System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to enter input information regarding preferences and travel; natural language processing means for receiving and analyzing the input information; generative model means for generating a travel plan based on the analyzed information; means for arranging transportation and lodging in bulk based on the travel plan; and means for transmitting the travel plan and arrangement information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When planning a trip, many people struggle with the time and effort of planning it themselves, gathering information, and arranging transportation and accommodations. In these circumstances, there is a demand for services that allow users to easily plan trips efficiently and with high satisfaction, but current tools and services that meet this need are lacking. The present invention aims to solve these problems and provide a system that allows users to efficiently plan their ideal trip. [Means for solving the problem]

[0005] The present invention solves the aforementioned problems using the following means. The system includes a means for a user to input information about preferences and travel, a natural language processing means for receiving and analyzing the input information, a generative model means for generating a travel plan based on the analyzed information, a means for arranging transportation and accommodations in one go based on the travel plan, and a means for transmitting the travel plan and arrangement information to the user. Furthermore, the generative AI model includes a means for generating an optimal travel plan based on information learned from multiple databases related to the user's preferences and travel, thereby realizing a highly personalized travel plan tailored to the user's preferences. Furthermore, the system includes a means for the user to confirm and modify the travel plan, thereby improving final satisfaction.

[0006] "User" refers to an end user who uses this system to make travel plans.

[0007] "Preferences" refers to the user's personal tastes and interests, and the criteria used to determine travel destinations and activities.

[0008] "Travel input information" refers to all information entered by the user, such as travel destination, budget, itinerary, and activities of interest.

[0009] "Natural language processing means" refers to technology that analyzes input text data and understands the user's preferences and requests.

[0010] "Generative model means" refers to an AI model that automatically generates optimal travel plans based on the results of natural language processing.

[0011] "Transportation" refers to the means of transportation used for travel (e.g., train, plane, bus, etc.).

[0012] "Accommodation" refers to facilities where you stay during your trip (e.g., hotels, inns, guesthouses, etc.).

[0013] "Means of making arrangements" refers to the function of making reservations for transportation and accommodations all at once based on the generated travel plan.

[0014] "Transmitting means" refers to the technology used to provide planned travel details and arrangements to users electronically.

[0015] "Multiple databases" refers to multiple sources of information that store travel-related information and that are used by the generative model means to learn.

[0016] "Means for reviewing and modifying" refers to an interface that allows the user to review the generated itinerary and modify it as necessary. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0039] System configuration

[0040] 1. A means for users to enter preference and travel input information

[0041] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[0042] 2. Natural language processing means for receiving and analyzing the input information

[0043] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[0044] 3. A generation model means for generating a travel plan based on the analysis information.

[0045] Based on the analyzed information, the server uses a generative AI model to create an optimal travel plan. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences.

[0046] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0047] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[0048] 5. Means for transmitting said travel planning and arrangement information to a user

[0049] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[0050] Program processing (natural language explanation)

[0051] 1. Receiving input information

[0052] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[0053] 2. Natural Language Processing

[0054] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[0055] 3. Creating proposals using generative AI models

[0056] The server inputs the analyzed information into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[0057] 4. Execution of arrangements

[0058] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[0059] 5. Providing Information to Users

[0060] The server sends the completed arrangement information and the entire travel plan to the user, who can then check and finalize the information on their own device.

[0061] Specific examples

[0062] For example, suppose a user wants to refresh themselves in a place rich in nature on a three-day holiday. The user opens the application and enters, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0063] The device sends the input information to the server, which uses natural language processing technology to extract keywords such as "nature," "hiking," "hot springs," and "50,000 yen." The generative AI model then suggests optimal travel destinations based on these keywords. In this example, Hakuba Village in Nagano Prefecture is identified as the best fit, and offers include round-trip Shinkansen travel, hiking trails, and accommodations at Hakuba Onsen.

[0064] The server sends these proposals to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user a confirmation that the arrangements have been completed, allowing the user to make a comprehensive travel plan without any hassle.

[0065] This allows users to travel efficiently and with a high level of satisfaction, increasing the convenience of the system.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] A user opens an application or website and enters basic travel information (e.g., desired travel destinations, travel duration, budget, and activities of interest).

[0069] Step 2:

[0070] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[0071] Step 3:

[0072] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[0073] Step 4:

[0074] The server analyzes the received data using natural language processing techniques, such as tokenizing the text, tagging parts of speech, and recognizing entities, to extract user preferences and requirements.

[0075] Step 5:

[0076] The server inputs the analyzed information into a generative AI model, which then generates the optimal travel plan for the user based on data learned from multiple databases, including selecting travel destinations, suggesting activities, selecting transportation options, and suggesting accommodations.

[0077] Step 6:

[0078] The server checks the generated itinerary to see if it is consistent as a whole and meets constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[0079] Step 7:

[0080] The server sends the confirmed travel plan to the terminal, typically using an HTTP response.

[0081] Step 8:

[0082] The terminal displays the itinerary received from the server on the user interface, allowing the user to check the displayed itinerary and make any necessary corrections.

[0083] Step 9:

[0084] If the user is satisfied with the travel plan, he or she sends a "confirm reservation" instruction from the terminal.

[0085] Step 10:

[0086] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[0087] Step 11:

[0088] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[0089] Step 12:

[0090] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[0091] Through the above processing steps, the user can efficiently and easily create and realize a comprehensive travel plan.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] In conventional travel planning systems, even if users input their preferences and travel information, the corresponding plans and reservation arrangements are often not made quickly and appropriately. Furthermore, few systems allow for bulk arrangements, requiring users to make separate reservations for each mode of transportation and accommodation, which is time-consuming. Furthermore, it is difficult to flexibly adjust plans according to users' preferences and requests.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes: a means for a user to input information about preferences and travel; a natural language processing means for receiving and analyzing the input information; a generative model means for generating a travel plan based on the analyzed information; a means for arranging transportation and accommodations in a single transaction based on the travel plan; a means for transmitting the travel plan and arrangement information to the user; a means for exchanging the arrangement information with an external reservation system through API integration; a means for the generative AI model to generate a travel plan based on prompt text; and a means for the natural language processing technology to extract keywords and entities. This allows for the rapid and appropriate generation of an optimal travel plan based on the user's input preferences and requests, enabling the system to make all-in-one arrangements. Furthermore, the system allows users to easily confirm and revise the plan, enabling flexible travel schedule planning.

[0097] "Preferences" is information that indicates a user's personal tastes and interests.

[0098] A "travel plan" is a proposal that includes travel itineraries, transportation, accommodation, and activity details generated based on the user's preferences and requirements.

[0099] "Natural language processing" is a technology that analyzes text information entered by a user and extracts meaning, keywords, and entities.

[0100] A "generative model" is an AI algorithm that automatically generates appropriate travel plans based on user input information.

[0101] "Arrangements" refers to making reservations for transportation, accommodation, etc. based on travel plans.

[0102] "External Booking System" means an external online booking platform for making reservations for transportation or accommodation.

[0103] "API integration" is an interface that allows the server and external reservation systems to communicate with each other.

[0104] A "prompt sentence" is an instruction sentence input to a generative AI model to generate a plan.

[0105] "Keywords" are important words that indicate the user's requirements and preferences, and are extracted using natural language processing.

[0106] An "entity" is a word or phrase that has a particular meaning or attribute in natural language processing.

[0107] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0108] The system is implemented using the following hardware and software.

[0109] Hardware and software used

[0110] 1. Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[0111] 2. Server: A central server for data analysis and plan generation.

[0112] 3. Natural language processing tools: Tools for analyzing text data, such as Python's NLTK and spaCy.

[0113] 4. Generative AI model: An AI algorithm for automatically generating travel plans, such as GPT-4.

[0114] 5. External booking system: An online platform for booking transportation and accommodation.

[0115] 6. API integration: An interface for communication between the server and external reservation systems.

[0116] Overall system picture

[0117] 1. User Input

[0118] The user opens a dedicated application or website and enters details about their preferences and travel information. For example, they might enter, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0119] The terminal sends this input information to the server.

[0120] 2. Natural Language Processing

[0121] The server analyzes the information received from the terminal using natural language processing tools (NLTK, spaCy) and extracts keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen."

[0122] 3. Creating a travel plan using a generative AI model

[0123] The server creates a prompt sentence based on the analyzed information and inputs it into the generative AI model (GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday."

[0124] Based on this prompt, the generative AI model generates an optimal travel plan that includes destinations, activities, transportation, and accommodations. For example, a plan suggesting Hakuba Village in Nagano Prefecture includes a round-trip bullet train ride, hiking trails, and accommodations at Hakuba Onsen.

[0125] 4. Making travel arrangements

[0126] The server then arranges transportation and accommodation based on the generated travel plan. Through API integration with an external reservation system, Shinkansen tickets and accommodation reservations are automatically made.

[0127] 5. Providing information to users

[0128] The server sends the completed arrangement information and the entire travel plan to the user's terminal.

[0129] Users can check the information on their own devices and modify the plan as needed.

[0130] Specific examples

[0131] For example, suppose a user wants to refresh themselves in a natural setting over a three-day holiday. The user opens the application, enters "I want to refresh myself in a natural setting. I like hiking and hot springs. My budget is 50,000 yen," and sends this information to the server. The server uses natural language processing technology to extract the keywords "nature," "hiking," "hot springs," and "50,000 yen," and based on this, inputs prompts into the generative AI model. The generative AI model generates a travel plan that includes Hakuba Village in Nagano Prefecture, and makes Shinkansen tickets and accommodation reservations based on this plan. Finally, the server sends a confirmation of the completed arrangements to the user, who then confirms and modifies the travel plan.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] Collecting user input

[0135] The user opens a dedicated application or website and enters details about their preferences and travel information, such as "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0136] Input: User preferences and travel information

[0137] Output: Data containing user input information

[0138] Operation: The device sends the user's input information to the server.

[0139] Step 2:

[0140] Keyword extraction using natural language processing

[0141] The server analyzes the input information received from the device using natural language processing tools (such as NLTK or spaCy). As a result of the analysis, keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen" are extracted.

[0142] Input: User input information

[0143] Output: Extracted keywords and entities

[0144] How it works: The server uses natural language processing tools to parse the information and extract keywords and entities.

[0145] Step 3:

[0146] Generative AI model for travel planning

[0147] The server creates a prompt sentence based on the analyzed information and inputs it to the generative AI model (for example, GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday." The generative AI model generates a travel plan based on this prompt sentence.

[0148] Input: Extracted keywords and entities, prompt sentence

[0149] Output: Generated itinerary

[0150] How it works: The server inputs prompts into the generative AI model to generate an optimal travel plan.

[0151] Step 4:

[0152] Making travel arrangements

[0153] Based on the generated travel plan, the server automatically arranges transportation and accommodations through API integration with external reservation systems (e.g., online transportation reservation systems and accommodation reservation platforms).

[0154] Input: Travel Plan

[0155] Output: Reservation arrangement completion information

[0156] How it works: The server uses API integration to book and arrange transportation and accommodation.

[0157] Step 5:

[0158] Providing information to users

[0159] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check the information and modify the plan as necessary.

[0160] Input: Completed reservation information, overall travel plan

[0161] Output: User confirmation and correction results

[0162] How it works: The server sends information to the user's device, and the user confirms and modifies the plan.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Conventional travel planning systems have difficulty making travel suggestions that match a user's preferences and budget, and they also lack the ability to provide personalized content tailored to individual requirements. This requires users to make detailed plans and arrangements themselves, which is time-consuming. The present invention aims to solve these problems and provide a system that provides users with optimal travel plans and personalized video content.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes a means for allowing a user to input information about preferences and travel, a natural language processing means for receiving and analyzing the input information, and a generative model means for generating a travel plan based on the analyzed information, thereby enabling automatic generation of an optimal travel plan for the user and provision of personalized video content.

[0168] The "means for the user to input information about preferences and travel" refers to a means for the user to input information such as his / her travel preferences, budget, travel period, and places he / she wants to visit through an interface.

[0169] The "natural language processing means for receiving and analyzing the input information" is a means for receiving information input by a user and analyzing the information using natural language processing technology.

[0170] The "generative model means for generating a travel plan based on the analyzed information" is a means that utilizes a generative AI model to generate an optimal travel plan that meets the user's requirements based on the analyzed information.

[0171] The "means for collectively arranging transportation and accommodation based on the travel plan" refers to a means for collectively arranging transportation and accommodation to be used by the user based on the generated travel plan.

[0172] The "means for creating personalized video content from the travel plan" refers to a means for creating personalized video content based on the generated travel plan, allowing the user to deepen their understanding of the travel destination and activities.

[0173] The "means for transmitting the travel plan and arrangement information to the user" refers to a means for transmitting the final generated travel plan and information on arranged transportation and accommodations to the user's device.

[0174] "Means for a generative AI model to generate optimal travel plans based on information learned from multiple databases related to user preferences and travel" refers to a means for a generative AI model to propose travel plans that best suit a user's preferences and requests based on the analytical results learned from past data and multiple information sources.

[0175] The "means for the user to confirm and modify the travel plan" refers to a means for the user to confirm the generated travel plan and modify it as necessary.

[0176] overview

[0177] This invention relates to a system that automatically generates optimal travel plans based on user preferences and travel information. The system uses natural language processing technology and generative AI models to create travel plans, arrange transportation and accommodations, and even generate personalized video content.

[0178] System configuration and processing

[0179] 1. A means for users to enter preference and travel input information

[0180] Using a dedicated application, users can input their preferences and travel information, such as activities of interest, travel duration, budget, and purpose, etc. This information is sent to the server via an interface installed on a smartphone or tablet.

[0181] 2. Natural language processing means for receiving and analyzing the input information

[0182] The server receives data entered by the user through the device. The received data is analyzed using natural language processing technology to understand the user's preferences and requests. This process uses Python and the Transformers library, and the analysis is performed using the GPT-2 model.

[0183] 3. A generation model means for generating a travel plan based on the analysis information.

[0184] Based on information analyzed using natural language processing, a generative AI model generates an optimal travel plan. The generative AI model suggests travel destinations, activities, transportation, and accommodations based on the user's preferences and requests. This generation uses a model trained on public data and past databases to make highly accurate suggestions.

[0185] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0186] The server arranges transportation (e.g., train, plane, bus) and accommodation (e.g., hotel, inn) based on the generated travel plan. This includes API integration with external reservation systems, allowing for automated bulk arrangements.

[0187] 5. Means for creating personalized video content from said travel plans

[0188] Based on the generated travel plan, the server generates personalized video content to help users understand their trip and enhance their experience. This video content includes introductions to travel destinations and footage of planned visits, allowing the user to visually convey the appeal of the trip.

[0189] 6. Means for transmitting said travel planning and arrangement information to the user

[0190] The completed travel plan and arrangement information is sent from the server to the user's terminal, where the user can review the information and make any necessary corrections.

[0191] Examples of concrete examples and prompts

[0192] For example, suppose a user is thinking about refreshing themselves in a natural setting on a three-day holiday and enters, "I like hiking and hot springs. My budget is 50,000 yen." Based on this information, the server extracts the keywords "nature," "hiking," "hot springs," and "50,000 yen," and uses a generative AI model (GPT-2) to suggest Hakuba Village in Nagano Prefecture and generate a travel plan including a round-trip Shinkansen train, hiking trails, and accommodations at Hakuba Hot Springs. Based on this plan, Shinkansen tickets and accommodations are booked, and an introductory video about Hakuba Village is generated and distributed to the user.

[0193] Example prompt sentence:

[0194] The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the best travel plan.

[0195] The system allows users to have an efficient and engaging travel experience without the hassle of detailed planning.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The user inputs information about their preferences and trip into the device. In this step, the user launches the application and inputs specific information such as the activities they are interested in (e.g., hiking, hot springs), their budget (e.g., 50,000 yen), and the duration of their trip (e.g., 3 days). The device temporarily stores the input information.

[0199] Step 2:

[0200] The terminal sends the information entered by the user to the server, where the terminal performs a format check and formats the data appropriately for natural language processing. The formatted data is then sent over the Internet to the server.

[0201] Step 3:

[0202] The server analyzes the received input information using natural language processing. Here, it uses Python's Natural Language Toolkit (NLTK) and Transformers library to extract keywords and entities (e.g., "hiking," "hot springs," "50,000 yen," etc.) from the input content and understands the user's preferences and requests. The analyzed information is passed on to the next step.

[0203] Step 4:

[0204] The server uses a generative AI model to generate a travel plan based on the analysis information. Specifically, a generative AI model such as GPT-2 is used to generate an optimal travel plan that matches the user's preferences and requirements. This generation process includes selecting a travel destination, suggesting activities, selecting transportation, and selecting accommodation. The prompt sentence used is, "The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the optimal travel plan."

[0205] Step 5:

[0206] Based on the generated travel plan, the server arranges transportation and accommodations in one go. Here, Shinkansen tickets and hotel reservations are automatically made using the API of a third-party reservation system. Reservation confirmation information is returned to the server and passed on to the next processing step.

[0207] Step 6:

[0208] The server creates personalized video content based on the generated itinerary. It collects footage and information about the places and activities to be visited in the generated itinerary and creates a video using editing software (e.g., Adobe Premiere Pro). The video is provided in a format that allows users to visually check the itinerary.

[0209] Step 7:

[0210] The server sends the completed travel plan, arrangement information, and personalized video content to the user's terminal, where the user can review the information and make any necessary corrections, thereby finalizing the travel plan.

[0211] Step 8:

[0212] After the user has finalized and revised their travel plans, the server confirms the final arrangements and reconfirms that all reservations have been made. At this step, the server sends the user a final trip confirmation and notifies them that their plans are complete.

[0213] This allows users to efficiently obtain optimal travel plans and personalized content based on their preferences and requirements.

[0214] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0215] This invention relates to a system that automatically generates optimal travel plans based on input of user preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust travel plans according to the user's emotional state.

[0216] System configuration

[0217] 1. A means for users to enter preference and travel input information

[0218] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[0219] 2. Natural language processing means for receiving and analyzing the input information

[0220] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[0221] 3. Emotion engine that recognizes emotions from user input

[0222] The emotion engine analyzes the user's emotions from the information they input and how they express it, and recognizes their emotional state, such as whether they are happy, tired, or stressed.

[0223] 4. A generation model means for generating a travel plan based on the analysis information

[0224] The server uses the analyzed information and the results of the emotion engine to create an optimal travel plan using a generative AI model. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences and emotions.

[0225] 5. A means of arranging transportation and accommodations in one go based on the travel plan.

[0226] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[0227] 6. Means for transmitting said travel planning and arrangement information to the user

[0228] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[0229] Program processing (natural language explanation)

[0230] 1. Receiving input information

[0231] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[0232] 2. Natural Language Processing

[0233] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[0234] 3. Emotional Recognition

[0235] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[0236] 4. Creating proposals using generative AI models

[0237] The server inputs the analyzed information and the results of the emotion engine into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[0238] 5. Execution of arrangements

[0239] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[0240] 6. Providing Information to Users

[0241] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check and finalize the information.

[0242] Specific examples

[0243] For example, suppose a user is very tired from work and is thinking about going on a three-day trip to relax. The user opens the application and enters, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen."

[0244] When the device sends the input information to the server, the server uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then recognizes the user's emotion, "I'm tired and want to relax." The generative AI model then suggests the optimal travel destination based on these keywords and emotions. In this example, Hakone, a tranquil hot spring resort, is identified as the optimal destination, and includes round-trip travel by Shinkansen and accommodation at a hot spring inn.

[0245] The server sends these suggestions to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user confirmation that the arrangements have been completed, allowing the user to easily create a fulfilling travel plan and refresh both their body and mind.

[0246] This allows users to travel efficiently and with high satisfaction, increasing the system's usability. By combining it with an emotion engine, it becomes possible to make more personalized suggestions than conventional travel planning systems, providing a travel experience that meets the user's unique needs.

[0247] The processing flow will be explained below.

[0248] Step 1:

[0249] A user opens an application or website and enters basic travel information and preferences (desired destinations, travel duration, budget, activities of interest, etc.).

[0250] Step 2:

[0251] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[0252] Step 3:

[0253] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[0254] Step 4:

[0255] The server analyzes the received data using natural language processing technology, dividing the text data into tokens, tagging parts of speech, and recognizing entities to extract user preferences and requirements.

[0256] Step 5:

[0257] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[0258] Step 6:

[0259] The server inputs the analyzed information and the results of the emotion engine into a generative AI model, which uses information learned from multiple databases to generate an optimal travel plan, including destination selection, activity suggestions, transportation options, and accommodation suggestions.

[0260] Step 7:

[0261] The server checks the generated itinerary to ensure overall consistency and compliance with constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[0262] Step 8:

[0263] The server sends the confirmed travel plan to the terminal using an HTTP response.

[0264] Step 9:

[0265] The terminal displays the travel plan received from the server on the user interface. The user can check the displayed travel plan and have the option to modify activities, accommodations, etc. as necessary.

[0266] Step 10:

[0267] If the user is satisfied with the travel plan, he / she presses the "confirm reservation" button to confirm the plan, and the terminal sends this instruction to the server.

[0268] Step 11:

[0269] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[0270] Step 12:

[0271] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[0272] Step 13:

[0273] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[0274] Example 2

[0275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0276] Conventional travel planning systems could propose travel plans based on the user's preferences and budget, but they did not take the user's emotional state into account, resulting in a lack of personalization. This made it difficult to provide a travel plan that was optimal for the user's current emotional and mental state, making it a challenge to improve user satisfaction.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0278] In this invention, the server includes means for a user to input information about preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions from the input information, generative model means for generating a travel plan based on the analysis information and emotion recognition results, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user, thereby making it possible to propose an optimal travel plan based on the user's preferences and emotional state.

[0279] "User" means an individual or organization that uses the system to make travel plans.

[0280] "Preferences" refers to personal preferences such as the user's preferred activities, environments, and conditions.

[0281] "Input Information" is a set of data that a user provides to the system, including preferences, travel wishes, travel purpose, budget, duration, etc.

[0282] A "terminal" is an electronic device used by a user to input information and communicate with a server, and includes smartphones, tablets, computers, etc.

[0283] A "server" is a computer system that receives, analyzes, and processes input information from a user.

[0284] "Natural language processing means" refers to the technology and process that analyzes information entered by a user and extracts keywords and entities.

[0285] "Emotional engine means" refers to techniques and processes for recognizing a user's emotional state from input information.

[0286] The "generative model means" is an artificial intelligence model for generating optimal travel plans based on natural language processing and emotion recognition results.

[0287] "Travel Plan" refers to the travel schedule and suggestions generated by the system based on the user's preferences and emotions.

[0288] "Transportation" refers to the means of transportation offered in a travel plan, including trains, planes, buses, etc.

[0289] "Accommodation" refers to accommodation facilities included in the travel plan, and includes hotels, inns, guest houses, etc.

[0290] "Consolidation" refers to the process by which the system makes a single booking of transportation and accommodation based on a travel plan.

[0291] "Transmission" refers to the processes and techniques for communicating the generated travel planning and arrangement information to the user.

[0292] This invention relates to a system that automatically generates optimal travel plans based on a user's preferences and feelings, and makes all-in-one arrangements for transportation and accommodations. This system is characterized by the fact that a user inputs information via a dedicated application or website, and a server analyzes the information to generate and arrange a travel plan, and then sends the results to the user.

[0293] 1. Receiving input information

[0294] A user uses a device (e.g., smartphone, tablet, computer, etc.) to input information about their preferences and travel plans. Specific information includes the purpose of the trip, activities of interest, budget, and travel duration. Suppose the user inputs, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." The device then sends this input information to the server. During this process, the device verifies the format and accuracy of the input information before sending it.

[0295] 2. Natural Language Processing Methods

[0296] The server receives input information from the device and analyzes it using a natural language processing library (e.g., SpaCy or NLTK). Through this analysis, it extracts keywords and entities (e.g., "relaxation," "hot springs," "50,000 yen," etc.) to understand the user's preferences and travel needs.

[0297] 3. Means of Emotion Recognition

[0298] An emotion engine installed in the server recognizes the user's emotions from text information. For example, if a user enters the expression "I'm busy, so I want to relax," the server determines that the user is feeling stressed. At this stage, an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) is used. Specifically, the server passes the text to the emotion analysis API and obtains an emotion score (e.g., joy, sadness, stress, etc.).

[0299] 4. Generative Modeling Methods

[0300] The server uses a generative AI model to generate an optimal travel plan based on the analyzed information and the results of the emotion engine. This generative AI model learns information from different databases (e.g., tourist destination database, accommodation database, etc.). For example, it uses Microsoft Azure OpenAI Service to suggest travel destinations, activities, transportation, and accommodations. Specifically, the server inputs the analysis results and emotion recognition results as prompts to the generative AI model and receives the proposed travel plan.

[0301] 5. Bulk ordering method

[0302] The server arranges transportation and accommodations based on the generated travel plan. To do this, it connects with the API of an external reservation system (e.g., a travel reservation site API) to process reservations for bullet train tickets and hot spring inns in one go. Specifically, the server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation is complete.

[0303] 6. Means of transmission

[0304] The server sends the completed arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Once the user has finally confirmed the travel plan, the server sends confirmation information again. Specifically, the server sends the generated travel plan and reservation results to the device as a single data package and displays them in a user-viewable UI.

[0305] Specific examples

[0306] For example, a user is very tired from work and wants to plan a three-day trip to relax. The user opens the application and enters, "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen." The device sends this information to the server, which uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then analyzes the information and recognizes the emotion, "The user is tired and wants to relax." The generative AI model in the server then suggests the best travel destination based on these keywords and emotions.

[0307] Prompt Sentence Examples

[0308] "We provide input information from a user who wants to plan a three-day relaxing trip. The user likes hot springs and has a budget of around 50,000 yen. The user is also stressed and wants to relax. Please generate the optimal travel plan."

[0309] This system proposes optimal travel plans based on the user's emotional state, resulting in a higher level of personalization than conventional travel planning systems and improving user satisfaction.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1: Receiving input information

[0312] The user uses the device to input information about the trip. Specific input information includes the purpose of the trip, activities of interest, budget, and duration of the trip. For example, the user might input, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." This input information is sent from the device to the server. The server receives the input information and passes it on to the next process. Based on the input, it verifies whether the data format is correct and whether the required information is included, and prepares for the next step.

[0313] Step 2: Natural Language Processing

[0314] The server analyzes the received input information using a natural language processing library (e.g., SpaCy or NLTK). Specifically, it divides the text into tokens and extracts keywords and entities. For example, it extracts keywords such as "relaxation," "hot springs," and "50,000 yen." Based on this analysis, it understands the user's preferences and travel requirements and generates data to be used in the next step. As an output, it generates a dictionary containing the analyzed keywords and entities.

[0315] Step 3: Recognize emotions

[0316] The emotion engine installed on the server recognizes the user's emotions from the received input information. Specifically, it uses an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) to analyze the input text and obtain the user's emotion score. For example, it recognizes that the user is feeling stressed from the expression "I'm busy, so I want to relax." The output is an emotion score such as joy, sadness, or stress.

[0317] Step 4: Generative AI model creates recommendations

[0318] The server generates an optimal travel plan using a generative AI model based on the results of natural language processing and emotion recognition. Specifically, the analysis results and emotion recognition results are input into the generative AI model as prompts, and a suggested travel plan is received. For example, the prompt might read, "The user provides input information that they would like to plan a three-day relaxing trip. The user likes hot springs and has a budget of approximately 50,000 yen. The user is also feeling stressed and wants to relax. Please generate the optimal travel plan." The output is an optimal travel plan that includes travel destinations, activities, transportation, and accommodations.

[0319] Step 5: Execute the arrangement

[0320] The server arranges transportation and accommodations based on the generated travel plan. Specifically, it connects with an external reservation system (e.g., a travel reservation site API) and sends reservation data to complete the arrangements. For example, it makes reservations for Shinkansen tickets or hot spring resorts. The server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation has been completed. As an output, it provides confirmation that the reservation has been completed.

[0321] Step 6: Providing Information to Users

[0322] The server sends the completed travel arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Specifically, the generated travel plan and reservation results are sent as a single data package and displayed in a user-viewable UI. When the user finally confirms the travel plan, the server sends confirmation information again. The confirmed travel plan and information on the completion of arrangements are provided as output.

[0323] Through the above processing steps, the user can efficiently create a travel plan that matches his or her preferences and emotional state.

[0324] (Application example 2)

[0325] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Conventional travel planning systems only require users to input their preferences and travel information, and do not propose or adjust plans that take into account the user's emotional state. This makes it difficult to provide optimal travel plans that match the user's emotions, resulting in low levels of satisfaction. Furthermore, there are no seamless systems that allow users to directly purchase proposed travel plans, forcing users to take the time and effort to make individual arrangements on separate platforms. There is a need for a system that solves these problems, proposes optimal travel plans based on the user's emotions, and enables direct purchases on online shopping platforms.

[0327] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0328] In this invention, the server includes means for a user to input information related to preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions, generative model means for generating a travel plan based on the analysis information and the results of the emotion engine, means for displaying the travel plan and recommended related products on an online shopping platform, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user. This makes it possible to propose and adjust an optimal travel plan that suits the user's emotions, and further enables the proposed travel plan to be seamlessly purchased through the online shopping platform.

[0329] A "means for user input of preference and travel input information" is a device or software that provides an interface for a user to input details about their interests and travel.

[0330] The "natural language processing means for receiving and analyzing the input information" is a system that has the technology and functions for analyzing text data sent by a user and extracting meaning.

[0331] "Emotion engine means for recognizing user's emotions" refers to a system and technology for analyzing and recognizing a user's emotional state from the information input by the user and the way in which it is expressed.

[0332] The "generative model means for generating a travel plan based on the analysis information and the results of the emotion engine" is a system that includes an artificial intelligence model and its operation for automatically generating an optimal travel plan based on the analyzed information and the user's emotional state.

[0333] "Means for displaying the travel plan and recommended related products on the online shopping platform" refers to technology and interfaces for displaying the generated travel plan and related products on an online shopping site or app in a manner that is visible to the user.

[0334] "Means for arranging transportation and accommodations in one go based on the travel plan" refers to a system and function for arranging reservations for the necessary transportation and accommodations in one go according to the generated travel plan.

[0335] The "means for transmitting the travel plan and arrangement information to the user" refers to communication means and technology for transmitting the generated travel plan and arrangement information based on it to the user's device.

[0336] This invention relates to a system that automatically generates an optimal travel plan based on input of a user's preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust the travel plan according to the user's emotional state. Specific embodiments are described below.

[0337] System configuration

[0338] 1. A means for users to enter preference and travel input information

[0339] Using a smartphone application, users input their preferences and travel-related information, including activities of interest (e.g., hiking, hot springs), travel duration, budget, and travel purpose.

[0340] The interface is built in React Native and the data is sent to the server in JSON format.

[0341] 2. Natural language processing means for receiving and analyzing the input information

[0342] The server receives the input information sent by the user, and the received data is analyzed using natural language processing techniques, such as SpaCy, to extract keywords and entities.

[0343] For example, user input such as "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen" is analyzed.

[0344] 3. Emotion engine means for recognizing user emotions

[0345] Based on the analysis of input information, the system uses the Google Cloud Natural Language API to recognize the user's emotions. For example, it analyzes and recognizes the user's emotion, such as "I'm tired and want to relax."

[0346] 4. A generative model means for generating a travel plan based on the analysis information and the results of the emotion engine.

[0347] The server generates an optimal travel plan based on the analyzed information and the results of the emotion engine using a generative AI model, which uses OpenAI's GPT.

[0348] Specific examples of prompts are as follows:

[0349] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[0350] Activities: Hot Springs

[0351] Duration: 3 days

[0352] Budget: 50,000 yen

[0353] Emotion: Relaxed

[0354] Please submit your proposal in the following format:

[0355] Travel destination: Hakone

[0356] Activities: Hot Springs

[0357] Transportation: Shinkansen

[0358] Accommodation: Hot spring inn

[0359] 5. Means for displaying said travel plans and recommended related products on a shopping platform

[0360] The generated travel plans and related products (travel packages, transportation tickets, accommodation, etc.) are displayed on a shopping platform. This interface is built using web technologies (e.g., HTML, CSS, JavaScript).

[0361] Users can make reservations and purchases directly from this screen.

[0362] 6. A means of arranging transportation and accommodations in one go based on the travel plan.

[0363] The server then makes all necessary travel arrangements (trains, planes, etc.) and accommodation reservations based on the generated travel plan. This arrangement is carried out through API integration with an external reservation system.

[0364] 7. Means for transmitting said travel planning and arrangement information to the user

[0365] Once completed, travel plans and arrangements are sent to the user's smartphone, where they can review the information and make any necessary corrections or final confirmations.

[0366] Specific processing examples

[0367] For example, suppose a user is feeling very stressed and wants to relax. The user enters "I would like to take a relaxing hot spring trip" into the application. The server receives this information and analyzes it using natural language processing means. After the emotion engine means recognizes the user's emotion as "tired," the generative AI model means generates an optimal travel plan. This plan suggests a hot spring inn in Hakone, a tranquil hot spring resort, and recommends the Shinkansen as a means of transportation. The information is displayed on an online shopping platform, and the user can make a reservation directly, completing travel arrangements easily and quickly.

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1:

[0370] Users input preferences and travel information into the platform.

[0371] Input: A user opens a smartphone application and enters preferences and travel information, such as "I'd like to take a relaxing trip to a hot spring."

[0372] Output: The input information is sent to the server in JSON format.

[0373] What it does: When a user enters information into an application's interface, the information is formatted on the front end and sent to the back end as an API request.

[0374] Step 2:

[0375] The server receives input information from the user and analyzes it using natural language processing means.

[0376] Input: Information entered by the user (e.g., Relax, Hot Springs, Budget: 50,000 yen, etc.).

[0377] Output: Keywords and entities are extracted as the analysis results.

[0378] Specific operation: The server uses a natural language processing library such as SpaCy to parse the received JSON data and extract keywords and entities.

[0379] Step 3:

[0380] The server recognizes the user's emotions using an emotion engine means.

[0381] Input: Keywords and entities extracted by natural language processing.

[0382] Output: User's emotional state (e.g. tired, wanting to relax, etc.).

[0383] Specific operation: The server uses the Google Cloud Natural Language API to recognize the user's emotion from the parsed input information and obtains the user's emotional state as a result.

[0384] Step 4:

[0385] The server generates a travel plan using a generative AI model means.

[0386] Input: Analysis information and sentiment engine results.

[0387] Output: Optimized travel plan (e.g. destinations, activities, transportation, accommodation, etc.).

[0388] What it does: The server uses OpenAI's GPT model to generate a travel plan using the following prompt:

[0389] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[0390] Activities: Hot Springs

[0391] Duration: 3 days

[0392] Budget: 50,000 yen

[0393] Emotion: Relaxed

[0394] Please submit your proposal in the following format:

[0395] Travel destination: Hakone

[0396] Activities: Hot Springs

[0397] Transportation: Shinkansen

[0398] Accommodation: Hot spring inn

[0399] Step 5:

[0400] The server displays the generated travel plan and related products on the shopping platform.

[0401] Input: Generated itinerary and related product recommendations.

[0402] Output: Display screen on the online shopping platform.

[0403] Specific operation: The server uses HTML, CSS, and JavaScript to display the generated travel plan and related products on the shopping site.

[0404] Step 6:

[0405] The user reviews and approves the generated itinerary.

[0406] Input: Travel itinerary displayed on a shopping platform.

[0407] Output: User approval or correction request.

[0408] What it does: The user clicks a button on the screen to approve the travel plan, and can request modifications if necessary.

[0409] Step 7:

[0410] Receive payment information and arrange transportation and accommodations all in one place.

[0411] Input: User's payment information and approved travel plans.

[0412] Output: Booking completion information and confirmation notice.

[0413] Specific operation: The server connects to the API of an external reservation system to arrange transportation and accommodation. Once the arrangements are complete, it generates reservation completion information and notifies the user.

[0414] Step 8:

[0415] The travel planning and arrangement information is transmitted to the user.

[0416] Input: Completed booking information and overall travel plan.

[0417] Output: Confirmation information sent to the user's device.

[0418] Specific operation: The server notifies the user's smartphone of the generated travel plan and reservation completion information, allowing the user to check the travel plan.

[0419] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0425] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0426] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0427] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0428] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0429] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0430] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0431] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0432] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0433] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0434] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0435] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0436] System configuration

[0437] 1. A means for users to enter preference and travel input information

[0438] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[0439] 2. Natural language processing means for receiving and analyzing the input information

[0440] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[0441] 3. A generation model means for generating a travel plan based on the analysis information.

[0442] Based on the analyzed information, the server uses a generative AI model to create an optimal travel plan. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences.

[0443] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0444] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[0445] 5. Means for transmitting said travel planning and arrangement information to a user

[0446] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[0447] Program processing (natural language explanation)

[0448] 1. Receiving input information

[0449] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[0450] 2. Natural Language Processing

[0451] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[0452] 3. Creating proposals using generative AI models

[0453] The server inputs the analyzed information into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[0454] 4. Execution of arrangements

[0455] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[0456] 5. Providing Information to Users

[0457] The server sends the completed arrangement information and the entire travel plan to the user, who can then check and finalize the information on their own device.

[0458] Specific examples

[0459] For example, suppose a user wants to refresh themselves in a place rich in nature on a three-day holiday. The user opens the application and enters, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0460] The device sends the input information to the server, which uses natural language processing technology to extract keywords such as "nature," "hiking," "hot springs," and "50,000 yen." The generative AI model then suggests optimal travel destinations based on these keywords. In this example, Hakuba Village in Nagano Prefecture is identified as the best fit, and offers include round-trip Shinkansen travel, hiking trails, and accommodations at Hakuba Onsen.

[0461] The server sends these proposals to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user a confirmation that the arrangements have been completed, allowing the user to make a comprehensive travel plan without any hassle.

[0462] This allows users to travel efficiently and with a high level of satisfaction, increasing the convenience of the system.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] A user opens an application or website and enters basic travel information (e.g., desired travel destinations, travel duration, budget, and activities of interest).

[0466] Step 2:

[0467] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[0468] Step 3:

[0469] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[0470] Step 4:

[0471] The server analyzes the received data using natural language processing techniques, such as tokenizing the text, tagging parts of speech, and recognizing entities, to extract user preferences and requirements.

[0472] Step 5:

[0473] The server inputs the analyzed information into a generative AI model, which then generates the optimal travel plan for the user based on data learned from multiple databases, including selecting travel destinations, suggesting activities, selecting transportation options, and suggesting accommodations.

[0474] Step 6:

[0475] The server checks the generated itinerary to see if it is consistent as a whole and meets constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[0476] Step 7:

[0477] The server sends the confirmed travel plan to the terminal, typically using an HTTP response.

[0478] Step 8:

[0479] The terminal displays the itinerary received from the server on the user interface, allowing the user to check the displayed itinerary and make any necessary corrections.

[0480] Step 9:

[0481] If the user is satisfied with the travel plan, he or she sends a "confirm reservation" instruction from the terminal.

[0482] Step 10:

[0483] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[0484] Step 11:

[0485] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[0486] Step 12:

[0487] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[0488] Through the above processing steps, the user can efficiently and easily create and realize a comprehensive travel plan.

[0489] Example 1

[0490] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0491] In conventional travel planning systems, even if users input their preferences and travel information, the corresponding plans and reservation arrangements are often not made quickly and appropriately. Furthermore, few systems allow for bulk arrangements, requiring users to make separate reservations for each mode of transportation and accommodation, which is time-consuming. Furthermore, it is difficult to flexibly adjust plans according to users' preferences and requests.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0493] In this invention, the server includes: a means for a user to input information about preferences and travel; a natural language processing means for receiving and analyzing the input information; a generative model means for generating a travel plan based on the analyzed information; a means for arranging transportation and accommodations in a single transaction based on the travel plan; a means for transmitting the travel plan and arrangement information to the user; a means for exchanging the arrangement information with an external reservation system through API integration; a means for the generative AI model to generate a travel plan based on prompt text; and a means for the natural language processing technology to extract keywords and entities. This allows for the rapid and appropriate generation of an optimal travel plan based on the user's input preferences and requests, enabling the system to make all-in-one arrangements. Furthermore, the system allows users to easily confirm and revise the plan, enabling flexible travel schedule planning.

[0494] "Preferences" is information that indicates a user's personal tastes and interests.

[0495] A "travel plan" is a proposal that includes travel itineraries, transportation, accommodation, and activity details generated based on the user's preferences and requirements.

[0496] "Natural language processing" is a technology that analyzes text information entered by a user and extracts meaning, keywords, and entities.

[0497] A "generative model" is an AI algorithm that automatically generates appropriate travel plans based on user input information.

[0498] "Arrangements" refers to making reservations for transportation, accommodation, etc. based on travel plans.

[0499] "External Booking System" means an external online booking platform for making reservations for transportation or accommodation.

[0500] "API integration" is an interface that allows the server and external reservation systems to communicate with each other.

[0501] A "prompt sentence" is an instruction sentence input to a generative AI model to generate a plan.

[0502] "Keywords" are important words that indicate the user's requirements and preferences, and are extracted using natural language processing.

[0503] An "entity" is a word or phrase that has a particular meaning or attribute in natural language processing.

[0504] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0505] The system is implemented using the following hardware and software.

[0506] Hardware and software used

[0507] 1. Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[0508] 2. Server: A central server for data analysis and plan generation.

[0509] 3. Natural language processing tools: Tools for analyzing text data, such as Python's NLTK and spaCy.

[0510] 4. Generative AI model: An AI algorithm for automatically generating travel plans, such as GPT-4.

[0511] 5. External booking system: An online platform for booking transportation and accommodation.

[0512] 6. API integration: An interface for communication between the server and external reservation systems.

[0513] Overall system picture

[0514] 1. User Input

[0515] The user opens a dedicated application or website and enters details about their preferences and travel information. For example, they might enter, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0516] The terminal sends this input information to the server.

[0517] 2. Natural Language Processing

[0518] The server analyzes the information received from the terminal using natural language processing tools (NLTK, spaCy) and extracts keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen."

[0519] 3. Creating a travel plan using a generative AI model

[0520] The server creates a prompt sentence based on the analyzed information and inputs it into the generative AI model (GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday."

[0521] Based on this prompt, the generative AI model generates an optimal travel plan that includes destinations, activities, transportation, and accommodations. For example, a plan suggesting Hakuba Village in Nagano Prefecture includes a round-trip bullet train ride, hiking trails, and accommodations at Hakuba Onsen.

[0522] 4. Making travel arrangements

[0523] The server then arranges transportation and accommodation based on the generated travel plan. Through API integration with an external reservation system, Shinkansen tickets and accommodation reservations are automatically made.

[0524] 5. Providing information to users

[0525] The server sends the completed arrangement information and the entire travel plan to the user's terminal.

[0526] Users can check the information on their own devices and modify the plan as needed.

[0527] Specific examples

[0528] For example, suppose a user wants to refresh themselves in a natural setting over a three-day holiday. The user opens the application, enters "I want to refresh myself in a natural setting. I like hiking and hot springs. My budget is 50,000 yen," and sends this information to the server. The server uses natural language processing technology to extract the keywords "nature," "hiking," "hot springs," and "50,000 yen," and based on this, inputs prompts into the generative AI model. The generative AI model generates a travel plan that includes Hakuba Village in Nagano Prefecture, and makes Shinkansen tickets and accommodation reservations based on this plan. Finally, the server sends a confirmation of the completed arrangements to the user, who then confirms and modifies the travel plan.

[0529] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0530] Step 1:

[0531] Collecting user input

[0532] The user opens a dedicated application or website and enters details about their preferences and travel information, such as "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0533] Input: User preferences and travel information

[0534] Output: Data containing user input information

[0535] Operation: The device sends the user's input information to the server.

[0536] Step 2:

[0537] Keyword extraction using natural language processing

[0538] The server analyzes the input information received from the device using natural language processing tools (such as NLTK or spaCy). As a result of the analysis, keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen" are extracted.

[0539] Input: User input information

[0540] Output: Extracted keywords and entities

[0541] How it works: The server uses natural language processing tools to parse the information and extract keywords and entities.

[0542] Step 3:

[0543] Generative AI model for travel planning

[0544] The server creates a prompt sentence based on the analyzed information and inputs it to the generative AI model (for example, GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday." The generative AI model generates a travel plan based on this prompt sentence.

[0545] Input: Extracted keywords and entities, prompt sentence

[0546] Output: Generated itinerary

[0547] How it works: The server inputs prompts into the generative AI model to generate an optimal travel plan.

[0548] Step 4:

[0549] Making travel arrangements

[0550] Based on the generated travel plan, the server automatically arranges transportation and accommodations through API integration with external reservation systems (e.g., online transportation reservation systems and accommodation reservation platforms).

[0551] Input: Travel Plan

[0552] Output: Reservation arrangement completion information

[0553] How it works: The server uses API integration to book and arrange transportation and accommodation.

[0554] Step 5:

[0555] Providing information to users

[0556] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check the information and modify the plan as necessary.

[0557] Input: Completed reservation information, overall travel plan

[0558] Output: User confirmation and correction results

[0559] How it works: The server sends information to the user's device, and the user confirms and modifies the plan.

[0560] (Application example 1)

[0561] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0562] Conventional travel planning systems have difficulty making travel suggestions that match a user's preferences and budget, and they also lack the ability to provide personalized content tailored to individual requirements. This requires users to make detailed plans and arrangements themselves, which is time-consuming. The present invention aims to solve these problems and provide a system that provides users with optimal travel plans and personalized video content.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0564] In this invention, the server includes a means for allowing a user to input information about preferences and travel, a natural language processing means for receiving and analyzing the input information, and a generative model means for generating a travel plan based on the analyzed information, thereby enabling automatic generation of an optimal travel plan for the user and provision of personalized video content.

[0565] The "means for the user to input information about preferences and travel" refers to a means for the user to input information such as his / her travel preferences, budget, travel period, and places he / she wants to visit through an interface.

[0566] The "natural language processing means for receiving and analyzing the input information" is a means for receiving information input by a user and analyzing the information using natural language processing technology.

[0567] The "generative model means for generating a travel plan based on the analyzed information" is a means that utilizes a generative AI model to generate an optimal travel plan that meets the user's requirements based on the analyzed information.

[0568] The "means for collectively arranging transportation and accommodation based on the travel plan" refers to a means for collectively arranging transportation and accommodation to be used by the user based on the generated travel plan.

[0569] The "means for creating personalized video content from the travel plan" refers to a means for creating personalized video content based on the generated travel plan, allowing the user to deepen their understanding of the travel destination and activities.

[0570] The "means for transmitting the travel plan and arrangement information to the user" refers to a means for transmitting the final generated travel plan and information on arranged transportation and accommodations to the user's device.

[0571] "Means for a generative AI model to generate optimal travel plans based on information learned from multiple databases related to user preferences and travel" refers to a means for a generative AI model to propose travel plans that best suit a user's preferences and requests based on the analytical results learned from past data and multiple information sources.

[0572] The "means for the user to confirm and modify the travel plan" refers to a means for the user to confirm the generated travel plan and modify it as necessary.

[0573] overview

[0574] This invention relates to a system that automatically generates optimal travel plans based on user preferences and travel information. The system uses natural language processing technology and generative AI models to create travel plans, arrange transportation and accommodations, and even generate personalized video content.

[0575] System configuration and processing

[0576] 1. A means for users to enter preference and travel input information

[0577] Using a dedicated application, users can input their preferences and travel information, such as activities of interest, travel duration, budget, and purpose, etc. This information is sent to the server via an interface installed on a smartphone or tablet.

[0578] 2. Natural language processing means for receiving and analyzing the input information

[0579] The server receives data entered by the user through the device. The received data is analyzed using natural language processing technology to understand the user's preferences and requests. This process uses Python and the Transformers library, and the analysis is performed using the GPT-2 model.

[0580] 3. A generation model means for generating a travel plan based on the analysis information.

[0581] Based on information analyzed using natural language processing, a generative AI model generates an optimal travel plan. The generative AI model suggests travel destinations, activities, transportation, and accommodations based on the user's preferences and requests. This generation uses a model trained on public data and past databases to make highly accurate suggestions.

[0582] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0583] The server arranges transportation (e.g., train, plane, bus) and accommodation (e.g., hotel, inn) based on the generated travel plan. This includes API integration with external reservation systems, allowing for automated bulk arrangements.

[0584] 5. Means for creating personalized video content from said travel plans

[0585] Based on the generated travel plan, the server generates personalized video content to help users understand their trip and enhance their experience. This video content includes introductions to travel destinations and footage of planned visits, allowing the user to visually convey the appeal of the trip.

[0586] 6. Means for transmitting said travel planning and arrangement information to the user

[0587] The completed travel plan and arrangement information is sent from the server to the user's terminal, where the user can review the information and make any necessary corrections.

[0588] Examples of concrete examples and prompts

[0589] For example, suppose a user is thinking about refreshing themselves in a natural setting on a three-day holiday and enters, "I like hiking and hot springs. My budget is 50,000 yen." Based on this information, the server extracts the keywords "nature," "hiking," "hot springs," and "50,000 yen," and uses a generative AI model (GPT-2) to suggest Hakuba Village in Nagano Prefecture and generate a travel plan including a round-trip Shinkansen train, hiking trails, and accommodations at Hakuba Hot Springs. Based on this plan, Shinkansen tickets and accommodations are booked, and an introductory video about Hakuba Village is generated and distributed to the user.

[0590] Example prompt sentence:

[0591] The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the best travel plan.

[0592] The system allows users to have an efficient and engaging travel experience without the hassle of detailed planning.

[0593] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0594] Step 1:

[0595] The user inputs information about their preferences and trip into the device. In this step, the user launches the application and inputs specific information such as the activities they are interested in (e.g., hiking, hot springs), their budget (e.g., 50,000 yen), and the duration of their trip (e.g., 3 days). The device temporarily stores the input information.

[0596] Step 2:

[0597] The terminal sends the information entered by the user to the server, where the terminal performs a format check and formats the data appropriately for natural language processing. The formatted data is then sent over the Internet to the server.

[0598] Step 3:

[0599] The server analyzes the received input information using natural language processing. Here, it uses Python's Natural Language Toolkit (NLTK) and Transformers library to extract keywords and entities (e.g., "hiking," "hot springs," "50,000 yen," etc.) from the input content and understands the user's preferences and requests. The analyzed information is passed on to the next step.

[0600] Step 4:

[0601] The server uses a generative AI model to generate a travel plan based on the analysis information. Specifically, a generative AI model such as GPT-2 is used to generate an optimal travel plan that matches the user's preferences and requirements. This generation process includes selecting a travel destination, suggesting activities, selecting transportation, and selecting accommodation. The prompt sentence used is, "The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the optimal travel plan."

[0602] Step 5:

[0603] Based on the generated travel plan, the server arranges transportation and accommodations in one go. Here, Shinkansen tickets and hotel reservations are automatically made using the API of a third-party reservation system. Reservation confirmation information is returned to the server and passed on to the next processing step.

[0604] Step 6:

[0605] The server creates personalized video content based on the generated itinerary. It collects footage and information about the places and activities to be visited in the generated itinerary and creates a video using editing software (e.g., Adobe Premiere Pro). The video is provided in a format that allows users to visually check the itinerary.

[0606] Step 7:

[0607] The server sends the completed travel plan, arrangement information, and personalized video content to the user's terminal, where the user can review the information and make any necessary corrections, thereby finalizing the travel plan.

[0608] Step 8:

[0609] After the user has finalized and revised their travel plans, the server confirms the final arrangements and reconfirms that all reservations have been made. At this step, the server sends the user a final trip confirmation and notifies them that their plans are complete.

[0610] This allows users to efficiently obtain optimal travel plans and personalized content based on their preferences and requirements.

[0611] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0612] This invention relates to a system that automatically generates optimal travel plans based on input of user preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust travel plans according to the user's emotional state.

[0613] System configuration

[0614] 1. A means for users to enter preference and travel input information

[0615] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[0616] 2. Natural language processing means for receiving and analyzing the input information

[0617] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[0618] 3. Emotion engine that recognizes emotions from user input

[0619] The emotion engine analyzes the user's emotions from the information they input and how they express it, and recognizes their emotional state, such as whether they are happy, tired, or stressed.

[0620] 4. A generation model means for generating a travel plan based on the analysis information

[0621] The server uses the analyzed information and the results of the emotion engine to create an optimal travel plan using a generative AI model. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences and emotions.

[0622] 5. A means of arranging transportation and accommodations in one go based on the travel plan.

[0623] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[0624] 6. Means for transmitting said travel planning and arrangement information to the user

[0625] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[0626] Program processing (natural language explanation)

[0627] 1. Receiving input information

[0628] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[0629] 2. Natural Language Processing

[0630] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[0631] 3. Emotional Recognition

[0632] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[0633] 4. Creating proposals using generative AI models

[0634] The server inputs the analyzed information and the results of the emotion engine into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[0635] 5. Execution of arrangements

[0636] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[0637] 6. Providing Information to Users

[0638] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check and finalize the information.

[0639] Specific examples

[0640] For example, suppose a user is very tired from work and is thinking about going on a three-day trip to relax. The user opens the application and enters, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen."

[0641] When the device sends the input information to the server, the server uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then recognizes the user's emotion, "I'm tired and want to relax." The generative AI model then suggests the optimal travel destination based on these keywords and emotions. In this example, Hakone, a tranquil hot spring resort, is identified as the optimal destination, and includes round-trip travel by Shinkansen and accommodation at a hot spring inn.

[0642] The server sends these suggestions to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user confirmation that the arrangements have been completed, allowing the user to easily create a fulfilling travel plan and refresh both their body and mind.

[0643] This allows users to travel efficiently and with high satisfaction, increasing the system's usability. By combining it with an emotion engine, it becomes possible to make more personalized suggestions than conventional travel planning systems, providing a travel experience that meets the user's unique needs.

[0644] The processing flow will be explained below.

[0645] Step 1:

[0646] A user opens an application or website and enters basic travel information and preferences (desired destinations, travel duration, budget, activities of interest, etc.).

[0647] Step 2:

[0648] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[0649] Step 3:

[0650] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[0651] Step 4:

[0652] The server analyzes the received data using natural language processing technology, dividing the text data into tokens, tagging parts of speech, and recognizing entities to extract user preferences and requirements.

[0653] Step 5:

[0654] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[0655] Step 6:

[0656] The server inputs the analyzed information and the results of the emotion engine into a generative AI model, which uses information learned from multiple databases to generate an optimal travel plan, including destination selection, activity suggestions, transportation options, and accommodation suggestions.

[0657] Step 7:

[0658] The server checks the generated itinerary to ensure overall consistency and compliance with constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[0659] Step 8:

[0660] The server sends the confirmed travel plan to the terminal using an HTTP response.

[0661] Step 9:

[0662] The terminal displays the travel plan received from the server on the user interface. The user can check the displayed travel plan and have the option to modify activities, accommodations, etc. as necessary.

[0663] Step 10:

[0664] If the user is satisfied with the travel plan, he / she presses the "confirm reservation" button to confirm the plan, and the terminal sends this instruction to the server.

[0665] Step 11:

[0666] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[0667] Step 12:

[0668] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[0669] Step 13:

[0670] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[0671] Example 2

[0672] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0673] Conventional travel planning systems could propose travel plans based on the user's preferences and budget, but they did not take the user's emotional state into account, resulting in a lack of personalization. This made it difficult to provide a travel plan that was optimal for the user's current emotional and mental state, making it a challenge to improve user satisfaction.

[0674] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0675] In this invention, the server includes means for a user to input information about preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions from the input information, generative model means for generating a travel plan based on the analysis information and emotion recognition results, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user, thereby making it possible to propose an optimal travel plan based on the user's preferences and emotional state.

[0676] "User" means an individual or organization that uses the system to make travel plans.

[0677] "Preferences" refers to personal preferences such as the user's preferred activities, environments, and conditions.

[0678] "Input Information" is a set of data that a user provides to the system, including preferences, travel wishes, travel purpose, budget, duration, etc.

[0679] A "terminal" is an electronic device used by a user to input information and communicate with a server, and includes smartphones, tablets, computers, etc.

[0680] A "server" is a computer system that receives, analyzes, and processes input information from a user.

[0681] "Natural language processing means" refers to the technology and process that analyzes information entered by a user and extracts keywords and entities.

[0682] "Emotional engine means" refers to techniques and processes for recognizing a user's emotional state from input information.

[0683] The "generative model means" is an artificial intelligence model for generating optimal travel plans based on natural language processing and emotion recognition results.

[0684] "Travel Plan" refers to the travel schedule and suggestions generated by the system based on the user's preferences and emotions.

[0685] "Transportation" refers to the means of transportation offered in a travel plan, including trains, planes, buses, etc.

[0686] "Accommodation" refers to accommodation facilities included in the travel plan, and includes hotels, inns, guest houses, etc.

[0687] "Consolidation" refers to the process by which the system makes a single booking of transportation and accommodation based on a travel plan.

[0688] "Transmission" refers to the processes and techniques for communicating the generated travel planning and arrangement information to the user.

[0689] This invention relates to a system that automatically generates optimal travel plans based on a user's preferences and feelings, and makes all-in-one arrangements for transportation and accommodations. This system is characterized by the fact that a user inputs information via a dedicated application or website, and a server analyzes the information to generate and arrange a travel plan, and then sends the results to the user.

[0690] 1. Receiving input information

[0691] A user uses a device (e.g., smartphone, tablet, computer, etc.) to input information about their preferences and travel plans. Specific information includes the purpose of the trip, activities of interest, budget, and travel duration. Suppose the user inputs, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." The device then sends this input information to the server. During this process, the device verifies the format and accuracy of the input information before sending it.

[0692] 2. Natural Language Processing Methods

[0693] The server receives input information from the device and analyzes it using a natural language processing library (e.g., SpaCy or NLTK). Through this analysis, it extracts keywords and entities (e.g., "relaxation," "hot springs," "50,000 yen," etc.) to understand the user's preferences and travel needs.

[0694] 3. Means of Emotion Recognition

[0695] An emotion engine installed in the server recognizes the user's emotions from text information. For example, if a user enters the expression "I'm busy, so I want to relax," the server determines that the user is feeling stressed. At this stage, an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) is used. Specifically, the server passes the text to the emotion analysis API and obtains an emotion score (e.g., joy, sadness, stress, etc.).

[0696] 4. Generative Modeling Methods

[0697] The server uses a generative AI model to generate an optimal travel plan based on the analyzed information and the results of the emotion engine. This generative AI model learns information from different databases (e.g., tourist destination database, accommodation database, etc.). For example, it uses Microsoft Azure OpenAI Service to suggest travel destinations, activities, transportation, and accommodations. Specifically, the server inputs the analysis results and emotion recognition results as prompts to the generative AI model and receives the proposed travel plan.

[0698] 5. Bulk ordering method

[0699] The server arranges transportation and accommodations based on the generated travel plan. To do this, it connects with the API of an external reservation system (e.g., a travel reservation site API) to process reservations for bullet train tickets and hot spring inns in one go. Specifically, the server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation is complete.

[0700] 6. Means of transmission

[0701] The server sends the completed arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Once the user has finally confirmed the travel plan, the server sends confirmation information again. Specifically, the server sends the generated travel plan and reservation results to the device as a single data package and displays them in a user-viewable UI.

[0702] Specific examples

[0703] For example, a user is very tired from work and wants to plan a three-day trip to relax. The user opens the application and enters, "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen." The device sends this information to the server, which uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then analyzes the information and recognizes the emotion, "The user is tired and wants to relax." The generative AI model in the server then suggests the best travel destination based on these keywords and emotions.

[0704] Prompt Sentence Examples

[0705] "We provide input information from a user who wants to plan a three-day relaxing trip. The user likes hot springs and has a budget of around 50,000 yen. The user is also stressed and wants to relax. Please generate the optimal travel plan."

[0706] This system proposes optimal travel plans based on the user's emotional state, resulting in a higher level of personalization than conventional travel planning systems and improving user satisfaction.

[0707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0708] Step 1: Receiving input information

[0709] The user uses the device to input information about the trip. Specific input information includes the purpose of the trip, activities of interest, budget, and duration of the trip. For example, the user might input, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." This input information is sent from the device to the server. The server receives the input information and passes it on to the next process. Based on the input, it verifies whether the data format is correct and whether the required information is included, and prepares for the next step.

[0710] Step 2: Natural Language Processing

[0711] The server analyzes the received input information using a natural language processing library (e.g., SpaCy or NLTK). Specifically, it divides the text into tokens and extracts keywords and entities. For example, it extracts keywords such as "relaxation," "hot springs," and "50,000 yen." Based on this analysis, it understands the user's preferences and travel requirements and generates data to be used in the next step. As an output, it generates a dictionary containing the analyzed keywords and entities.

[0712] Step 3: Recognize emotions

[0713] The emotion engine installed on the server recognizes the user's emotions from the received input information. Specifically, it uses an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) to analyze the input text and obtain the user's emotion score. For example, it recognizes that the user is feeling stressed from the expression "I'm busy, so I want to relax." The output is an emotion score such as joy, sadness, or stress.

[0714] Step 4: Generative AI model creates recommendations

[0715] The server generates an optimal travel plan using a generative AI model based on the results of natural language processing and emotion recognition. Specifically, the analysis results and emotion recognition results are input into the generative AI model as prompts, and a suggested travel plan is received. For example, the prompt might read, "The user provides input information that they would like to plan a three-day relaxing trip. The user likes hot springs and has a budget of approximately 50,000 yen. The user is also feeling stressed and wants to relax. Please generate the optimal travel plan." The output is an optimal travel plan that includes travel destinations, activities, transportation, and accommodations.

[0716] Step 5: Execute the arrangement

[0717] The server arranges transportation and accommodations based on the generated travel plan. Specifically, it connects with an external reservation system (e.g., a travel reservation site API) and sends reservation data to complete the arrangements. For example, it makes reservations for Shinkansen tickets or hot spring resorts. The server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation has been completed. As an output, it provides confirmation that the reservation has been completed.

[0718] Step 6: Providing Information to Users

[0719] The server sends the completed travel arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Specifically, the generated travel plan and reservation results are sent as a single data package and displayed in a user-viewable UI. When the user finally confirms the travel plan, the server sends confirmation information again. The confirmed travel plan and information on the completion of arrangements are provided as output.

[0720] Through the above processing steps, the user can efficiently create a travel plan that matches his or her preferences and emotional state.

[0721] (Application example 2)

[0722] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0723] Conventional travel planning systems only require users to input their preferences and travel information, and do not propose or adjust plans that take into account the user's emotional state. This makes it difficult to provide optimal travel plans that match the user's emotions, resulting in low levels of satisfaction. Furthermore, there are no seamless systems that allow users to directly purchase proposed travel plans, forcing users to take the time and effort to make individual arrangements on separate platforms. There is a need for a system that solves these problems, proposes optimal travel plans based on the user's emotions, and enables direct purchases on online shopping platforms.

[0724] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0725] In this invention, the server includes means for a user to input information related to preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions, generative model means for generating a travel plan based on the analysis information and the results of the emotion engine, means for displaying the travel plan and recommended related products on an online shopping platform, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user. This makes it possible to propose and adjust an optimal travel plan that suits the user's emotions, and further enables the proposed travel plan to be seamlessly purchased through the online shopping platform.

[0726] A "means for user input of preference and travel input information" is a device or software that provides an interface for a user to input details about their interests and travel.

[0727] The "natural language processing means for receiving and analyzing the input information" is a system that has the technology and functions for analyzing text data sent by a user and extracting meaning.

[0728] "Emotion engine means for recognizing user's emotions" refers to a system and technology for analyzing and recognizing a user's emotional state from the information input by the user and the way in which it is expressed.

[0729] The "generative model means for generating a travel plan based on the analysis information and the results of the emotion engine" is a system that includes an artificial intelligence model and its operation for automatically generating an optimal travel plan based on the analyzed information and the user's emotional state.

[0730] "Means for displaying the travel plan and recommended related products on the online shopping platform" refers to technology and interfaces for displaying the generated travel plan and related products on an online shopping site or app in a manner that is visible to the user.

[0731] "Means for arranging transportation and accommodations in one go based on the travel plan" refers to a system and function for arranging reservations for the necessary transportation and accommodations in one go according to the generated travel plan.

[0732] The "means for transmitting the travel plan and arrangement information to the user" refers to communication means and technology for transmitting the generated travel plan and arrangement information based on it to the user's device.

[0733] This invention relates to a system that automatically generates an optimal travel plan based on input of a user's preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust the travel plan according to the user's emotional state. Specific embodiments are described below.

[0734] System configuration

[0735] 1. A means for users to enter preference and travel input information

[0736] Using a smartphone application, users input their preferences and travel-related information, including activities of interest (e.g., hiking, hot springs), travel duration, budget, and travel purpose.

[0737] The interface is built in React Native and the data is sent to the server in JSON format.

[0738] 2. Natural language processing means for receiving and analyzing the input information

[0739] The server receives the input information sent by the user, and the received data is analyzed using natural language processing techniques, such as SpaCy, to extract keywords and entities.

[0740] For example, user input such as "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen" is analyzed.

[0741] 3. Emotion engine means for recognizing user emotions

[0742] Based on the analysis of input information, the system uses the Google Cloud Natural Language API to recognize the user's emotions. For example, it analyzes and recognizes the user's emotion, such as "I'm tired and want to relax."

[0743] 4. A generative model means for generating a travel plan based on the analysis information and the results of the emotion engine.

[0744] The server generates an optimal travel plan based on the analyzed information and the results of the emotion engine using a generative AI model, which uses OpenAI's GPT.

[0745] Specific examples of prompts are as follows:

[0746] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[0747] Activities: Hot Springs

[0748] Duration: 3 days

[0749] Budget: 50,000 yen

[0750] Emotion: Relaxed

[0751] Please submit your proposal in the following format:

[0752] Travel destination: Hakone

[0753] Activities: Hot Springs

[0754] Transportation: Shinkansen

[0755] Accommodation: Hot spring inn

[0756] 5. Means for displaying said travel plans and recommended related products on a shopping platform

[0757] The generated travel plans and related products (travel packages, transportation tickets, accommodation, etc.) are displayed on a shopping platform. This interface is built using web technologies (e.g., HTML, CSS, JavaScript).

[0758] Users can make reservations and purchases directly from this screen.

[0759] 6. A means of arranging transportation and accommodations in one go based on the travel plan.

[0760] The server then makes all necessary travel arrangements (trains, planes, etc.) and accommodation reservations based on the generated travel plan. This arrangement is carried out through API integration with an external reservation system.

[0761] 7. Means for transmitting said travel planning and arrangement information to the user

[0762] Once completed, travel plans and arrangements are sent to the user's smartphone, where they can review the information and make any necessary corrections or final confirmations.

[0763] Specific processing examples

[0764] For example, suppose a user is feeling very stressed and wants to relax. The user enters "I would like to take a relaxing hot spring trip" into the application. The server receives this information and analyzes it using natural language processing means. After the emotion engine means recognizes the user's emotion as "tired," the generative AI model means generates an optimal travel plan. This plan suggests a hot spring inn in Hakone, a tranquil hot spring resort, and recommends the Shinkansen as a means of transportation. The information is displayed on an online shopping platform, and the user can make a reservation directly, completing travel arrangements easily and quickly.

[0765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0766] Step 1:

[0767] Users input preferences and travel information into the platform.

[0768] Input: A user opens a smartphone application and enters preferences and travel information, such as "I'd like to take a relaxing trip to a hot spring."

[0769] Output: The input information is sent to the server in JSON format.

[0770] What it does: When a user enters information into an application's interface, the information is formatted on the front end and sent to the back end as an API request.

[0771] Step 2:

[0772] The server receives input information from the user and analyzes it using natural language processing means.

[0773] Input: Information entered by the user (e.g., Relax, Hot Springs, Budget: 50,000 yen, etc.).

[0774] Output: Keywords and entities are extracted as the analysis results.

[0775] Specific operation: The server uses a natural language processing library such as SpaCy to parse the received JSON data and extract keywords and entities.

[0776] Step 3:

[0777] The server recognizes the user's emotions using an emotion engine means.

[0778] Input: Keywords and entities extracted by natural language processing.

[0779] Output: User's emotional state (e.g. tired, wanting to relax, etc.).

[0780] Specific operation: The server uses the Google Cloud Natural Language API to recognize the user's emotion from the parsed input information and obtains the user's emotional state as a result.

[0781] Step 4:

[0782] The server generates a travel plan using a generative AI model means.

[0783] Input: Analysis information and sentiment engine results.

[0784] Output: Optimized travel plan (e.g. destinations, activities, transportation, accommodation, etc.).

[0785] What it does: The server uses OpenAI's GPT model to generate a travel plan using the following prompt:

[0786] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[0787] Activities: Hot Springs

[0788] Duration: 3 days

[0789] Budget: 50,000 yen

[0790] Emotion: Relaxed

[0791] Please submit your proposal in the following format:

[0792] Travel destination: Hakone

[0793] Activities: Hot Springs

[0794] Transportation: Shinkansen

[0795] Accommodation: Hot spring inn

[0796] Step 5:

[0797] The server displays the generated travel plan and related products on the shopping platform.

[0798] Input: Generated itinerary and related product recommendations.

[0799] Output: Display screen on the online shopping platform.

[0800] Specific operation: The server uses HTML, CSS, and JavaScript to display the generated travel plan and related products on the shopping site.

[0801] Step 6:

[0802] The user reviews and approves the generated itinerary.

[0803] Input: Travel itinerary displayed on a shopping platform.

[0804] Output: User approval or correction request.

[0805] What it does: The user clicks a button on the screen to approve the travel plan, and can request modifications if necessary.

[0806] Step 7:

[0807] Receive payment information and arrange transportation and accommodations all in one place.

[0808] Input: User's payment information and approved travel plans.

[0809] Output: Booking completion information and confirmation notice.

[0810] Specific operation: The server connects to the API of an external reservation system to arrange transportation and accommodation. Once the arrangements are complete, it generates reservation completion information and notifies the user.

[0811] Step 8:

[0812] The travel planning and arrangement information is transmitted to the user.

[0813] Input: Completed booking information and overall travel plan.

[0814] Output: Confirmation information sent to the user's device.

[0815] Specific operation: The server notifies the user's smartphone of the generated travel plan and reservation completion information, allowing the user to check the travel plan.

[0816] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0817] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0818] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0819] [Third embodiment]

[0820] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0821] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0822] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0823] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0824] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0825] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0826] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0827] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0828] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0829] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0830] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0831] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0832] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0833] System configuration

[0834] 1. A means for users to enter preference and travel input information

[0835] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[0836] 2. Natural language processing means for receiving and analyzing the input information

[0837] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[0838] 3. A generation model means for generating a travel plan based on the analysis information.

[0839] Based on the analyzed information, the server uses a generative AI model to create an optimal travel plan. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences.

[0840] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0841] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[0842] 5. Means for transmitting said travel planning and arrangement information to a user

[0843] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[0844] Program processing (natural language explanation)

[0845] 1. Receiving input information

[0846] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[0847] 2. Natural Language Processing

[0848] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[0849] 3. Creating proposals using generative AI models

[0850] The server inputs the analyzed information into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[0851] 4. Execution of arrangements

[0852] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[0853] 5. Providing Information to Users

[0854] The server sends the completed arrangement information and the entire travel plan to the user, who can then check and finalize the information on their own device.

[0855] Specific examples

[0856] For example, suppose a user wants to refresh themselves in a place rich in nature on a three-day holiday. The user opens the application and enters, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0857] The device sends the input information to the server, which uses natural language processing technology to extract keywords such as "nature," "hiking," "hot springs," and "50,000 yen." The generative AI model then suggests optimal travel destinations based on these keywords. In this example, Hakuba Village in Nagano Prefecture is identified as the best fit, and offers include round-trip Shinkansen travel, hiking trails, and accommodations at Hakuba Onsen.

[0858] The server sends these proposals to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user a confirmation that the arrangements have been completed, allowing the user to make a comprehensive travel plan without any hassle.

[0859] This allows users to travel efficiently and with a high level of satisfaction, increasing the convenience of the system.

[0860] The processing flow will be explained below.

[0861] Step 1:

[0862] A user opens an application or website and enters basic travel information (e.g., desired travel destinations, travel duration, budget, and activities of interest).

[0863] Step 2:

[0864] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[0865] Step 3:

[0866] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[0867] Step 4:

[0868] The server analyzes the received data using natural language processing techniques, such as tokenizing the text, tagging parts of speech, and recognizing entities, to extract user preferences and requirements.

[0869] Step 5:

[0870] The server inputs the analyzed information into a generative AI model, which then generates the optimal travel plan for the user based on data learned from multiple databases, including selecting travel destinations, suggesting activities, selecting transportation options, and suggesting accommodations.

[0871] Step 6:

[0872] The server checks the generated itinerary to see if it is consistent as a whole and meets constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[0873] Step 7:

[0874] The server sends the confirmed travel plan to the terminal, typically using an HTTP response.

[0875] Step 8:

[0876] The terminal displays the itinerary received from the server on the user interface, allowing the user to check the displayed itinerary and make any necessary corrections.

[0877] Step 9:

[0878] If the user is satisfied with the travel plan, he or she sends a "confirm reservation" instruction from the terminal.

[0879] Step 10:

[0880] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[0881] Step 11:

[0882] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[0883] Step 12:

[0884] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[0885] Through the above processing steps, the user can efficiently and easily create and realize a comprehensive travel plan.

[0886] Example 1

[0887] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0888] In conventional travel planning systems, even if users input their preferences and travel information, the corresponding plans and reservation arrangements are often not made quickly and appropriately. Furthermore, few systems allow for bulk arrangements, requiring users to make separate reservations for each mode of transportation and accommodation, which is time-consuming. Furthermore, it is difficult to flexibly adjust plans according to users' preferences and requests.

[0889] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0890] In this invention, the server includes: a means for a user to input information about preferences and travel; a natural language processing means for receiving and analyzing the input information; a generative model means for generating a travel plan based on the analyzed information; a means for arranging transportation and accommodations in a single transaction based on the travel plan; a means for transmitting the travel plan and arrangement information to the user; a means for exchanging the arrangement information with an external reservation system through API integration; a means for the generative AI model to generate a travel plan based on prompt text; and a means for the natural language processing technology to extract keywords and entities. This allows for the rapid and appropriate generation of an optimal travel plan based on the user's input preferences and requests, enabling the system to make all-in-one arrangements. Furthermore, the system allows users to easily confirm and revise the plan, enabling flexible travel schedule planning.

[0891] "Preferences" is information that indicates a user's personal tastes and interests.

[0892] A "travel plan" is a proposal that includes travel itineraries, transportation, accommodation, and activity details generated based on the user's preferences and requirements.

[0893] "Natural language processing" is a technology that analyzes text information entered by a user and extracts meaning, keywords, and entities.

[0894] A "generative model" is an AI algorithm that automatically generates appropriate travel plans based on user input information.

[0895] "Arrangements" refers to making reservations for transportation, accommodation, etc. based on travel plans.

[0896] "External Booking System" means an external online booking platform for making reservations for transportation or accommodation.

[0897] "API integration" is an interface that allows the server and external reservation systems to communicate with each other.

[0898] A "prompt sentence" is an instruction sentence input to a generative AI model to generate a plan.

[0899] "Keywords" are important words that indicate the user's requirements and preferences, and are extracted using natural language processing.

[0900] An "entity" is a word or phrase that has a particular meaning or attribute in natural language processing.

[0901] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[0902] The system is implemented using the following hardware and software.

[0903] Hardware and software used

[0904] 1. Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[0905] 2. Server: A central server for data analysis and plan generation.

[0906] 3. Natural language processing tools: Tools for analyzing text data, such as Python's NLTK and spaCy.

[0907] 4. Generative AI model: An AI algorithm for automatically generating travel plans, such as GPT-4.

[0908] 5. External booking system: An online platform for booking transportation and accommodation.

[0909] 6. API integration: An interface for communication between the server and external reservation systems.

[0910] Overall system picture

[0911] 1. User Input

[0912] The user opens a dedicated application or website and enters details about their preferences and travel information. For example, they might enter, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0913] The terminal sends this input information to the server.

[0914] 2. Natural Language Processing

[0915] The server analyzes the information received from the terminal using natural language processing tools (NLTK, spaCy) and extracts keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen."

[0916] 3. Creating a travel plan using a generative AI model

[0917] The server creates a prompt sentence based on the analyzed information and inputs it into the generative AI model (GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday."

[0918] Based on this prompt, the generative AI model generates an optimal travel plan that includes destinations, activities, transportation, and accommodations. For example, a plan suggesting Hakuba Village in Nagano Prefecture includes a round-trip bullet train ride, hiking trails, and accommodations at Hakuba Onsen.

[0919] 4. Making travel arrangements

[0920] The server then arranges transportation and accommodation based on the generated travel plan. Through API integration with an external reservation system, Shinkansen tickets and accommodation reservations are automatically made.

[0921] 5. Providing information to users

[0922] The server sends the completed arrangement information and the entire travel plan to the user's terminal.

[0923] Users can check the information on their own devices and modify the plan as needed.

[0924] Specific examples

[0925] For example, suppose a user wants to refresh themselves in a natural setting over a three-day holiday. The user opens the application, enters "I want to refresh myself in a natural setting. I like hiking and hot springs. My budget is 50,000 yen," and sends this information to the server. The server uses natural language processing technology to extract the keywords "nature," "hiking," "hot springs," and "50,000 yen," and based on this, inputs prompts into the generative AI model. The generative AI model generates a travel plan that includes Hakuba Village in Nagano Prefecture, and makes Shinkansen tickets and accommodation reservations based on this plan. Finally, the server sends a confirmation of the completed arrangements to the user, who then confirms and modifies the travel plan.

[0926] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0927] Step 1:

[0928] Collecting user input

[0929] The user opens a dedicated application or website and enters details about their preferences and travel information, such as "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[0930] Input: User preferences and travel information

[0931] Output: Data containing user input information

[0932] Operation: The device sends the user's input information to the server.

[0933] Step 2:

[0934] Keyword extraction using natural language processing

[0935] The server analyzes the input information received from the device using natural language processing tools (such as NLTK or spaCy). As a result of the analysis, keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen" are extracted.

[0936] Input: User input information

[0937] Output: Extracted keywords and entities

[0938] How it works: The server uses natural language processing tools to parse the information and extract keywords and entities.

[0939] Step 3:

[0940] Generative AI model for travel planning

[0941] The server creates a prompt sentence based on the analyzed information and inputs it to the generative AI model (for example, GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday." The generative AI model generates a travel plan based on this prompt sentence.

[0942] Input: Extracted keywords and entities, prompt sentence

[0943] Output: Generated itinerary

[0944] How it works: The server inputs prompts into the generative AI model to generate an optimal travel plan.

[0945] Step 4:

[0946] Making travel arrangements

[0947] Based on the generated travel plan, the server automatically arranges transportation and accommodations through API integration with external reservation systems (e.g., online transportation reservation systems and accommodation reservation platforms).

[0948] Input: Travel Plan

[0949] Output: Reservation arrangement completion information

[0950] How it works: The server uses API integration to book and arrange transportation and accommodation.

[0951] Step 5:

[0952] Providing information to users

[0953] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check the information and modify the plan as necessary.

[0954] Input: Completed reservation information, overall travel plan

[0955] Output: User confirmation and correction results

[0956] How it works: The server sends information to the user's device, and the user confirms and modifies the plan.

[0957] (Application example 1)

[0958] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0959] Conventional travel planning systems have difficulty making travel suggestions that match a user's preferences and budget, and they also lack the ability to provide personalized content tailored to individual requirements. This requires users to make detailed plans and arrangements themselves, which is time-consuming. The present invention aims to solve these problems and provide a system that provides users with optimal travel plans and personalized video content.

[0960] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0961] In this invention, the server includes a means for allowing a user to input information about preferences and travel, a natural language processing means for receiving and analyzing the input information, and a generative model means for generating a travel plan based on the analyzed information, thereby enabling automatic generation of an optimal travel plan for the user and provision of personalized video content.

[0962] The "means for the user to input information about preferences and travel" refers to a means for the user to input information such as his / her travel preferences, budget, travel period, and places he / she wants to visit through an interface.

[0963] The "natural language processing means for receiving and analyzing the input information" is a means for receiving information input by a user and analyzing the information using natural language processing technology.

[0964] The "generative model means for generating a travel plan based on the analyzed information" is a means that utilizes a generative AI model to generate an optimal travel plan that meets the user's requirements based on the analyzed information.

[0965] The "means for collectively arranging transportation and accommodation based on the travel plan" refers to a means for collectively arranging transportation and accommodation to be used by the user based on the generated travel plan.

[0966] The "means for creating personalized video content from the travel plan" refers to a means for creating personalized video content based on the generated travel plan, allowing the user to deepen their understanding of the travel destination and activities.

[0967] The "means for transmitting the travel plan and arrangement information to the user" refers to a means for transmitting the final generated travel plan and information on arranged transportation and accommodations to the user's device.

[0968] "Means for a generative AI model to generate optimal travel plans based on information learned from multiple databases related to user preferences and travel" refers to a means for a generative AI model to propose travel plans that best suit a user's preferences and requests based on the analytical results learned from past data and multiple information sources.

[0969] The "means for the user to confirm and modify the travel plan" refers to a means for the user to confirm the generated travel plan and modify it as necessary.

[0970] overview

[0971] This invention relates to a system that automatically generates optimal travel plans based on user preferences and travel information. The system uses natural language processing technology and generative AI models to create travel plans, arrange transportation and accommodations, and even generate personalized video content.

[0972] System configuration and processing

[0973] 1. A means for users to enter preference and travel input information

[0974] Using a dedicated application, users can input their preferences and travel information, such as activities of interest, travel duration, budget, and purpose, etc. This information is sent to the server via an interface installed on a smartphone or tablet.

[0975] 2. Natural language processing means for receiving and analyzing the input information

[0976] The server receives data entered by the user through the device. The received data is analyzed using natural language processing technology to understand the user's preferences and requests. This process uses Python and the Transformers library, and the analysis is performed using the GPT-2 model.

[0977] 3. A generation model means for generating a travel plan based on the analysis information.

[0978] Based on information analyzed using natural language processing, a generative AI model generates an optimal travel plan. The generative AI model suggests travel destinations, activities, transportation, and accommodations based on the user's preferences and requests. This generation uses a model trained on public data and past databases to make highly accurate suggestions.

[0979] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[0980] The server arranges transportation (e.g., train, plane, bus) and accommodation (e.g., hotel, inn) based on the generated travel plan. This includes API integration with external reservation systems, allowing for automated bulk arrangements.

[0981] 5. Means for creating personalized video content from said travel plans

[0982] Based on the generated travel plan, the server generates personalized video content to help users understand their trip and enhance their experience. This video content includes introductions to travel destinations and footage of planned visits, allowing the user to visually convey the appeal of the trip.

[0983] 6. Means for transmitting said travel planning and arrangement information to the user

[0984] The completed travel plan and arrangement information is sent from the server to the user's terminal, where the user can review the information and make any necessary corrections.

[0985] Examples of concrete examples and prompts

[0986] For example, suppose a user is thinking about refreshing themselves in a natural setting on a three-day holiday and enters, "I like hiking and hot springs. My budget is 50,000 yen." Based on this information, the server extracts the keywords "nature," "hiking," "hot springs," and "50,000 yen," and uses a generative AI model (GPT-2) to suggest Hakuba Village in Nagano Prefecture and generate a travel plan including a round-trip Shinkansen train, hiking trails, and accommodations at Hakuba Hot Springs. Based on this plan, Shinkansen tickets and accommodations are booked, and an introductory video about Hakuba Village is generated and distributed to the user.

[0987] Example prompt sentence:

[0988] The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the best travel plan.

[0989] The system allows users to have an efficient and engaging travel experience without the hassle of detailed planning.

[0990] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0991] Step 1:

[0992] The user inputs information about their preferences and trip into the device. In this step, the user launches the application and inputs specific information such as the activities they are interested in (e.g., hiking, hot springs), their budget (e.g., 50,000 yen), and the duration of their trip (e.g., 3 days). The device temporarily stores the input information.

[0993] Step 2:

[0994] The terminal sends the information entered by the user to the server, where the terminal performs a format check and formats the data appropriately for natural language processing. The formatted data is then sent over the Internet to the server.

[0995] Step 3:

[0996] The server analyzes the received input information using natural language processing. Here, it uses Python's Natural Language Toolkit (NLTK) and Transformers library to extract keywords and entities (e.g., "hiking," "hot springs," "50,000 yen," etc.) from the input content and understands the user's preferences and requests. The analyzed information is passed on to the next step.

[0997] Step 4:

[0998] The server uses a generative AI model to generate a travel plan based on the analysis information. Specifically, a generative AI model such as GPT-2 is used to generate an optimal travel plan that matches the user's preferences and requirements. This generation process includes selecting a travel destination, suggesting activities, selecting transportation, and selecting accommodation. The prompt sentence used is, "The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the optimal travel plan."

[0999] Step 5:

[1000] Based on the generated travel plan, the server arranges transportation and accommodations in one go. Here, Shinkansen tickets and hotel reservations are automatically made using the API of a third-party reservation system. Reservation confirmation information is returned to the server and passed on to the next processing step.

[1001] Step 6:

[1002] The server creates personalized video content based on the generated itinerary. It collects footage and information about the places and activities to be visited in the generated itinerary and creates a video using editing software (e.g., Adobe Premiere Pro). The video is provided in a format that allows users to visually check the itinerary.

[1003] Step 7:

[1004] The server sends the completed travel plan, arrangement information, and personalized video content to the user's terminal, where the user can review the information and make any necessary corrections, thereby finalizing the travel plan.

[1005] Step 8:

[1006] After the user has finalized and revised their travel plans, the server confirms the final arrangements and reconfirms that all reservations have been made. At this step, the server sends the user a final trip confirmation and notifies them that their plans are complete.

[1007] This allows users to efficiently obtain optimal travel plans and personalized content based on their preferences and requirements.

[1008] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1009] This invention relates to a system that automatically generates optimal travel plans based on input of user preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust travel plans according to the user's emotional state.

[1010] System configuration

[1011] 1. A means for users to enter preference and travel input information

[1012] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[1013] 2. Natural language processing means for receiving and analyzing the input information

[1014] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[1015] 3. Emotion engine that recognizes emotions from user input

[1016] The emotion engine analyzes the user's emotions from the information they input and how they express it, and recognizes their emotional state, such as whether they are happy, tired, or stressed.

[1017] 4. A generation model means for generating a travel plan based on the analysis information

[1018] The server uses the analyzed information and the results of the emotion engine to create an optimal travel plan using a generative AI model. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences and emotions.

[1019] 5. A means of arranging transportation and accommodations in one go based on the travel plan.

[1020] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[1021] 6. Means for transmitting said travel planning and arrangement information to the user

[1022] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[1023] Program processing (natural language explanation)

[1024] 1. Receiving input information

[1025] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[1026] 2. Natural Language Processing

[1027] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[1028] 3. Emotional Recognition

[1029] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[1030] 4. Creating proposals using generative AI models

[1031] The server inputs the analyzed information and the results of the emotion engine into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[1032] 5. Execution of arrangements

[1033] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[1034] 6. Providing Information to Users

[1035] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check and finalize the information.

[1036] Specific examples

[1037] For example, suppose a user is very tired from work and is thinking about going on a three-day trip to relax. The user opens the application and enters, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen."

[1038] When the device sends the input information to the server, the server uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then recognizes the user's emotion, "I'm tired and want to relax." The generative AI model then suggests the optimal travel destination based on these keywords and emotions. In this example, Hakone, a tranquil hot spring resort, is identified as the optimal destination, and includes round-trip travel by Shinkansen and accommodation at a hot spring inn.

[1039] The server sends these suggestions to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user confirmation that the arrangements have been completed, allowing the user to easily create a fulfilling travel plan and refresh both their body and mind.

[1040] This allows users to travel efficiently and with high satisfaction, increasing the system's usability. By combining it with an emotion engine, it becomes possible to make more personalized suggestions than conventional travel planning systems, providing a travel experience that meets the user's unique needs.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] A user opens an application or website and enters basic travel information and preferences (desired destinations, travel duration, budget, activities of interest, etc.).

[1044] Step 2:

[1045] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[1046] Step 3:

[1047] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[1048] Step 4:

[1049] The server analyzes the received data using natural language processing technology, dividing the text data into tokens, tagging parts of speech, and recognizing entities to extract user preferences and requirements.

[1050] Step 5:

[1051] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[1052] Step 6:

[1053] The server inputs the analyzed information and the results of the emotion engine into a generative AI model, which uses information learned from multiple databases to generate an optimal travel plan, including destination selection, activity suggestions, transportation options, and accommodation suggestions.

[1054] Step 7:

[1055] The server checks the generated itinerary to ensure overall consistency and compliance with constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[1056] Step 8:

[1057] The server sends the confirmed travel plan to the terminal using an HTTP response.

[1058] Step 9:

[1059] The terminal displays the travel plan received from the server on the user interface. The user can check the displayed travel plan and have the option to modify activities, accommodations, etc. as necessary.

[1060] Step 10:

[1061] If the user is satisfied with the travel plan, he / she presses the "confirm reservation" button to confirm the plan, and the terminal sends this instruction to the server.

[1062] Step 11:

[1063] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[1064] Step 12:

[1065] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[1066] Step 13:

[1067] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[1068] Example 2

[1069] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1070] Conventional travel planning systems could propose travel plans based on the user's preferences and budget, but they did not take the user's emotional state into account, resulting in a lack of personalization. This made it difficult to provide a travel plan that was optimal for the user's current emotional and mental state, making it a challenge to improve user satisfaction.

[1071] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1072] In this invention, the server includes means for a user to input information about preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions from the input information, generative model means for generating a travel plan based on the analysis information and emotion recognition results, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user, thereby making it possible to propose an optimal travel plan based on the user's preferences and emotional state.

[1073] "User" means an individual or organization that uses the system to make travel plans.

[1074] "Preferences" refers to personal preferences such as the user's preferred activities, environments, and conditions.

[1075] "Input Information" is a set of data that a user provides to the system, including preferences, travel wishes, travel purpose, budget, duration, etc.

[1076] A "terminal" is an electronic device used by a user to input information and communicate with a server, and includes smartphones, tablets, computers, etc.

[1077] A "server" is a computer system that receives, analyzes, and processes input information from a user.

[1078] "Natural language processing means" refers to the technology and process that analyzes information entered by a user and extracts keywords and entities.

[1079] "Emotional engine means" refers to techniques and processes for recognizing a user's emotional state from input information.

[1080] The "generative model means" is an artificial intelligence model for generating optimal travel plans based on natural language processing and emotion recognition results.

[1081] "Travel Plan" refers to the travel schedule and suggestions generated by the system based on the user's preferences and emotions.

[1082] "Transportation" refers to the means of transportation offered in a travel plan, including trains, planes, buses, etc.

[1083] "Accommodation" refers to accommodation facilities included in the travel plan, and includes hotels, inns, guest houses, etc.

[1084] "Consolidation" refers to the process by which the system makes a single booking of transportation and accommodation based on a travel plan.

[1085] "Transmission" refers to the processes and techniques for communicating the generated travel planning and arrangement information to the user.

[1086] This invention relates to a system that automatically generates optimal travel plans based on a user's preferences and feelings, and makes all-in-one arrangements for transportation and accommodations. This system is characterized by the fact that a user inputs information via a dedicated application or website, and a server analyzes the information to generate and arrange a travel plan, and then sends the results to the user.

[1087] 1. Receiving input information

[1088] A user uses a device (e.g., smartphone, tablet, computer, etc.) to input information about their preferences and travel plans. Specific information includes the purpose of the trip, activities of interest, budget, and travel duration. Suppose the user inputs, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." The device then sends this input information to the server. During this process, the device verifies the format and accuracy of the input information before sending it.

[1089] 2. Natural Language Processing Methods

[1090] The server receives input information from the device and analyzes it using a natural language processing library (e.g., SpaCy or NLTK). Through this analysis, it extracts keywords and entities (e.g., "relaxation," "hot springs," "50,000 yen," etc.) to understand the user's preferences and travel needs.

[1091] 3. Means of Emotion Recognition

[1092] An emotion engine installed in the server recognizes the user's emotions from text information. For example, if a user enters the expression "I'm busy, so I want to relax," the server determines that the user is feeling stressed. At this stage, an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) is used. Specifically, the server passes the text to the emotion analysis API and obtains an emotion score (e.g., joy, sadness, stress, etc.).

[1093] 4. Generative Modeling Methods

[1094] The server uses a generative AI model to generate an optimal travel plan based on the analyzed information and the results of the emotion engine. This generative AI model learns information from different databases (e.g., tourist destination database, accommodation database, etc.). For example, it uses Microsoft Azure OpenAI Service to suggest travel destinations, activities, transportation, and accommodations. Specifically, the server inputs the analysis results and emotion recognition results as prompts to the generative AI model and receives the proposed travel plan.

[1095] 5. Bulk ordering method

[1096] The server arranges transportation and accommodations based on the generated travel plan. To do this, it connects with the API of an external reservation system (e.g., a travel reservation site API) to process reservations for bullet train tickets and hot spring inns in one go. Specifically, the server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation is complete.

[1097] 6. Means of transmission

[1098] The server sends the completed arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Once the user has finally confirmed the travel plan, the server sends confirmation information again. Specifically, the server sends the generated travel plan and reservation results to the device as a single data package and displays them in a user-viewable UI.

[1099] Specific examples

[1100] For example, a user is very tired from work and wants to plan a three-day trip to relax. The user opens the application and enters, "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen." The device sends this information to the server, which uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then analyzes the information and recognizes the emotion, "The user is tired and wants to relax." The generative AI model in the server then suggests the best travel destination based on these keywords and emotions.

[1101] Prompt Sentence Examples

[1102] "We provide input information from a user who wants to plan a three-day relaxing trip. The user likes hot springs and has a budget of around 50,000 yen. The user is also stressed and wants to relax. Please generate the optimal travel plan."

[1103] This system proposes optimal travel plans based on the user's emotional state, resulting in a higher level of personalization than conventional travel planning systems and improving user satisfaction.

[1104] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1105] Step 1: Receiving input information

[1106] The user uses the device to input information about the trip. Specific input information includes the purpose of the trip, activities of interest, budget, and duration of the trip. For example, the user might input, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." This input information is sent from the device to the server. The server receives the input information and passes it on to the next process. Based on the input, it verifies whether the data format is correct and whether the required information is included, and prepares for the next step.

[1107] Step 2: Natural Language Processing

[1108] The server analyzes the received input information using a natural language processing library (e.g., SpaCy or NLTK). Specifically, it divides the text into tokens and extracts keywords and entities. For example, it extracts keywords such as "relaxation," "hot springs," and "50,000 yen." Based on this analysis, it understands the user's preferences and travel requirements and generates data to be used in the next step. As an output, it generates a dictionary containing the analyzed keywords and entities.

[1109] Step 3: Recognize emotions

[1110] The emotion engine installed on the server recognizes the user's emotions from the received input information. Specifically, it uses an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) to analyze the input text and obtain the user's emotion score. For example, it recognizes that the user is feeling stressed from the expression "I'm busy, so I want to relax." The output is an emotion score such as joy, sadness, or stress.

[1111] Step 4: Generative AI model creates recommendations

[1112] The server generates an optimal travel plan using a generative AI model based on the results of natural language processing and emotion recognition. Specifically, the analysis results and emotion recognition results are input into the generative AI model as prompts, and a suggested travel plan is received. For example, the prompt might read, "The user provides input information that they would like to plan a three-day relaxing trip. The user likes hot springs and has a budget of approximately 50,000 yen. The user is also feeling stressed and wants to relax. Please generate the optimal travel plan." The output is an optimal travel plan that includes travel destinations, activities, transportation, and accommodations.

[1113] Step 5: Execute the arrangement

[1114] The server arranges transportation and accommodations based on the generated travel plan. Specifically, it connects with an external reservation system (e.g., a travel reservation site API) and sends reservation data to complete the arrangements. For example, it makes reservations for Shinkansen tickets or hot spring resorts. The server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation has been completed. As an output, it provides confirmation that the reservation has been completed.

[1115] Step 6: Providing Information to Users

[1116] The server sends the completed travel arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Specifically, the generated travel plan and reservation results are sent as a single data package and displayed in a user-viewable UI. When the user finally confirms the travel plan, the server sends confirmation information again. The confirmed travel plan and information on the completion of arrangements are provided as output.

[1117] Through the above processing steps, the user can efficiently create a travel plan that matches his or her preferences and emotional state.

[1118] (Application example 2)

[1119] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] Conventional travel planning systems only require users to input their preferences and travel information, and do not propose or adjust plans that take into account the user's emotional state. This makes it difficult to provide optimal travel plans that match the user's emotions, resulting in low levels of satisfaction. Furthermore, there are no seamless systems that allow users to directly purchase proposed travel plans, forcing users to take the time and effort to make individual arrangements on separate platforms. There is a need for a system that solves these problems, proposes optimal travel plans based on the user's emotions, and enables direct purchases on online shopping platforms.

[1121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1122] In this invention, the server includes means for a user to input information related to preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions, generative model means for generating a travel plan based on the analysis information and the results of the emotion engine, means for displaying the travel plan and recommended related products on an online shopping platform, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user. This makes it possible to propose and adjust an optimal travel plan that suits the user's emotions, and further enables the proposed travel plan to be seamlessly purchased through the online shopping platform.

[1123] A "means for user input of preference and travel input information" is a device or software that provides an interface for a user to input details about their interests and travel.

[1124] The "natural language processing means for receiving and analyzing the input information" is a system that has the technology and functions for analyzing text data sent by a user and extracting meaning.

[1125] "Emotion engine means for recognizing user's emotions" refers to a system and technology for analyzing and recognizing a user's emotional state from the information input by the user and the way in which it is expressed.

[1126] The "generative model means for generating a travel plan based on the analysis information and the results of the emotion engine" is a system that includes an artificial intelligence model and its operation for automatically generating an optimal travel plan based on the analyzed information and the user's emotional state.

[1127] "Means for displaying the travel plan and recommended related products on the online shopping platform" refers to technology and interfaces for displaying the generated travel plan and related products on an online shopping site or app in a manner that is visible to the user.

[1128] "Means for arranging transportation and accommodations in one go based on the travel plan" refers to a system and function for arranging reservations for the necessary transportation and accommodations in one go according to the generated travel plan.

[1129] The "means for transmitting the travel plan and arrangement information to the user" refers to communication means and technology for transmitting the generated travel plan and arrangement information based on it to the user's device.

[1130] This invention relates to a system that automatically generates an optimal travel plan based on input of a user's preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust the travel plan according to the user's emotional state. Specific embodiments are described below.

[1131] System configuration

[1132] 1. A means for users to enter preference and travel input information

[1133] Using a smartphone application, users input their preferences and travel-related information, including activities of interest (e.g., hiking, hot springs), travel duration, budget, and travel purpose.

[1134] The interface is built in React Native and the data is sent to the server in JSON format.

[1135] 2. Natural language processing means for receiving and analyzing the input information

[1136] The server receives the input information sent by the user, and the received data is analyzed using natural language processing techniques, such as SpaCy, to extract keywords and entities.

[1137] For example, user input such as "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen" is analyzed.

[1138] 3. Emotion engine means for recognizing user emotions

[1139] Based on the analysis of input information, the system uses the Google Cloud Natural Language API to recognize the user's emotions. For example, it analyzes and recognizes the user's emotion, such as "I'm tired and want to relax."

[1140] 4. A generative model means for generating a travel plan based on the analysis information and the results of the emotion engine.

[1141] The server generates an optimal travel plan based on the analyzed information and the results of the emotion engine using a generative AI model, which uses OpenAI's GPT.

[1142] Specific examples of prompts are as follows:

[1143] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[1144] Activities: Hot Springs

[1145] Duration: 3 days

[1146] Budget: 50,000 yen

[1147] Emotion: Relaxed

[1148] Please submit your proposal in the following format:

[1149] Travel destination: Hakone

[1150] Activities: Hot Springs

[1151] Transportation: Shinkansen

[1152] Accommodation: Hot spring inn

[1153] 5. Means for displaying said travel plans and recommended related products on a shopping platform

[1154] The generated travel plans and related products (travel packages, transportation tickets, accommodation, etc.) are displayed on a shopping platform. This interface is built using web technologies (e.g., HTML, CSS, JavaScript).

[1155] Users can make reservations and purchases directly from this screen.

[1156] 6. A means of arranging transportation and accommodations in one go based on the travel plan.

[1157] The server then makes all necessary travel arrangements (trains, planes, etc.) and accommodation reservations based on the generated travel plan. This arrangement is carried out through API integration with an external reservation system.

[1158] 7. Means for transmitting said travel planning and arrangement information to the user

[1159] Once completed, travel plans and arrangements are sent to the user's smartphone, where they can review the information and make any necessary corrections or final confirmations.

[1160] Specific processing examples

[1161] For example, suppose a user is feeling very stressed and wants to relax. The user enters "I would like to take a relaxing hot spring trip" into the application. The server receives this information and analyzes it using natural language processing means. After the emotion engine means recognizes the user's emotion as "tired," the generative AI model means generates an optimal travel plan. This plan suggests a hot spring inn in Hakone, a tranquil hot spring resort, and recommends the Shinkansen as a means of transportation. The information is displayed on an online shopping platform, and the user can make a reservation directly, completing travel arrangements easily and quickly.

[1162] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1163] Step 1:

[1164] Users input preferences and travel information into the platform.

[1165] Input: A user opens a smartphone application and enters preferences and travel information, such as "I'd like to take a relaxing trip to a hot spring."

[1166] Output: The input information is sent to the server in JSON format.

[1167] What it does: When a user enters information into an application's interface, the information is formatted on the front end and sent to the back end as an API request.

[1168] Step 2:

[1169] The server receives input information from the user and analyzes it using natural language processing means.

[1170] Input: Information entered by the user (e.g., Relax, Hot Springs, Budget: 50,000 yen, etc.).

[1171] Output: Keywords and entities are extracted as the analysis results.

[1172] Specific operation: The server uses a natural language processing library such as SpaCy to parse the received JSON data and extract keywords and entities.

[1173] Step 3:

[1174] The server recognizes the user's emotions using an emotion engine means.

[1175] Input: Keywords and entities extracted by natural language processing.

[1176] Output: User's emotional state (e.g. tired, wanting to relax, etc.).

[1177] Specific operation: The server uses the Google Cloud Natural Language API to recognize the user's emotion from the parsed input information and obtains the user's emotional state as a result.

[1178] Step 4:

[1179] The server generates a travel plan using a generative AI model means.

[1180] Input: Analysis information and sentiment engine results.

[1181] Output: Optimized travel plan (e.g. destinations, activities, transportation, accommodation, etc.).

[1182] What it does: The server uses OpenAI's GPT model to generate a travel plan using the following prompt:

[1183] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[1184] Activities: Hot Springs

[1185] Duration: 3 days

[1186] Budget: 50,000 yen

[1187] Emotion: Relaxed

[1188] Please submit your proposal in the following format:

[1189] Travel destination: Hakone

[1190] Activities: Hot Springs

[1191] Transportation: Shinkansen

[1192] Accommodation: Hot spring inn

[1193] Step 5:

[1194] The server displays the generated travel plan and related products on the shopping platform.

[1195] Input: Generated itinerary and related product recommendations.

[1196] Output: Display screen on the online shopping platform.

[1197] Specific operation: The server uses HTML, CSS, and JavaScript to display the generated travel plan and related products on the shopping site.

[1198] Step 6:

[1199] The user reviews and approves the generated itinerary.

[1200] Input: Travel itinerary displayed on a shopping platform.

[1201] Output: User approval or correction request.

[1202] What it does: The user clicks a button on the screen to approve the travel plan, and can request modifications if necessary.

[1203] Step 7:

[1204] Receive payment information and arrange transportation and accommodations all in one place.

[1205] Input: User's payment information and approved travel plans.

[1206] Output: Booking completion information and confirmation notice.

[1207] Specific operation: The server connects to the API of an external reservation system to arrange transportation and accommodation. Once the arrangements are complete, it generates reservation completion information and notifies the user.

[1208] Step 8:

[1209] The travel planning and arrangement information is transmitted to the user.

[1210] Input: Completed booking information and overall travel plan.

[1211] Output: Confirmation information sent to the user's device.

[1212] Specific operation: The server notifies the user's smartphone of the generated travel plan and reservation completion information, allowing the user to check the travel plan.

[1213] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1214] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1215] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 7, a 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.

[1219] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1220] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1221] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1223] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1224] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1225] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1226] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1227] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1228] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1229] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[1231] System configuration

[1232] 1. A means for users to enter preference and travel input information

[1233] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[1234] 2. Natural language processing means for receiving and analyzing the input information

[1235] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[1236] 3. A generation model means for generating a travel plan based on the analysis information.

[1237] Based on the analyzed information, the server uses a generative AI model to create an optimal travel plan. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences.

[1238] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[1239] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[1240] 5. Means for transmitting said travel planning and arrangement information to a user

[1241] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[1242] Program processing (natural language explanation)

[1243] 1. Receiving input information

[1244] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[1245] 2. Natural Language Processing

[1246] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[1247] 3. Creating proposals using generative AI models

[1248] The server inputs the analyzed information into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[1249] 4. Execution of arrangements

[1250] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[1251] 5. Providing Information to Users

[1252] The server sends the completed arrangement information and the entire travel plan to the user, who can then check and finalize the information on their own device.

[1253] Specific examples

[1254] For example, suppose a user wants to refresh themselves in a place rich in nature on a three-day holiday. The user opens the application and enters, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[1255] The device sends the input information to the server, which uses natural language processing technology to extract keywords such as "nature," "hiking," "hot springs," and "50,000 yen." The generative AI model then suggests optimal travel destinations based on these keywords. In this example, Hakuba Village in Nagano Prefecture is identified as the best fit, and offers include round-trip Shinkansen travel, hiking trails, and accommodations at Hakuba Onsen.

[1256] The server sends these proposals to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user a confirmation that the arrangements have been completed, allowing the user to make a comprehensive travel plan without any hassle.

[1257] This allows users to travel efficiently and with a high level of satisfaction, increasing the convenience of the system.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] A user opens an application or website and enters basic travel information (e.g., desired travel destinations, travel duration, budget, and activities of interest).

[1261] Step 2:

[1262] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[1263] Step 3:

[1264] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[1265] Step 4:

[1266] The server analyzes the received data using natural language processing techniques, such as tokenizing the text, tagging parts of speech, and recognizing entities, to extract user preferences and requirements.

[1267] Step 5:

[1268] The server inputs the analyzed information into a generative AI model, which then generates the optimal travel plan for the user based on data learned from multiple databases, including selecting travel destinations, suggesting activities, selecting transportation options, and suggesting accommodations.

[1269] Step 6:

[1270] The server checks the generated itinerary to see if it is consistent as a whole and meets constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[1271] Step 7:

[1272] The server sends the confirmed travel plan to the terminal, typically using an HTTP response.

[1273] Step 8:

[1274] The terminal displays the itinerary received from the server on the user interface, allowing the user to check the displayed itinerary and make any necessary corrections.

[1275] Step 9:

[1276] If the user is satisfied with the travel plan, he or she sends a "confirm reservation" instruction from the terminal.

[1277] Step 10:

[1278] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[1279] Step 11:

[1280] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[1281] Step 12:

[1282] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[1283] Through the above processing steps, the user can efficiently and easily create and realize a comprehensive travel plan.

[1284] Example 1

[1285] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1286] In conventional travel planning systems, even if users input their preferences and travel information, the corresponding plans and reservation arrangements are often not made quickly and appropriately. Furthermore, few systems allow for bulk arrangements, requiring users to make separate reservations for each mode of transportation and accommodation, which is time-consuming. Furthermore, it is difficult to flexibly adjust plans according to users' preferences and requests.

[1287] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1288] In this invention, the server includes: a means for a user to input information about preferences and travel; a natural language processing means for receiving and analyzing the input information; a generative model means for generating a travel plan based on the analyzed information; a means for arranging transportation and accommodations in a single transaction based on the travel plan; a means for transmitting the travel plan and arrangement information to the user; a means for exchanging the arrangement information with an external reservation system through API integration; a means for the generative AI model to generate a travel plan based on prompt text; and a means for the natural language processing technology to extract keywords and entities. This allows for the rapid and appropriate generation of an optimal travel plan based on the user's input preferences and requests, enabling the system to make all-in-one arrangements. Furthermore, the system allows users to easily confirm and revise the plan, enabling flexible travel schedule planning.

[1289] "Preferences" is information that indicates a user's personal tastes and interests.

[1290] A "travel plan" is a proposal that includes travel itineraries, transportation, accommodation, and activity details generated based on the user's preferences and requirements.

[1291] "Natural language processing" is a technology that analyzes text information entered by a user and extracts meaning, keywords, and entities.

[1292] A "generative model" is an AI algorithm that automatically generates appropriate travel plans based on user input information.

[1293] "Arrangements" refers to making reservations for transportation, accommodation, etc. based on travel plans.

[1294] "External Booking System" means an external online booking platform for making reservations for transportation or accommodation.

[1295] "API integration" is an interface that allows the server and external reservation systems to communicate with each other.

[1296] A "prompt sentence" is an instruction sentence input to a generative AI model to generate a plan.

[1297] "Keywords" are important words that indicate the user's requirements and preferences, and are extracted using natural language processing.

[1298] An "entity" is a word or phrase that has a particular meaning or attribute in natural language processing.

[1299] This invention relates to a system that automatically generates an optimal travel plan based on the user's preferences and travel information. The system of the present invention uses natural language processing and a generative AI model to create a travel plan based on the input information provided by the user, and can also arrange transportation and accommodations all at once.

[1300] The system is implemented using the following hardware and software.

[1301] Hardware and software used

[1302] 1. Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[1303] 2. Server: A central server for data analysis and plan generation.

[1304] 3. Natural language processing tools: Tools for analyzing text data, such as Python's NLTK and spaCy.

[1305] 4. Generative AI model: An AI algorithm for automatically generating travel plans, such as GPT-4.

[1306] 5. External booking system: An online platform for booking transportation and accommodation.

[1307] 6. API integration: An interface for communication between the server and external reservation systems.

[1308] Overall system picture

[1309] 1. User Input

[1310] The user opens a dedicated application or website and enters details about their preferences and travel information. For example, they might enter, "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[1311] The terminal sends this input information to the server.

[1312] 2. Natural Language Processing

[1313] The server analyzes the information received from the terminal using natural language processing tools (NLTK, spaCy) and extracts keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen."

[1314] 3. Creating a travel plan using a generative AI model

[1315] The server creates a prompt sentence based on the analyzed information and inputs it into the generative AI model (GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday."

[1316] Based on this prompt, the generative AI model generates an optimal travel plan that includes destinations, activities, transportation, and accommodations. For example, a plan suggesting Hakuba Village in Nagano Prefecture includes a round-trip bullet train ride, hiking trails, and accommodations at Hakuba Onsen.

[1317] 4. Making travel arrangements

[1318] The server then arranges transportation and accommodation based on the generated travel plan. Through API integration with an external reservation system, Shinkansen tickets and accommodation reservations are automatically made.

[1319] 5. Providing information to users

[1320] The server sends the completed arrangement information and the entire travel plan to the user's terminal.

[1321] Users can check the information on their own devices and modify the plan as needed.

[1322] Specific examples

[1323] For example, suppose a user wants to refresh themselves in a natural setting over a three-day holiday. The user opens the application, enters "I want to refresh myself in a natural setting. I like hiking and hot springs. My budget is 50,000 yen," and sends this information to the server. The server uses natural language processing technology to extract the keywords "nature," "hiking," "hot springs," and "50,000 yen," and based on this, inputs prompts into the generative AI model. The generative AI model generates a travel plan that includes Hakuba Village in Nagano Prefecture, and makes Shinkansen tickets and accommodation reservations based on this plan. Finally, the server sends a confirmation of the completed arrangements to the user, who then confirms and modifies the travel plan.

[1324] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1325] Step 1:

[1326] Collecting user input

[1327] The user opens a dedicated application or website and enters details about their preferences and travel information, such as "I want to refresh myself in a place rich in nature. I like hiking and hot springs. My budget is 50,000 yen."

[1328] Input: User preferences and travel information

[1329] Output: Data containing user input information

[1330] Operation: The device sends the user's input information to the server.

[1331] Step 2:

[1332] Keyword extraction using natural language processing

[1333] The server analyzes the input information received from the device using natural language processing tools (such as NLTK or spaCy). As a result of the analysis, keywords and entities such as "nature," "hiking," "hot springs," and "50,000 yen" are extracted.

[1334] Input: User input information

[1335] Output: Extracted keywords and entities

[1336] How it works: The server uses natural language processing tools to parse the information and extract keywords and entities.

[1337] Step 3:

[1338] Generative AI model for travel planning

[1339] The server creates a prompt sentence based on the analyzed information and inputs it to the generative AI model (for example, GPT-4). An example of a prompt sentence is, "The user wants to refresh themselves in a place rich in nature. They like hiking and hot springs, and their budget is 50,000 yen. Please suggest the best travel plan for a three-day holiday." The generative AI model generates a travel plan based on this prompt sentence.

[1340] Input: Extracted keywords and entities, prompt sentence

[1341] Output: Generated itinerary

[1342] How it works: The server inputs prompts into the generative AI model to generate an optimal travel plan.

[1343] Step 4:

[1344] Making travel arrangements

[1345] Based on the generated travel plan, the server automatically arranges transportation and accommodations through API integration with external reservation systems (e.g., online transportation reservation systems and accommodation reservation platforms).

[1346] Input: Travel Plan

[1347] Output: Reservation arrangement completion information

[1348] How it works: The server uses API integration to book and arrange transportation and accommodation.

[1349] Step 5:

[1350] Providing information to users

[1351] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check the information and modify the plan as necessary.

[1352] Input: Completed reservation information, overall travel plan

[1353] Output: User confirmation and correction results

[1354] How it works: The server sends information to the user's device, and the user confirms and modifies the plan.

[1355] (Application example 1)

[1356] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1357] Conventional travel planning systems have difficulty making travel suggestions that match a user's preferences and budget, and they also lack the ability to provide personalized content tailored to individual requirements. This requires users to make detailed plans and arrangements themselves, which is time-consuming. The present invention aims to solve these problems and provide a system that provides users with optimal travel plans and personalized video content.

[1358] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1359] In this invention, the server includes a means for allowing a user to input information about preferences and travel, a natural language processing means for receiving and analyzing the input information, and a generative model means for generating a travel plan based on the analyzed information, thereby enabling automatic generation of an optimal travel plan for the user and provision of personalized video content.

[1360] The "means for the user to input information about preferences and travel" refers to a means for the user to input information such as his / her travel preferences, budget, travel period, and places he / she wants to visit through an interface.

[1361] The "natural language processing means for receiving and analyzing the input information" is a means for receiving information input by a user and analyzing the information using natural language processing technology.

[1362] The "generative model means for generating a travel plan based on the analyzed information" is a means that utilizes a generative AI model to generate an optimal travel plan that meets the user's requirements based on the analyzed information.

[1363] The "means for collectively arranging transportation and accommodation based on the travel plan" refers to a means for collectively arranging transportation and accommodation to be used by the user based on the generated travel plan.

[1364] The "means for creating personalized video content from the travel plan" refers to a means for creating personalized video content based on the generated travel plan, allowing the user to deepen their understanding of the travel destination and activities.

[1365] The "means for transmitting the travel plan and arrangement information to the user" refers to a means for transmitting the final generated travel plan and information on arranged transportation and accommodations to the user's device.

[1366] "Means for a generative AI model to generate optimal travel plans based on information learned from multiple databases related to user preferences and travel" refers to a means for a generative AI model to propose travel plans that best suit a user's preferences and requests based on the analytical results learned from past data and multiple information sources.

[1367] The "means for the user to confirm and modify the travel plan" refers to a means for the user to confirm the generated travel plan and modify it as necessary.

[1368] overview

[1369] This invention relates to a system that automatically generates optimal travel plans based on user preferences and travel information. The system uses natural language processing technology and generative AI models to create travel plans, arrange transportation and accommodations, and even generate personalized video content.

[1370] System configuration and processing

[1371] 1. A means for users to enter preference and travel input information

[1372] Using a dedicated application, users can input their preferences and travel information, such as activities of interest, travel duration, budget, and purpose, etc. This information is sent to the server via an interface installed on a smartphone or tablet.

[1373] 2. Natural language processing means for receiving and analyzing the input information

[1374] The server receives data entered by the user through the device. The received data is analyzed using natural language processing technology to understand the user's preferences and requests. This process uses Python and the Transformers library, and the analysis is performed using the GPT-2 model.

[1375] 3. A generation model means for generating a travel plan based on the analysis information.

[1376] Based on information analyzed using natural language processing, a generative AI model generates an optimal travel plan. The generative AI model suggests travel destinations, activities, transportation, and accommodations based on the user's preferences and requests. This generation uses a model trained on public data and past databases to make highly accurate suggestions.

[1377] 4. A means of arranging transportation and accommodations in one go based on the travel plan.

[1378] The server arranges transportation (e.g., train, plane, bus) and accommodation (e.g., hotel, inn) based on the generated travel plan. This includes API integration with external reservation systems, allowing for automated bulk arrangements.

[1379] 5. Means for creating personalized video content from said travel plans

[1380] Based on the generated travel plan, the server generates personalized video content to help users understand their trip and enhance their experience. This video content includes introductions to travel destinations and footage of planned visits, allowing the user to visually convey the appeal of the trip.

[1381] 6. Means for transmitting said travel planning and arrangement information to the user

[1382] The completed travel plan and arrangement information is sent from the server to the user's terminal, where the user can review the information and make any necessary corrections.

[1383] Examples of concrete examples and prompts

[1384] For example, suppose a user is thinking about refreshing themselves in a natural setting on a three-day holiday and enters, "I like hiking and hot springs. My budget is 50,000 yen." Based on this information, the server extracts the keywords "nature," "hiking," "hot springs," and "50,000 yen," and uses a generative AI model (GPT-2) to suggest Hakuba Village in Nagano Prefecture and generate a travel plan including a round-trip Shinkansen train, hiking trails, and accommodations at Hakuba Hot Springs. Based on this plan, Shinkansen tickets and accommodations are booked, and an introductory video about Hakuba Village is generated and distributed to the user.

[1385] Example prompt sentence:

[1386] The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the best travel plan.

[1387] The system allows users to have an efficient and engaging travel experience without the hassle of detailed planning.

[1388] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1389] Step 1:

[1390] The user inputs information about their preferences and trip into the device. In this step, the user launches the application and inputs specific information such as the activities they are interested in (e.g., hiking, hot springs), their budget (e.g., 50,000 yen), and the duration of their trip (e.g., 3 days). The device temporarily stores the input information.

[1391] Step 2:

[1392] The terminal sends the information entered by the user to the server, where the terminal performs a format check and formats the data appropriately for natural language processing. The formatted data is then sent over the Internet to the server.

[1393] Step 3:

[1394] The server analyzes the received input information using natural language processing. Here, it uses Python's Natural Language Toolkit (NLTK) and Transformers library to extract keywords and entities (e.g., "hiking," "hot springs," "50,000 yen," etc.) from the input content and understands the user's preferences and requests. The analyzed information is passed on to the next step.

[1395] Step 4:

[1396] The server uses a generative AI model to generate a travel plan based on the analysis information. Specifically, a generative AI model such as GPT-2 is used to generate an optimal travel plan that matches the user's preferences and requirements. This generation process includes selecting a travel destination, suggesting activities, selecting transportation, and selecting accommodation. The prompt sentence used is, "The user is interested in hiking and hot springs, has a budget of 50,000 yen, and plans to travel for three days. Please suggest the optimal travel plan."

[1397] Step 5:

[1398] Based on the generated travel plan, the server arranges transportation and accommodations in one go. Here, Shinkansen tickets and hotel reservations are automatically made using the API of a third-party reservation system. Reservation confirmation information is returned to the server and passed on to the next processing step.

[1399] Step 6:

[1400] The server creates personalized video content based on the generated itinerary. It collects footage and information about the places and activities to be visited in the generated itinerary and creates a video using editing software (e.g., Adobe Premiere Pro). The video is provided in a format that allows users to visually check the itinerary.

[1401] Step 7:

[1402] The server sends the completed travel plan, arrangement information, and personalized video content to the user's terminal, where the user can review the information and make any necessary corrections, thereby finalizing the travel plan.

[1403] Step 8:

[1404] After the user has finalized and revised their travel plans, the server confirms the final arrangements and reconfirms that all reservations have been made. At this step, the server sends the user a final trip confirmation and notifies them that their plans are complete.

[1405] This allows users to efficiently obtain optimal travel plans and personalized content based on their preferences and requirements.

[1406] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1407] This invention relates to a system that automatically generates optimal travel plans based on input of user preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust travel plans according to the user's emotional state.

[1408] System configuration

[1409] 1. A means for users to enter preference and travel input information

[1410] Users use a dedicated application or website to input their preferences and travel-related information, such as their interests in activities (hiking, hot springs, etc.), travel duration, budget, and travel purpose.

[1411] 2. Natural language processing means for receiving and analyzing the input information

[1412] The input information sent from the device is sent to the server, which uses natural language processing technology to analyze the user's input information and understand the user's preferences and requests.

[1413] 3. Emotion engine that recognizes emotions from user input

[1414] The emotion engine analyzes the user's emotions from the information they input and how they express it, and recognizes their emotional state, such as whether they are happy, tired, or stressed.

[1415] 4. A generation model means for generating a travel plan based on the analysis information

[1416] The server uses the analyzed information and the results of the emotion engine to create an optimal travel plan using a generative AI model. This generative AI model learns information from multiple databases and makes suggestions tailored to the user's preferences and emotions.

[1417] 5. A means of arranging transportation and accommodations in one go based on the travel plan.

[1418] The server then makes all the arrangements for transportation (e.g., trains, planes, buses, etc.) and accommodations (e.g., hotels, inns, etc.) based on the generated travel plan, allowing the user to complete all of their travel arrangements hassle-free.

[1419] 6. Means for transmitting said travel planning and arrangement information to the user

[1420] The server sends the generated travel plan and reservation arrangement information to the user's terminal, where the user can check the information and make any necessary corrections.

[1421] Program processing (natural language explanation)

[1422] 1. Receiving input information

[1423] The user opens the application, enters information about their preferences and travel, and the device sends this information to the server.

[1424] 2. Natural Language Processing

[1425] The server uses natural language processing technology to analyze the information received from the terminal, extracting keywords and entities to understand the user's preferences and travel needs.

[1426] 3. Emotional Recognition

[1427] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[1428] 4. Creating proposals using generative AI models

[1429] The server inputs the analyzed information and the results of the emotion engine into a generative AI model to generate an optimal travel plan, including destinations, activities, transportation, and accommodation.

[1430] 5. Execution of arrangements

[1431] The server then arranges transportation and accommodation based on the generated itinerary, including API integration with external reservation systems.

[1432] 6. Providing Information to Users

[1433] The server sends the completed arrangement information and the entire travel plan to the user's terminal, where the user can check and finalize the information.

[1434] Specific examples

[1435] For example, suppose a user is very tired from work and is thinking about going on a three-day trip to relax. The user opens the application and enters, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen."

[1436] When the device sends the input information to the server, the server uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then recognizes the user's emotion, "I'm tired and want to relax." The generative AI model then suggests the optimal travel destination based on these keywords and emotions. In this example, Hakone, a tranquil hot spring resort, is identified as the optimal destination, and includes round-trip travel by Shinkansen and accommodation at a hot spring inn.

[1437] The server sends these suggestions to the user's device, and once the user is satisfied with the plan and confirms it, the server makes all-in-one arrangements for the Shinkansen ticket and accommodation reservations. Finally, the server sends the user confirmation that the arrangements have been completed, allowing the user to easily create a fulfilling travel plan and refresh both their body and mind.

[1438] This allows users to travel efficiently and with high satisfaction, increasing the system's usability. By combining it with an emotion engine, it becomes possible to make more personalized suggestions than conventional travel planning systems, providing a travel experience that meets the user's unique needs.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] A user opens an application or website and enters basic travel information and preferences (desired destinations, travel duration, budget, activities of interest, etc.).

[1442] Step 2:

[1443] The terminal checks the information entered by the user to see if there are any omissions or errors, and once the check is complete, sends the information to the server.

[1444] Step 3:

[1445] The server receives the input information sent from the device, imports the received data, and prepares it for analysis.

[1446] Step 4:

[1447] The server analyzes the received data using natural language processing technology, dividing the text data into tokens, tagging parts of speech, and recognizing entities to extract user preferences and requirements.

[1448] Step 5:

[1449] The emotion engine recognizes emotions from user input. For example, it determines that a user is feeling stressed based on an expression such as "I'm busy, so I want to relax."

[1450] Step 6:

[1451] The server inputs the analyzed information and the results of the emotion engine into a generative AI model, which uses information learned from multiple databases to generate an optimal travel plan, including destination selection, activity suggestions, transportation options, and accommodation suggestions.

[1452] Step 7:

[1453] The server checks the generated itinerary to ensure overall consistency and compliance with constraints such as budget and duration. If there are any inconsistencies, the itinerary is regenerated.

[1454] Step 8:

[1455] The server sends the confirmed travel plan to the terminal using an HTTP response.

[1456] Step 9:

[1457] The terminal displays the travel plan received from the server on the user interface. The user can check the displayed travel plan and have the option to modify activities, accommodations, etc. as necessary.

[1458] Step 10:

[1459] If the user is satisfied with the travel plan, he / she presses the "confirm reservation" button to confirm the plan, and the terminal sends this instruction to the server.

[1460] Step 11:

[1461] The server receives the final reservation information and begins arranging reservations for transportation and accommodation. It connects to an external reservation system via API and makes the necessary reservations.

[1462] Step 12:

[1463] The server confirms that all arrangements have been completed and generates a final confirmation, which is then sent to the terminal.

[1464] Step 13:

[1465] The terminal displays the final confirmation information on the user interface, where the user can confirm their travel details and download any necessary documents and tickets.

[1466] Example 2

[1467] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1468] Conventional travel planning systems could propose travel plans based on the user's preferences and budget, but they did not take the user's emotional state into account, resulting in a lack of personalization. This made it difficult to provide a travel plan that was optimal for the user's current emotional and mental state, making it a challenge to improve user satisfaction.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1470] In this invention, the server includes means for a user to input information about preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions from the input information, generative model means for generating a travel plan based on the analysis information and emotion recognition results, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user, thereby making it possible to propose an optimal travel plan based on the user's preferences and emotional state.

[1471] "User" means an individual or organization that uses the system to make travel plans.

[1472] "Preferences" refers to personal preferences such as the user's preferred activities, environments, and conditions.

[1473] "Input Information" is a set of data that a user provides to the system, including preferences, travel wishes, travel purpose, budget, duration, etc.

[1474] A "terminal" is an electronic device used by a user to input information and communicate with a server, and includes smartphones, tablets, computers, etc.

[1475] A "server" is a computer system that receives, analyzes, and processes input information from a user.

[1476] "Natural language processing means" refers to the technology and process that analyzes information entered by a user and extracts keywords and entities.

[1477] "Emotional engine means" refers to techniques and processes for recognizing a user's emotional state from input information.

[1478] The "generative model means" is an artificial intelligence model for generating optimal travel plans based on natural language processing and emotion recognition results.

[1479] "Travel Plan" refers to the travel schedule and suggestions generated by the system based on the user's preferences and emotions.

[1480] "Transportation" refers to the means of transportation offered in a travel plan, including trains, planes, buses, etc.

[1481] "Accommodation" refers to accommodation facilities included in the travel plan, and includes hotels, inns, guest houses, etc.

[1482] "Consolidation" refers to the process by which the system makes a single booking of transportation and accommodation based on a travel plan.

[1483] "Transmission" refers to the processes and techniques for communicating the generated travel planning and arrangement information to the user.

[1484] This invention relates to a system that automatically generates optimal travel plans based on a user's preferences and feelings, and makes all-in-one arrangements for transportation and accommodations. This system is characterized by the fact that a user inputs information via a dedicated application or website, and a server analyzes the information to generate and arrange a travel plan, and then sends the results to the user.

[1485] 1. Receiving input information

[1486] A user uses a device (e.g., smartphone, tablet, computer, etc.) to input information about their preferences and travel plans. Specific information includes the purpose of the trip, activities of interest, budget, and travel duration. Suppose the user inputs, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." The device then sends this input information to the server. During this process, the device verifies the format and accuracy of the input information before sending it.

[1487] 2. Natural Language Processing Methods

[1488] The server receives input information from the device and analyzes it using a natural language processing library (e.g., SpaCy or NLTK). Through this analysis, it extracts keywords and entities (e.g., "relaxation," "hot springs," "50,000 yen," etc.) to understand the user's preferences and travel needs.

[1489] 3. Means of Emotion Recognition

[1490] An emotion engine installed in the server recognizes the user's emotions from text information. For example, if a user enters the expression "I'm busy, so I want to relax," the server determines that the user is feeling stressed. At this stage, an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) is used. Specifically, the server passes the text to the emotion analysis API and obtains an emotion score (e.g., joy, sadness, stress, etc.).

[1491] 4. Generative Modeling Methods

[1492] The server uses a generative AI model to generate an optimal travel plan based on the analyzed information and the results of the emotion engine. This generative AI model learns information from different databases (e.g., tourist destination database, accommodation database, etc.). For example, it uses Microsoft Azure OpenAI Service to suggest travel destinations, activities, transportation, and accommodations. Specifically, the server inputs the analysis results and emotion recognition results as prompts to the generative AI model and receives the proposed travel plan.

[1493] 5. Bulk ordering method

[1494] The server arranges transportation and accommodations based on the generated travel plan. To do this, it connects with the API of an external reservation system (e.g., a travel reservation site API) to process reservations for bullet train tickets and hot spring inns in one go. Specifically, the server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation is complete.

[1495] 6. Means of transmission

[1496] The server sends the completed arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Once the user has finally confirmed the travel plan, the server sends confirmation information again. Specifically, the server sends the generated travel plan and reservation results to the device as a single data package and displays them in a user-viewable UI.

[1497] Specific examples

[1498] For example, a user is very tired from work and wants to plan a three-day trip to relax. The user opens the application and enters, "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen." The device sends this information to the server, which uses natural language processing technology to extract the keywords "relaxation," "hot springs," and "50,000 yen." The emotion engine then analyzes the information and recognizes the emotion, "The user is tired and wants to relax." The generative AI model in the server then suggests the best travel destination based on these keywords and emotions.

[1499] Prompt Sentence Examples

[1500] "We provide input information from a user who wants to plan a three-day relaxing trip. The user likes hot springs and has a budget of around 50,000 yen. The user is also stressed and wants to relax. Please generate the optimal travel plan."

[1501] This system proposes optimal travel plans based on the user's emotional state, resulting in a higher level of personalization than conventional travel planning systems and improving user satisfaction.

[1502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1503] Step 1: Receiving input information

[1504] The user uses the device to input information about the trip. Specific input information includes the purpose of the trip, activities of interest, budget, and duration of the trip. For example, the user might input, "I want to go to a place to relax. I like hot springs, and my budget is about 50,000 yen." This input information is sent from the device to the server. The server receives the input information and passes it on to the next process. Based on the input, it verifies whether the data format is correct and whether the required information is included, and prepares for the next step.

[1505] Step 2: Natural Language Processing

[1506] The server analyzes the received input information using a natural language processing library (e.g., SpaCy or NLTK). Specifically, it divides the text into tokens and extracts keywords and entities. For example, it extracts keywords such as "relaxation," "hot springs," and "50,000 yen." Based on this analysis, it understands the user's preferences and travel requirements and generates data to be used in the next step. As an output, it generates a dictionary containing the analyzed keywords and entities.

[1507] Step 3: Recognize emotions

[1508] The emotion engine installed on the server recognizes the user's emotions from the received input information. Specifically, it uses an emotion analysis API (e.g., Microsoft Azure Text Analytics or IBM Watson Natural Language Understanding) to analyze the input text and obtain the user's emotion score. For example, it recognizes that the user is feeling stressed from the expression "I'm busy, so I want to relax." The output is an emotion score such as joy, sadness, or stress.

[1509] Step 4: Generative AI model creates recommendations

[1510] The server generates an optimal travel plan using a generative AI model based on the results of natural language processing and emotion recognition. Specifically, the analysis results and emotion recognition results are input into the generative AI model as prompts, and a suggested travel plan is received. For example, the prompt might read, "The user provides input information that they would like to plan a three-day relaxing trip. The user likes hot springs and has a budget of approximately 50,000 yen. The user is also feeling stressed and wants to relax. Please generate the optimal travel plan." The output is an optimal travel plan that includes travel destinations, activities, transportation, and accommodations.

[1511] Step 5: Execute the arrangement

[1512] The server arranges transportation and accommodations based on the generated travel plan. Specifically, it connects with an external reservation system (e.g., a travel reservation site API) and sends reservation data to complete the arrangements. For example, it makes reservations for Shinkansen tickets or hot spring resorts. The server sends the necessary data (e.g., date, time, location, number of people, etc.) to the reservation API and receives information that the reservation has been completed. As an output, it provides confirmation that the reservation has been completed.

[1513] Step 6: Providing Information to Users

[1514] The server sends the completed travel arrangement information and the entire travel plan to the user's device. The user can check the plan contents through the device and make any necessary changes. Specifically, the generated travel plan and reservation results are sent as a single data package and displayed in a user-viewable UI. When the user finally confirms the travel plan, the server sends confirmation information again. The confirmed travel plan and information on the completion of arrangements are provided as output.

[1515] Through the above processing steps, the user can efficiently create a travel plan that matches his or her preferences and emotional state.

[1516] (Application example 2)

[1517] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1518] Conventional travel planning systems only require users to input their preferences and travel information, and do not propose or adjust plans that take into account the user's emotional state. This makes it difficult to provide optimal travel plans that match the user's emotions, resulting in low levels of satisfaction. Furthermore, there are no seamless systems that allow users to directly purchase proposed travel plans, forcing users to take the time and effort to make individual arrangements on separate platforms. There is a need for a system that solves these problems, proposes optimal travel plans based on the user's emotions, and enables direct purchases on online shopping platforms.

[1519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1520] In this invention, the server includes means for a user to input information related to preferences and travel, natural language processing means for receiving and analyzing the input information, emotion engine means for recognizing the user's emotions, generative model means for generating a travel plan based on the analysis information and the results of the emotion engine, means for displaying the travel plan and recommended related products on an online shopping platform, means for collectively arranging transportation and accommodations based on the travel plan, and means for transmitting the travel plan and arrangement information to the user. This makes it possible to propose and adjust an optimal travel plan that suits the user's emotions, and further enables the proposed travel plan to be seamlessly purchased through the online shopping platform.

[1521] A "means for user input of preference and travel input information" is a device or software that provides an interface for a user to input details about their interests and travel.

[1522] The "natural language processing means for receiving and analyzing the input information" is a system that has the technology and functions for analyzing text data sent by a user and extracting meaning.

[1523] "Emotion engine means for recognizing user's emotions" refers to a system and technology for analyzing and recognizing a user's emotional state from the information input by the user and the way in which it is expressed.

[1524] The "generative model means for generating a travel plan based on the analysis information and the results of the emotion engine" is a system that includes an artificial intelligence model and its operation for automatically generating an optimal travel plan based on the analyzed information and the user's emotional state.

[1525] "Means for displaying the travel plan and recommended related products on the online shopping platform" refers to technology and interfaces for displaying the generated travel plan and related products on an online shopping site or app in a manner that is visible to the user.

[1526] "Means for arranging transportation and accommodations in one go based on the travel plan" refers to a system and function for arranging reservations for the necessary transportation and accommodations in one go according to the generated travel plan.

[1527] The "means for transmitting the travel plan and arrangement information to the user" refers to communication means and technology for transmitting the generated travel plan and arrangement information based on it to the user's device.

[1528] This invention relates to a system that automatically generates an optimal travel plan based on input of a user's preferences and travel information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose and adjust the travel plan according to the user's emotional state. Specific embodiments are described below.

[1529] System configuration

[1530] 1. A means for users to enter preference and travel input information

[1531] Using a smartphone application, users input their preferences and travel-related information, including activities of interest (e.g., hiking, hot springs), travel duration, budget, and travel purpose.

[1532] The interface is built in React Native and the data is sent to the server in JSON format.

[1533] 2. Natural language processing means for receiving and analyzing the input information

[1534] The server receives the input information sent by the user, and the received data is analyzed using natural language processing techniques, such as SpaCy, to extract keywords and entities.

[1535] For example, user input such as "I want to go somewhere to relax. I like hot springs, and my budget is around 50,000 yen" is analyzed.

[1536] 3. Emotion engine means for recognizing user emotions

[1537] Based on the analysis of input information, the system uses the Google Cloud Natural Language API to recognize the user's emotions. For example, it analyzes and recognizes the user's emotion, such as "I'm tired and want to relax."

[1538] 4. A generative model means for generating a travel plan based on the analysis information and the results of the emotion engine.

[1539] The server generates an optimal travel plan based on the analyzed information and the results of the emotion engine using a generative AI model, which uses OpenAI's GPT.

[1540] Specific examples of prompts are as follows:

[1541] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[1542] Activities: Hot Springs

[1543] Duration: 3 days

[1544] Budget: 50,000 yen

[1545] Emotion: Relaxed

[1546] Please submit your proposal in the following format:

[1547] Travel destination: Hakone

[1548] Activities: Hot Springs

[1549] Transportation: Shinkansen

[1550] Accommodation: Hot spring inn

[1551] 5. Means for displaying said travel plans and recommended related products on a shopping platform

[1552] The generated travel plans and related products (travel packages, transportation tickets, accommodation, etc.) are displayed on a shopping platform. This interface is built using web technologies (e.g., HTML, CSS, JavaScript).

[1553] Users can make reservations and purchases directly from this screen.

[1554] 6. A means of arranging transportation and accommodations in one go based on the travel plan.

[1555] The server then makes all necessary travel arrangements (trains, planes, etc.) and accommodation reservations based on the generated travel plan. This arrangement is carried out through API integration with an external reservation system.

[1556] 7. Means for transmitting said travel planning and arrangement information to the user

[1557] Once completed, travel plans and arrangements are sent to the user's smartphone, where they can review the information and make any necessary corrections or final confirmations.

[1558] Specific processing examples

[1559] For example, suppose a user is feeling very stressed and wants to relax. The user enters "I would like to take a relaxing hot spring trip" into the application. The server receives this information and analyzes it using natural language processing means. After the emotion engine means recognizes the user's emotion as "tired," the generative AI model means generates an optimal travel plan. This plan suggests a hot spring inn in Hakone, a tranquil hot spring resort, and recommends the Shinkansen as a means of transportation. The information is displayed on an online shopping platform, and the user can make a reservation directly, completing travel arrangements easily and quickly.

[1560] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1561] Step 1:

[1562] Users input preferences and travel information into the platform.

[1563] Input: A user opens a smartphone application and enters preferences and travel information, such as "I'd like to take a relaxing trip to a hot spring."

[1564] Output: The input information is sent to the server in JSON format.

[1565] What it does: When a user enters information into an application's interface, the information is formatted on the front end and sent to the back end as an API request.

[1566] Step 2:

[1567] The server receives input information from the user and analyzes it using natural language processing means.

[1568] Input: Information entered by the user (e.g., Relax, Hot Springs, Budget: 50,000 yen, etc.).

[1569] Output: Keywords and entities are extracted as the analysis results.

[1570] Specific operation: The server uses a natural language processing library such as SpaCy to parse the received JSON data and extract keywords and entities.

[1571] Step 3:

[1572] The server recognizes the user's emotions using an emotion engine means.

[1573] Input: Keywords and entities extracted by natural language processing.

[1574] Output: User's emotional state (e.g. tired, wanting to relax, etc.).

[1575] Specific operation: The server uses the Google Cloud Natural Language API to recognize the user's emotion from the parsed input information and obtains the user's emotional state as a result.

[1576] Step 4:

[1577] The server generates a travel plan using a generative AI model means.

[1578] Input: Analysis information and sentiment engine results.

[1579] Output: Optimized travel plan (e.g. destinations, activities, transportation, accommodation, etc.).

[1580] What it does: The server uses OpenAI's GPT model to generate a travel plan using the following prompt:

[1581] Generate the best itinerary based on the user's needs and emotions. Use the following information:

[1582] Activities: Hot Springs

[1583] Duration: 3 days

[1584] Budget: 50,000 yen

[1585] Emotion: Relaxed

[1586] Please submit your proposal in the following format:

[1587] Travel destination: Hakone

[1588] Activities: Hot Springs

[1589] Transportation: Shinkansen

[1590] Accommodation: Hot spring inn

[1591] Step 5:

[1592] The server displays the generated travel plan and related products on the shopping platform.

[1593] Input: Generated itinerary and related product recommendations.

[1594] Output: Display screen on the online shopping platform.

[1595] Specific operation: The server uses HTML, CSS, and JavaScript to display the generated travel plan and related products on the shopping site.

[1596] Step 6:

[1597] The user reviews and approves the generated itinerary.

[1598] Input: Travel itinerary displayed on a shopping platform.

[1599] Output: User approval or correction request.

[1600] What it does: The user clicks a button on the screen to approve the travel plan, and can request modifications if necessary.

[1601] Step 7:

[1602] Receive payment information and arrange transportation and accommodations all in one place.

[1603] Input: User's payment information and approved travel plans.

[1604] Output: Booking completion information and confirmation notice.

[1605] Specific operation: The server connects to the API of an external reservation system to arrange transportation and accommodation. Once the arrangements are complete, it generates reservation completion information and notifies the user.

[1606] Step 8:

[1607] The travel planning and arrangement information is transmitted to the user.

[1608] Input: Completed booking information and overall travel plan.

[1609] Output: Confirmation information sent to the user's device.

[1610] Specific operation: The server notifies the user's smartphone of the generated travel plan and reservation completion information, allowing the user to check the travel plan.

[1611] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1612] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1613] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1614] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1615] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1616] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1617] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1618] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1619] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1620] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1621] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1622] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1623] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1624] 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.

[1625] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1626] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1627] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1628] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1629] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1630] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1631] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1632] The following is further disclosed regarding the above embodiment.

[1633] (Claim 1)

[1634] means for a user to input preference and travel input information;

[1635] natural language processing means for receiving and analyzing the input information;

[1636] a generation model means for generating a travel plan based on the analysis information;

[1637] A means for collectively arranging transportation and accommodations based on the travel plan;

[1638] means for transmitting said travel planning and arrangement information to a user.

[1639] (Claim 2)

[1640] 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on user preferences and information learned from multiple travel databases.

[1641] (Claim 3)

[1642] 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan.

[1643] "Example 1"

[1644] (Claim 1)

[1645] means for a user to input preference and travel input information;

[1646] natural language processing means for receiving and analyzing the input information;

[1647] a generation model means for generating a travel plan based on the analysis information;

[1648] A means for collectively arranging transportation and accommodations based on the travel plan;

[1649] means for transmitting said travel planning and arrangement information to a user;

[1650] A means for exchanging the arrangement information with an external reservation system through API linkage;

[1651] A means for the generative AI model to generate a travel plan based on a prompt sentence;

[1652] The system wherein the natural language processing technology includes means for extracting keywords and entities.

[1653] (Claim 2)

[1654] 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on user preferences and information learned from multiple travel databases.

[1655] (Claim 3)

[1656] 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan.

[1657] "Application Example 1"

[1658] (Claim 1)

[1659] means for a user to input preference and travel input information;

[1660] natural language processing means for receiving and analyzing the input information;

[1661] a generation model means for generating a travel plan based on the analysis information;

[1662] A means for collectively arranging transportation and accommodations based on the travel plan;

[1663] means for creating personalized video content from said travel itinerary;

[1664] means for transmitting said travel planning and arrangement information to a user.

[1665] (Claim 2)

[1666] 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on user preferences and information learned from multiple travel databases.

[1667] (Claim 3)

[1668] 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan.

[1669] "Example 2: Combining Emotion Engines"

[1670] (Claim 1)

[1671] means for a user to input preference and travel input information;

[1672] natural language processing means for receiving and analyzing the input information;

[1673] emotion engine means for recognizing a user's emotion from input information;

[1674] a generative model means for generating a travel plan based on the analysis information and emotion recognition results;

[1675] A means for collectively arranging transportation and accommodations based on the travel plan;

[1676] means for transmitting said travel planning and arrangement information to a user.

[1677] (Claim 2)

[1678] 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on user preferences and information learned from multiple travel databases.

[1679] (Claim 3)

[1680] 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan.

[1681] "Application example 2 when combining emotion engines"

[1682] (Claim 1)

[1683] means for a user to input preference and travel input information;

[1684] natural language processing means for receiving and analyzing the input information;

[1685] emotion engine means for recognizing the emotion of a user;

[1686] a generative model means for generating a travel plan based on the analysis information and the result of the emotion engine;

[1687] means for displaying the travel plan and recommended related products on a shopping platform;

[1688] A means for collectively arranging transportation and accommodations based on the travel plan;

[1689] means for transmitting said travel planning and arrangement information to a user.

[1690] (Claim 2)

[1691] 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on user preferences and information learned from multiple travel information repositories.

[1692] (Claim 3)

[1693] 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan. [Explanation of symbols]

[1694] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to input preference and travel input information; natural language processing means for receiving and analyzing the input information; a generation model means for generating a travel plan based on the analysis information; A means for collectively arranging transportation and accommodations based on the travel plan; means for transmitting said travel planning and arrangement information to a user.

2. 10. The system of claim 1, wherein the generative AI model includes means for generating an optimal travel plan based on information learned from multiple databases of user preferences and travel.

3. 10. The system of claim 1, further comprising means for a user to confirm and modify said travel plan.

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