's past behavior. For example, it extracts cities that the user frequently visits and activities that interest them.

[0660] Based on the analysis results, the server utilizes a generative artificial intelligence model to generate a travel plan optimized for the user. This plan includes potential destinations, accommodations, and budget predictions, and is customized according to the user's individual needs. For example, if a user is interested in medieval history, the system might suggest a plan to tour historical cities in Europe.

[0661] The generated travel plan is presented to the user via the terminal. The user can interact with this plan using natural language on the interface, reviewing details and requesting modifications as needed. An example of a prompt might be, "Create a travel plan that includes visits to historical cities and museums, based on the user's past travel history and communication service posts."

[0662] This process allows users to efficiently obtain travel plans tailored to their preferences, resulting in a fast-paced and personalized travel experience.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] Users first access the system through an application or web interface. As input, they provide permission to access their past travel history and outgoing information on electronic communication services. This information allows the system to build a user profile and collect data. As output, they obtain a state where they have been granted access rights for data collection.

[0666] Step 2:

[0667] The server collects data received from users and stores it in a database. The inputs here are the user's movement history and outgoing information. The data stored in the database is analyzed using a natural language processing library (e.g., spaCy) to tokenize text data and extract key phrases. The output is an analysis result indicating the user's preferences and tastes.

[0668] Step 3:

[0669] Based on the analysis results from Step 2, the server creates a generative AI model using a machine learning platform (e.g., TensorFlow) to generate a travel plan optimized for the user's preferences. The input includes the analyzed user's interests and past behavior data. Based on this data, it lists potential destinations and accommodations. The output is a customized travel plan.

[0670] Step 4:

[0671] The terminal displays the travel plan received from the server to the user. The input here is a pre-generated travel plan. The user can review the displayed plan and provide feedback to the system using natural language. The output includes the user's reviewed travel plan and any change requests.

[0672] Step 5:

[0673] The server dynamically adjusts the travel plan based on the user feedback received in step 4. The input is user feedback data. The generating AI model is reused to update the plan and generate a new plan. The output is an updated travel plan that reflects the user's requests.

[0674] Step 6:

[0675] Once the user confirms their plan, the server provides links for booking or purchasing based on the finalized travel plan. The input is the confirmed travel plan. A link to a booking site is generated and provided to the user. The output presents the user with a booking URL or purchase link, enabling quick booking.

[0676] (Application Example 1)

[0677] 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".

[0678] Modern travelers seek travel experiences tailored to their individual interests and preferences within limited time and budget constraints, but traditional travel planning services struggle to adequately meet these needs. Furthermore, it's difficult for users to experience a travel plan in a virtual environment before actually traveling, leaving uncertainty about whether they will be satisfied with the plan.

[0679] 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.

[0680] In this invention, the server includes means for acquiring information on the user's past behavior, records on an information sharing service, and related information; means for analyzing the user's characteristics based on the acquired information; means for generating a visit plan optimized for the user based on the analysis results; means for interacting with the user in natural language and adjusting or finalizing the visit plan; and means for visually reproducing the visit plan in a virtual reality environment. This makes it possible for the user to virtually experience the travel plan before actually traveling, providing an optimal travel experience tailored to individual needs.

[0681] "User's past behavior information" refers to the user's past activity history, including travel history and information posted on communication services.

[0682] An "information sharing service" is an online platform used by users to exchange and share information with other people.

[0683] A "virtual reality environment" is an artificial environment created using computer technology, a virtual space in which users can have an experience similar to that of the real world.

[0684] A "travel plan" is a personalized travel plan that includes the places a traveler will visit and the activities they will experience within a specific period of time.

[0685] "Communicating in natural language" means interacting with a computer system and communicating using the language that humans normally use.

[0686] "Analyzing user characteristics" means analyzing and understanding user interests and preferences based on collected data.

[0687] "Visual representation" means using visual techniques to display or reproduce things in a way that resembles reality.

[0688] This invention relates to a system that provides a visit plan tailored to the individual needs of the user and allows the user to experience that plan in a virtual reality environment. Specific embodiments thereof are described below.

[0689] Users access this system using smart glasses or head-mounted displays. The device retrieves the user's past behavioral information and records from information-sharing services and sends them to the server. Based on the collected information, the server analyzes the user's characteristics using natural language processing and machine learning algorithms. This analysis allows for a detailed understanding of the user's interests and preferences.

[0690] Next, the server generates a visit plan optimized for the user based on the analysis results. This plan is tailored to the user's specific interests and past travel history and includes destination suggestions and simulations of tourist attractions.

[0691] The server converts the generated travel plan into a virtual reality environment and visually recreates it. This allows the user to experience the destination as if they were virtually visiting it and to review the details of the plan. For example, if a user has a travel plan to Paris, it can realistically provide a VR experience that includes the Eiffel Tower and the Louvre Museum.

[0692] Users can interact with the system via their terminal using natural language to adjust or finalize their visit plans. The server receives user change requests as needed and dynamically updates the plan.

[0693] Furthermore, once the plan is finalized, the server presents the user with instructions for booking and purchasing. This allows the user to smoothly proceed with travel preparations.

[0694] As a concrete example, an example of a prompt message to a generative AI model would be: "If the user wants a VR trip to Paris, generate a personalized VR experience that includes the Eiffel Tower and the Louvre Museum."

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The device acquires the user's past behavioral information and records from information-sharing services. Specifically, it collects travel history and social media posting data to the extent permitted by the user. This information is then sent to the server and is treated as input data. The output is the collection of that behavioral information.

[0698] Step 2:

[0699] The server analyzes the user behavior information it receives. Using natural language processing tools (e.g., spaCy and NLTK) and machine learning algorithms (e.g., TensorFlow and PyTorch), it converts this information into data and extracts the user's interests and preferences. The input is the behavior information collected in the previous stage, and the output is a profile of the analyzed user characteristics and interests.

[0700] Step 3:

[0701] The server generates a user-optimized travel plan based on the analysis results. This plan includes recommended sightseeing spots and activities. The input is the user's characteristic profile, and the output is a personalized travel plan for that user.

[0702] Step 4:

[0703] The server prepares to visually reproduce the generated travel plan in a virtual reality environment. Specifically, it uses a VR development environment such as Unity 3D to build a virtual simulation of the travel destination. The input is the travel plan, and the output is VR simulation data based on that plan.

[0704] Step 5:

[0705] Users experience a virtual reality environment through their device and confirm or adjust their visit plan by interacting with the server in natural language. Input is user feedback and adjustment requests, and output is the adjusted visit plan.

[0706] Step 6:

[0707] After the final visit plan is confirmed, the server prepares to provide the user with a route for booking and purchasing travel. Specifically, it generates links and ticket purchase options and displays them on the user's device. The input is the confirmed visit plan, and the output is information such as booking links necessary for arrangements.

[0708] 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.

[0709] This invention is a travel planning system that combines a user's travel history and posts on communication services with an emotion engine that recognizes the user's emotions. Its purpose is to provide the most suitable travel plan while considering the user's emotions.

[0710] The system begins with the user accessing it through an application or web portal. The user grants the system access to travel history and communication service data, thereby making all the necessary data for sentiment analysis available.

[0711] The server collects users' travel history and posts on communication services and analyzes them using a proprietary algorithm. The collected data is then used with natural language processing and machine learning techniques to provide insights into users' behavioral patterns and interests.

[0712] Furthermore, the server uses an emotion engine to recognize the user's emotional state based on information extracted from communication service posts and user interactions. This allows for a more accurate identification of the mood and preferences the user desires for their travel plans. As an example of the results of emotion analysis, if the user is tired, a travel plan emphasizing relaxation will be provided, while if they express active emotions, a plan including active activities will be suggested.

[0713] The analysis results are used to generate a travel plan that includes the optimal destination, accommodation, and sightseeing activities for the user. This plan is presented to the user via their device, and the user can provide feedback through the interface regarding more specific requests or changes.

[0714] The server leverages feedback to dynamically update and adjust travel plans. It then finalizes the travel plan and simultaneously provides users with necessary booking and purchase links via their device. In this way, the system, combined with an emotion engine, delivers a personalized travel experience based on the user's emotions and needs.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] Users access the application or web portal, enter their profile information, and register. This includes granting permission to access their travel history and posts on communication services, after which the system is ready to be used.

[0718] Step 2:

[0719] The server securely collects users' travel history and posts from communication services using APIs and data import tools. The data is then organized into an analyzable format.

[0720] Step 3:

[0721] The server processes the collected dataset using a natural language processing algorithm to analyze the user's past interests and preferences. The results are stored in a database as a profile.

[0722] Step 4:

[0723] The server uses an emotion engine to determine a user's emotions from their posts and interactions on the communication service. This involves analyzing the tone of the text and keyword patterns to assess their emotions.

[0724] Step 5:

[0725] The server generates a travel plan best suited to the user based on analyzed preference and sentiment data. This plan includes destination selection, activity suggestions, and accommodation arrangements.

[0726] Step 6:

[0727] The device presents the generated travel plan through a user interface. The user can review the provided plan, provide feedback in natural language, or specify further requests.

[0728] Step 7:

[0729] Based on user feedback, the server dynamically readjusts the travel plan, taking into account new emotional states and incorporating specific requests.

[0730] Step 8:

[0731] The user confirms the travel plan they deem optimal. Once the confirmation instruction is sent from the device to the server, the server generates and provides a link for booking or purchase. This ensures a smooth travel booking process.

[0732] (Example 2)

[0733] 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".

[0734] Conventional travel planning systems have the drawback of being unable to adequately consider users' emotions and dynamic needs, resulting in the provision of only uniform travel plans. Therefore, there is a need to provide appropriate and flexible travel plans based on each user's individual emotional state and preferences.

[0735] 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.

[0736] In this invention, the server includes means for collecting user data, means for analyzing the collected data using natural language processing and learning algorithms to recognize the user's emotional state, and means for generating an optimal travel plan based on the user's emotional state and behavioral history. This makes it possible to provide personalized travel plans that respond to the user's dynamic requests.

[0737] "User data" refers to information related to a user, such as their travel history or posts on communication services.

[0738] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0739] A "learning algorithm" refers to a technique that learns patterns and rules from given data and uses that knowledge to make predictions and classifications on new data.

[0740] "Emotional state" refers to the user's psychological state and emotional tendencies.

[0741] A "travel plan" is a suggestion that includes the optimal destination and activities for the user's trip, and is based on the user's individual needs and feelings.

[0742] This invention is a system for individually optimizing a user's travel experience and is implemented in the following specific way.

[0743] Users access the system via an application or web portal. Users input data into the system by granting permission for access to data collected from their travel history and communication services. This data is stored in a cloud-based database and used for subsequent analysis.

[0744] The server collects user-authorized travel history and posts on communication services. This data collection utilizes API-based data retrieval and database access technologies. The specific software used is the Python programming language and its related libraries, enabling effective data aggregation from various platforms.

[0745] The collected data is analyzed using natural language processing (NLP) and learning algorithms. NLP techniques are implemented using the Google Cloud Natural Language API and open-source natural language processing libraries. This process extracts sentiment scores and areas of interest from user posts to recognize the user's emotional state.

[0746] Subsequently, the server generates an optimal travel plan based on the user's emotional state and past behavioral history. A generative AI model is used for plan generation. This model utilizes prompt statements to determine appropriate destinations and activities from multiple options. The following prompt statements are used as specific examples:

[0747] The user's recent emotion was identified as "needs relaxation." Please suggest a good hot spring resort.

[0748] The terminal presents the generated travel plan to the user and provides an interface for the user to provide feedback on the plan. Based on the user's feedback, the system dynamically updates the plan and provides the user with the final plan and booking link.

[0749] In this way, the system personalizes travel plans according to the user's emotions and needs, and provides support for appropriate booking and purchase.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users access the system via an application or web portal and grant permission to access travel history and communication service data. Inputs are user authentication information and access permission data, while output is a state ready for data collection. Specifically, users authenticate on a login screen and confirm access to relevant data within the application.

[0753] Step 2:

[0754] The server begins collecting data with the user's permission. The input is the user's travel and communication history data, and the output is the collected dataset. This stage involves specific actions such as interacting with an external communication service platform via an API to retrieve past travel history from the database.

[0755] Step 3:

[0756] The server uses the collected data to execute natural language processing and learning algorithms. The input is the collected dataset, and the output is an analysis result indicating the user's emotional state and interests. Specifically, it extracts emotional keywords from the dataset using NLP techniques, and then uses an emotion engine to evaluate the user's emotional state.

[0757] Step 4:

[0758] The server generates a travel plan based on the analysis results. The input is the user's emotional state and behavioral history, and analyzed interests. The output is a plan that includes a suitable travel destination, accommodation, and activities for the user. Here, a generative AI model is used to create prompts that recommend appropriate travel destinations and activities and to construct the plan.

[0759] Step 5:

[0760] The terminal presents the generated travel plan to the user. The input is the travel plan from the server, and the output is the plan information displayed via the user interface. Specifically, the user can review the plan displayed on the screen and provide real-time feedback on the plan.

[0761] Step 6:

[0762] The server receives user feedback and dynamically updates the travel plan. The input is user feedback, and the output is the updated travel plan. Based on the feedback information, the plan content is adjusted, and if necessary, it is regenerated using a new AI model.

[0763] Step 7:

[0764] The system provides users with their finalized travel plans and booking links via their device. The input is the finalized travel plan, and the output is booking information and customizable links. Specifically, the confirmed travel plan is sent to the user via email or application notification, making it easy for them to make necessary arrangements.

[0765] (Application Example 2)

[0766] 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".

[0767] Modern consumers struggle to find suitable products and services amidst a vast amount of information. At the same time, they demand personalized recommendations tailored to their individual emotions and preferences. In particular, the lack of product recommendations based on consumers' real-time emotions and interests in physical stores can lead to decreased satisfaction with the shopping experience. There is a need for systems that address these challenges.

[0768] 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.

[0769] In this invention, the server includes means for collecting the user's travel history, posts on communication services, and related information; means for analyzing the user's interests, preferences, or travel patterns based on the collected information; and means for extracting the user's visual or auditory information, analyzing their emotions in real time, and recommending products. This enables personalized product recommendations and travel plan provision tailored to the user's emotional state.

[0770] A "user" is a consumer who uses the system to receive travel planning and product recommendations.

[0771] "Travel history" refers to records of destinations and accommodations that a user has visited in the past.

[0772] "Posts on communication services" refer to messages and comments that users publish on social media or other online platforms.

[0773] "Means of collecting information" refers to the technologies and processes used to obtain users' travel history and posts on communication services.

[0774] "Means of analysis" refers to technologies used to analyze users' interests and preferences based on collected information.

[0775] "Methods for generating travel plans" refers to the process of formulating a travel plan suitable for the user based on analysis results.

[0776] "Means of interacting, adjusting, or confirming in natural language" refers to functions that communicate with users using natural language to modify or confirm travel plans.

[0777] "Means for extracting visual or auditory information and analyzing emotions in real time" refers to technologies that identify emotions in real time from what a user sees or the sounds they make.

[0778] "A means of recommending products" is a process of suggesting appropriate products based on the user's emotional state.

[0779] This invention provides a system for user travel planning and product recommendations. The system consists of three components: the user, the server, and the terminal.

[0780] First, the user accesses the system via a device. This device, such as smart glasses or a smartphone, is worn or carried by the user and is equipped with a camera to capture visual information and a microphone to acquire audio information. The user provides permission for the system to begin processing data by sharing their travel history and posts on communication services.

[0781] Next, the server collects travel history and posting data from communication services provided by the user. Furthermore, the server is equipped with a sentiment analysis engine and analyzes this data using natural language processing APIs (e.g., Google Cloud NLP, Amazon Comprehend). The server leverages machine learning algorithms to identify the user's emotional state and interests.

[0782] Based on this analysis, the server generates an optimal travel plan for the user. Furthermore, it recommends appropriate products to the device in real time based on the user's emotional state. This personalizes the user's shopping experience and travel planning, making it more satisfying.

[0783] Furthermore, users can receive feedback from the server via their devices and submit their opinions on travel plans and product recommendations. The server uses this feedback to dynamically adjust the plans and recommendations.

[0784] As a concrete example, when a user is browsing products in a physical store, the server analyzes visual and audio data and detects an emotion such as "I want to relax." In this case, a suggestion such as "Here are some recommended aromatherapy oils" would be displayed on the device's screen.

[0785] An example of a prompt message might be, "The user is in a state where they want to relax emotionally. Please recommend the best product from the store for this state." This allows the user to intelligently select products and services that match their emotional state.

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The server collects travel history and communication service posting data that users have authorized. Input includes information about past travel destinations and social media posts provided by the user. This data is stored in cloud storage and processed into a structured format. The output generates the dataset necessary for analysis.

[0789] Step 2:

[0790] The server uses a generative AI model and natural language processing API to analyze the collected data and identify the user's emotional state and interests. The input is the dataset generated in step 1, which the sentiment analysis engine uses as its basis. Data processing involves tokenization of the text data and sentiment scoring, and the output generates a profile of the user's emotional state and interests.

[0791] Step 3:

[0792] The server generates optimal travel plans and product recommendations for the user based on the emotional state and interest profiles created in Step 2. Inputs include the user's emotional state, past interests, and travel history. A recommendation algorithm is used for data calculation to determine the best options. The output consists of specific travel plans and product lists.

[0793] Step 4:

[0794] The terminal displays travel plans and product recommendations sent from the server to the user. This allows the user to receive suggestions in real time. The input is the plan and recommendation list generated in step 3, and the output is the information displayed on the user's visual display. The terminal uses audio and visual information as its interface.

[0795] Step 5:

[0796] The user sends feedback to the server about the information provided through the terminal. The input is the user's feedback, which includes plan adjustments and new requests. The output is the user's new requests sent to the server.

[0797] Step 6:

[0798] The server receives user feedback and dynamically updates travel plans and product recommendations. The input is the feedback received in step 5. As an update, the plan generation algorithm is run again, and new plans and recommendation lists are generated in the output. This ensures that users always receive the best service based on their needs.

[0799] 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.

[0800] 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 those described above. 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 shown 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] Means for collecting user travel history, posts on communication services and related information,

[0823] A means for analyzing the user's interests, preferences, or travel patterns based on the collected information,

[0824] A means for generating the optimal travel plan for the user based on the analysis results,

[0825] A means for interacting with the user in natural language and adjusting or confirming the aforementioned travel plan,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which provides a link for booking or purchasing based on the generated travel plan.

[0829] (Claim 3)

[0830] The system according to claim 1, which dynamically updates the travel plan generated based on the user's feedback.

[0831] "Example 1"

[0832] (Claim 1)

[0833] A device that collects the user's past movement history, information transmitted on electronic communication services, and related information,

[0834] Based on the collected information, a device is used to analyze the user's preferences, tastes, or movement tendencies.

[0835] Based on the analysis results, a device that uses a generative artificial intelligence model to create a travel plan optimized for the user,

[0836] A device that communicates bidirectionally with the user using natural language and adjusts or confirms the aforementioned travel plan,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which provides connections for arrangement or acquisition based on the travel plan created.

[0840] (Claim 3)

[0841] The system according to claim 1, which dynamically modifies the generated travel plan based on the user's feedback.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] Means for obtaining information about a user's past behavior, records on information sharing services, and related information,

[0845] A means for analyzing user characteristics based on the acquired information,

[0846] A means for generating a visit plan optimized for the user based on the analysis results,

[0847] Means for interacting with the user in natural language and adjusting or confirming the aforementioned visit plan,

[0848] A means for visually reproducing the aforementioned visit plan in a virtual reality environment,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which presents a route for making a reservation or purchase based on the generated visit plan.

[0852] (Claim 3)

[0853] The system according to claim 1, which dynamically updates the visit plan generated based on the user's evaluation information.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] Means of collecting user data,

[0857] A means of recognizing a user's emotional state by analyzing data collected using natural language processing and learning algorithms,

[0858] A means for generating an optimal travel plan based on the user's emotional state and behavioral history,

[0859] A means of interacting with the user through a communication device and dynamically adjusting or confirming the travel plan,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, which generates a booking or purchase link based on a travel plan provided to the user.

[0863] (Claim 3)

[0864] The system according to claim 1, which dynamically updates a travel plan generated based on information from the user.

[0865] "Application example 2 when combining with an emotional engine"

[0866] (Claim 1)

[0867] Means for collecting user travel history, posts on communication services and related information,

[0868] A means for analyzing the user's interests, preferences, or travel patterns based on the collected information,

[0869] A means for generating the optimal travel plan for the user based on the analysis results,

[0870] A means for interacting with the user in natural language and adjusting or confirming the aforementioned travel plan,

[0871] A method for extracting the user's visual or auditory information, analyzing their emotions in real time, and recommending products,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which provides a link for booking or purchasing based on the aforementioned travel plan and product recommendations.

[0875] (Claim 3)

[0876] The system according to claim 1, which dynamically updates travel plans and product recommendations generated based on user feedback. [Explanation of Symbols]

[0877] 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. Means for collecting user travel history, posts on communication services and related information, A means for analyzing the user's interests, preferences, or travel patterns based on the collected information, A means for generating the optimal travel plan for the user based on the analysis results, A means for interacting with the user in natural language and adjusting or confirming the aforementioned travel plan, A system that includes this.

2. The system according to claim 1, which provides a link for booking or purchasing based on the generated travel plan.

3. The system according to claim 1, which dynamically updates the travel plan generated based on the user's feedback.

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