System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a user inputted travel request; means for analyzing the user input with a natural language processing engine; means for generating a travel plan candidate based on the analysis; means for crawling external sources to gather up-to-date information; means for presenting the generated travel plan candidate to the user; means for re-generating the plan based on user feedback; and means for executing a reservation based on the finalized plan.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] Conventional travel planning and booking methods require users to individually research multiple booking sites and information sources to create the optimal plan, which is time-consuming and labor-intensive. Furthermore, it is difficult to comprehensively collect and evaluate appropriate information, which can lead to users missing the optimal plan. There is a need for a system that reduces the burden of this manual work and allows users to plan and book trips more easily and efficiently. [Means for solving the problem]

[0005] The present invention provides a system that receives travel requests entered by a user, analyzes them using a natural language processing engine, and generates a travel plan including suggested destinations, transportation options, accommodations, activities, and meals based on the analysis results. Furthermore, the system crawls external information sources to collect the latest information and suggests it to the user. The system also regenerates the plan based on the user's feedback and handles the reservation procedure based on the confirmed plan, allowing the user to quickly and efficiently plan and complete the reservation.

[0006] "User" refers to an individual or corporation who wishes to use the System to create and book a travel plan.

[0007] "Travel wishes" refers to the requirements and conditions a user has regarding a trip, such as specific wishes such as the place they want to go, their budget, transportation, schedule, and desired activities.

[0008] A "natural language processing engine" refers to a program or algorithm that analyzes natural language text entered by a user and extracts useful information from it.

[0009] "Parsing" refers to using a natural language processing engine to process the user's text input and extract the necessary information (e.g., destination and budget).

[0010] "Candidate travel plans" refer to specific travel schedules and contents proposed based on the user's preferences, including destinations, transportation, accommodations, activities, meals, etc.

[0011] "External information sources" refer to various information sources that provide the latest information about a user's travel plans, such as reservation sites on the Internet, individual store homepages, and personal blogs.

[0012] "Crawling" refers to a system automatically crawling through multiple sources on the Internet to collect specified information.

[0013] "Latest Information" refers to the most recent travel-related information collected by crawling from external sources.

[0014] "Proposal" refers to the act of presenting the generated travel plan candidates to the user and asking for their evaluation and approval.

[0015] "Feedback" refers to the user's evaluation of proposed travel plans and requests for revisions.

[0016] "Regeneration" refers to the process of recreating a potential itinerary based on feedback.

[0017] "Confirming the plan" refers to the user and the system agreeing to approve and fix the final travel plan.

[0018] "Reservation procedure" refers to the act of the system booking details of accommodation, transportation, activities, meals, etc. included in the travel plan on your behalf.

[0019] "Reservation Confirmation Information" means detailed information regarding each reservation made under a confirmed travel plan. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and books all destinations, transportation, accommodation, activities, and meals all at once.

[0042] User Input

[0043] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance.").

[0044] Analysis using natural language processing

[0045] The server uses a natural language processing (NLP) engine to analyze the input text and extract necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," and "car").

[0046] Generate a plan

[0047] The server generates travel plan suggestions based on the extracted information. Specifically, the server searches a database for possible destinations, transportation options, accommodations, activities, and meals. It also crawls external information sources (reservation sites, individual store homepages, personal blogs, etc.) to collect the latest information.

[0048] Suggestions and Feedback

[0049] The server sends the generated travel plan candidates to the terminal and presents them to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or, "Do you have a cheaper plan?"

[0050] regeneration

[0051] The server receives user feedback and regenerates the plan as necessary. For example, it can search for more reasonably priced accommodation and propose a new plan.

[0052] Confirmation and booking process

[0053] Once the user confirms the plan, the server handles the reservation process on their behalf, specifically checking availability of accommodations, making reservations, securing parking spaces for their car, and making reservations for activities and meals.

[0054] Providing reservation confirmation information

[0055] Once the reservation is complete, the server compiles the information and notifies the user, for example, by sending a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Onsen Ryokan have been reserved."

[0056] Specific examples

[0057] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate and suggest travel destinations (e.g., Okinawa), transportation methods (airplane), accommodations (resort hotels), activities (snorkeling, beaches, etc.), and meals (restaurants within the resort). Once the user confirms and approves the plan, the server will complete the reservation process and notify the user of the completed information.

[0058] The system allows users to effortlessly create comprehensive travel plans and complete reservations efficiently.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user inputs their travel wishes through the chat interface on the device. For example, they might input a sentence like, "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0062] Step 2:

[0063] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[0064] Step 3:

[0065] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car."

[0066] Step 4:

[0067] The server uses the extracted information to search for suitable destinations, transportation options, accommodations, activities, and dining options. In addition to the information in the database, it also crawls external information sources (reservation sites, individual store homepages, blogs, etc.) to collect the latest information.

[0068] Step 5:

[0069] The server generates a travel plan including possible destinations (e.g., Hakone), transportation options (private car), accommodations (XX hot spring inn), activities (hot springs, massage), and meals (△△ soba restaurant).

[0070] Step 6:

[0071] The server sends the generated travel plan to the terminal and presents it to the user, who then checks the plan and enters feedback.

[0072] Step 7:

[0073] The device receives the user's feedback and sends it to the server. The server regenerates the plan based on the feedback. For example, if the user requests a cheaper plan, the server will search for and suggest cheaper accommodations within the user's budget.

[0074] Step 8:

[0075] After the user confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, activities, and meals. For example, the user accesses the reservation website for a certain hot spring inn, checks room availability, and confirms the reservation.

[0076] Step 9:

[0077] The server verifies that the reservations are complete and aggregates all reservations to generate a reservation confirmation, which includes accommodation confirmation, activity reservation numbers, meal reservation details, etc.

[0078] Step 10:

[0079] The server sends the reservation confirmation information to the terminal and notifies the user, who receives the confirmation message through the terminal and can reconfirm or change the reservation as necessary.

[0080] In this way, users can efficiently create, confirm, and book travel plans using interactive AI chat.

[0081] Example 1

[0082] 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."

[0083] Planning and booking a trip can be a time-consuming process, requiring users to check multiple websites and services. This wastes time and makes it difficult for users to find the best plan. The objective of this invention is to provide a system that allows users to easily and efficiently plan a trip and make reservations all at once.

[0084] 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.

[0085] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling databases and external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans, thereby enabling users to efficiently and easily plan their trips and make reservations all at once.

[0086] "User" means any person or entity that uses the System to plan and book travel.

[0087] A "terminal" is a device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0088] The "interactive AI chat interface" is a user interface that allows users to input their travel preferences in natural language and interact with the system.

[0089] A "natural language processing engine" is a technology used to analyze user input and extract important information. Specifically, it is a text analysis technology.

[0090] "Candidate travel plans" are multiple travel plan options generated based on user input.

[0091] A "database" is an information collection that stores travel-related information and is a source of information that the server accesses to generate potential travel plans.

[0092] "Crawling external sources" refers to the process of automatically searching public sources on the Internet to gather the latest information.

[0093] "Feedback" refers to the user's evaluation or opinion on the proposed travel plans.

[0094] "Booking Process" means the process by which a reservation is made for some or all elements of a trip (e.g., accommodation, transportation, activities, meals, etc.).

[0095] "Booking in one go" means completing the booking for all elements of a travel plan in a single operation.

[0096] This invention is a system that can propose and book travel plans in one go based on travel preferences entered by a user. The system includes a server, a terminal, and a natural language processing engine that analyzes user input.

[0097] User Input

[0098] The device can be a smartphone, tablet, or personal computer. This device is equipped with an interactive AI chat interface, which allows users to input their travel preferences in natural language. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0099] Analysis using natural language processing

[0100] The server analyzes the input text received from the user using a natural language processing engine (e.g., Google® NLP, spaCy) to extract necessary information, such as the type of trip, budget, and desired means of transportation.

[0101] Generate a plan

[0102] The server generates a candidate itinerary based on the extracted information. At this stage, the server searches for relevant information from a database (e.g., MySQL®, MongoDB) and also uses a web crawler (e.g., Beautiful Soup, Scrapy) to gather up-to-date information from external sources. The generated candidate itinerary includes suggestions for destinations, transportation, accommodations, activities, and meals.

[0103] Suggestions and Feedback

[0104] The server sends the generated travel plan candidates to the terminal. The user can review the plans through the terminal and provide feedback. For example, feedback such as "I'd like that plan," or "Is there a cheaper plan?" is possible.

[0105] regeneration

[0106] The server receives user feedback and regenerates plans as needed. For example, if a user requests a lower budget, the server will search again and present a more reasonable plan.

[0107] Confirmation and booking process

[0108] Once the user confirms their plan, the server handles all the booking procedures, including checking availability of accommodation, reserving a parking space for the user's car, and booking activities and meals.

[0109] Providing reservation confirmation information

[0110] Once the reservation procedure is complete, the server will send a summary of the reservation confirmation information to the user, such as a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Hot Springs Inn have been reserved."

[0111] Specific examples

[0112] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate a travel plan with Okinawa as the destination, suggesting airplanes, resort hotels, snorkeling and beach activities, restaurants within the resort, etc. Once the user confirms and approves the plan, the server will carry out all reservation procedures and notify the user of the completed information.

[0113] This system allows users to plan their trips in one place and complete reservations efficiently, significantly reducing the amount of work required in the planning stage of a trip.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] A user opens a conversational AI chat interface using a device

[0117] A user opens an interactive AI chat interface using a smartphone, tablet, or personal computer. The interface is provided through a browser or a dedicated application. The input is the details of the user's desired trip (e.g., "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."), and the output is that this information is sent to the server.

[0118] Step 2:

[0119] The server receives the user's input

[0120] The server receives the user's input text through an HTTP request, which is stored on the server and passed to the next processing step. The input is the user's desired travel details, and the output is the user's desired data in text format.

[0121] Step 3:

[0122] The server launches a natural language processing engine to analyze the input text.

[0123] The server launches a natural language processing engine (e.g., Google NLP, spaCy) and analyzes the user's input text. The analysis extracts the necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," "car"). The input is the user's desired data in text format, and the output is the analyzed information (a list of keywords and phrases).

[0124] Step 4:

[0125] The server generates travel plan candidates

[0126] The server generates itinerary suggestions based on the extracted information. Specifically, it searches for relevant information from databases (e.g., MySQL, MongoDB) and collects up-to-date information from external sources using web crawlers (e.g., Beautiful Soup, Scrapy). The input is the parsed information, and the output is the generated itinerary suggestions (suggestions for destinations, transportation, accommodation, activities, and meals).

[0127] Step 5:

[0128] The server sends the generated travel plan candidates to the terminal.

[0129] The server sends the generated travel plan candidates to the terminal in JSON format via the REST API. The input is the travel plan candidates, and the output is the travel plan information sent to the terminal.

[0130] Step 6:

[0131] The device displays the proposed travel plan to the user.

[0132] The terminal displays the received travel plan candidates on a user interface. The user can review the plans and provide feedback. The input is the travel plan information sent to the terminal, and the output is the user's feedback.

[0133] Step 7:

[0134] The server receives user feedback and regenerates the plan as needed.

[0135] The server receives feedback from the user and regenerates the itinerary as needed, for example, re-searching for reasonably priced accommodations and generating a new itinerary. The input is the user's feedback, and the output is the regenerated itinerary candidates.

[0136] Step 8:

[0137] Once the user confirms the plan, the server starts the reservation process.

[0138] Once the user finalizes their travel plans, the server initiates the reservation process, including checking and reserving accommodations, reserving parking spaces for their vehicle, and booking activities and meals. The input is the finalized travel plans, and the output is reservation confirmation information.

[0139] Step 9:

[0140] The server collects the reservation confirmation information and notifies the user.

[0141] Once the reservation procedure is completed, the server sends the reservation confirmation information to the user via email or in-app notification. The input is the reservation confirmation information, and the output is a notification message to the user.

[0142] (Application example 1)

[0143] 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."

[0144] Modern travel planning is problematic because it requires a lot of time and effort. It is particularly cumbersome for users, as it requires separate reservations for destinations, transportation, accommodations, activities, and meals. Furthermore, various payments must be made each time a reservation is made, often complicating the process. For this reason, there is a demand for a system that automatically proposes comprehensive travel plans and smoothly handles everything from reservations to payments.

[0145] 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.

[0146] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating a plan based on user feedback, means for making a reservation based on the confirmed plan, and means for making an electronic payment when making the reservation. This allows the user to plan, book, and pay for all of their travel plans through a single interface.

[0147] "User input" is text data that describes travel wishes and conditions in natural language.

[0148] A "natural language processing engine" is a software system that analyzes user input and extracts requirements and information from it.

[0149] "Candidate itineraries" are suggested options for travel destinations, transportation, accommodations, activities, meals, etc., based on analyzed user input.

[0150] "External sources" refers to the latest information collected from various websites, databases, APIs, etc. on the Internet.

[0151] "Crawling" refers to the process of automatically collecting data from external sources over the Internet.

[0152] "User feedback" refers to the evaluation and additional information provided by the user regarding the proposed travel plan.

[0153] "Regeneration" is the process of recreating a travel plan based on user feedback.

[0154] "Reservation Facility" means a system or process that fulfills reservations for accommodation, transportation, activities, and meals according to a confirmed travel plan.

[0155] "Electronic payment" refers to online payment procedures using credit cards, digital wallets, etc.

[0156] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and reserves destinations, transportation, accommodations, activities, and meals all at once. This system is composed of users, a server, and terminals.

[0157] 1. User Input and Natural Language Processing

[0158] First, a user uses a device such as a smartphone or tablet to input their travel preferences into a conversational AI chat interface. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's somewhere I can drive to." This interface runs on the device.

[0159] The server analyzes the user's input using a natural language processing engine (e.g., SpaCy or Google NLP API) and extracts information such as the travel destination, budget, transportation method, and duration.

[0160] 2. Generate a travel plan

[0161] The server uses the analyzed information to crawl databases and external sources (e.g., travel booking sites and blogs) to generate itinerary suggestions, including suggested destinations, transportation options, accommodations, activities, and meals.

[0162] The generated travel plan candidates are sent back to the terminal and presented to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or "Is there a cheaper plan?"

[0163] 3. Regenerate the plan

[0164] The server can regenerate the plan as needed based on user feedback, for example, by finding more affordable accommodations or additional activities, and then regenerating and presenting the itinerary.

[0165] 4. Reservations and Electronic Payments

[0166] Once the user confirms the plan, the server checks the availability of accommodations, reserves transportation, reserves parking for the user's car, and makes reservations for activities and meals all at once. In the process of carrying out this confirmed reservation procedure, the reservation payment is made using an electronic payment service (e.g., Stripe or PayPal). This allows the user to simplify the complicated procedures and complete the reservation and payment all at once.

[0167] Hardware and software used

[0168] Hardware: Smartphones, tablets, computers

[0169] Software: Natural language processing engine (SpaCy, Google NLP API), conversational AI chat interface, travel database, electronic payment services (Stripe, PayPal)

[0170] Specific examples

[0171] For example, if a user enters "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to fly," the following steps will be executed:

[0172] 1. Natural language processing: Extract "resort," "parent and child," "three-day weekend," "under 100,000 yen," and "airplane" from the input text.

[0173] 2. Travel plan generation: Based on these conditions, search for resorts, transportation (airplane), accommodations, activities (snorkeling, beaches, etc.), and dining options.

[0174] 3. Feedback and Regeneration: Receive feedback from users and provide an even more optimal plan.

[0175] 4. Reservation and payment: Once the user confirms the plan, they enter their payment information on the terminal to complete the reservation process.

[0176] Example prompts to input to a generative AI model:

[0177] User input: I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it should be somewhere I can drive to.

[0178] AI Prompt: Enter your travel destination, budget, mode of transportation, and any specific activity or accommodation preferences.

[0179] This system allows users to easily plan all of their travel plans through a single interface, and smoothly complete reservations and payments.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] User Input

[0183] Users use an interactive AI chat interface on their smartphone or tablet to input their travel preferences. For example, they might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance." This input is sent to the device as text data.

[0184] Step 2:

[0185] Natural Language Processing

[0186] The server receives the user's input text and analyzes it using a natural language processing engine (for example, SpaCy or Google NLP API). The input is natural language text about travel preferences, and the output is structured data that extracts information such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car." This structured data is then passed on to the next step.

[0187] Step 3:

[0188] Travel plan generation

[0189] The server crawls databases and external information sources (e.g., travel booking sites and blogs) based on the structured data to generate potential itineraries. In this step, the structured data is used as input, and a potential itinerary is generated as output, including destinations, transportation options, accommodations, activities, and meals. This results in multiple potential itineraries.

[0190] Step 4:

[0191] Plan presentation

[0192] The server sends the generated itinerary candidates to the terminal and presents them to the user. The input is the data of the generated itinerary candidates, and the output is the itinerary candidates displayed on the user's terminal. The user can check it and provide feedback.

[0193] Step 5:

[0194] Feedback capture and regeneration

[0195] The user provides feedback on the proposed itinerary. For example, they may respond with, "Is there a cheaper plan?" This feedback is sent to the server. The server analyzes the received feedback and regenerates the itinerary as necessary. The input is the user's feedback and the previous itinerary candidate, and the output is the regenerated new itinerary candidate.

[0196] Step 6:

[0197] Reservation confirmed

[0198] Once the user confirms their travel plans, the server checks availability of accommodations, reserves transportation, reserves parking spaces for their car, and makes reservations for activities and meals all at once. In this step, the server receives the user's confirmed travel plans as input and generates reservation-related data as output.

[0199] Step 7:

[0200] Electronic Payment

[0201] The server uses an electronic payment service (such as Stripe or PayPal) to process payment for the reservation when the reservation is made. The input is the user's payment information and confirmed travel plans, and the output is a reservation confirmation that is notified to the user. At this point, the user can confirm that the reservation and payment have been completed.

[0202] 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.

[0203] This invention is a system that allows users to input their travel preferences via an interactive AI chat, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[0204] User Input

[0205] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I want to go on a refreshing hot spring trip with my friends next month. My budget is 100,000 yen, and I'd prefer a place that's accessible by train.").

[0206] Analysis using natural language processing

[0207] The server passes the user's input to a natural language processing (NLP) engine, which extracts necessary information from the input text (e.g., "hot spring trip," "next month," "budget of 100,000 yen," "train").

[0208] emotion recognition

[0209] Based on the extracted information, the server simultaneously uses an emotion recognition engine to determine emotions from user input and past feedback. For example, if a user uses keywords such as "I want to refresh myself" or "I'm tired," the server recognizes that relaxation is important.

[0210] Generate a plan

[0211] The server generates suitable destination, transportation, accommodation, activity, and dining suggestions based on the extracted information and emotion recognition results. Specifically, it searches for suggestions within the database and crawls external information sources (reservation sites, store homepages, blogs, etc.) to collect the latest information.

[0212] Suggestions and Feedback

[0213] The server sends the generated travel plan candidates to the terminal and presents them to the user. Based on the output of the emotion recognition engine, the server adjusts the order and content of the suggestions taking the user's emotions into account. The user can then review them and enter feedback.

[0214] regeneration

[0215] The device receives the user's feedback and sends it to the server. The server then regenerates the plan based on the feedback and emotion recognition results. For example, if the user requests a cheaper accommodation, the server will re-search and suggest cheaper accommodation options within the budget.

[0216] Emotion-based re-proposal

[0217] The server uses an emotion recognition engine to instantly detect when a user is feeling dissatisfied and automatically generate and propose alternative plans. For example, if a user emphasizes that they are "tired" or "want to relax," the server will present a plan that is more focused on relaxation.

[0218] Confirmation and booking process

[0219] After the user confirms and approves the plan, the device sends the information to the server, which then initiates the reservation process for the selected accommodation, activities, and meals. For example, the user accesses a reservation site for a hot spring inn, checks room availability, and makes a reservation, as well as other activity and meal reservations.

[0220] Providing reservation confirmation information

[0221] Once the reservation is complete, the server compiles the information and sends a confirmation message to the user, such as "Your reservation is complete. You have also secured a stay at a hot spring inn and a relaxing massage."

[0222] Specific examples

[0223] For example, if a user inputs, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere accessible by train," the server will use natural language processing and emotion recognition to determine that the user has a strong desire to refresh themselves. As a result, it will suggest a hot spring trip plan focused on relaxation (for example, including a massage and meditation session in addition to the hot springs). If the user approves, the server will complete the reservation process for that plan and provide the user with reservation confirmation information.

[0224] The system aims to enable users to easily and effectively create, confirm, and book travel plans. In addition, by utilizing an emotion recognition engine, it is possible to make suggestions that more accurately reflect the user's emotions and needs.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] The user accesses the chat interface on the device and inputs their travel wishes. For example, they might input a sentence like, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere I can get to by train."

[0228] Step 2:

[0229] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[0230] Step 3:

[0231] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[0232] Step 4:

[0233] The server uses an emotion recognition engine to determine the user's emotions based on the extracted information. For example, if keywords such as "refreshed" or "tired" are included, the server will recognize that the user values ​​relaxation.

[0234] Step 5:

[0235] The server uses the extracted information and emotion recognition results to generate suitable destination, transportation, accommodation, activity, and dining suggestions. For example, the server crawls databases and external information sources to suggest hot spring inns in Hakone, relaxing massage plans, and delicious local restaurants.

[0236] Step 6:

[0237] The server sends the generated travel plan (e.g., destination Hakone, transportation by train, accommodation at XX hot spring inn, activities at massage, meals at △△ restaurant) to the terminal and presents it to the user. Here, the server adjusts the content and order of suggestions based on the output of the emotion recognition engine, taking into account the user's emotions.

[0238] Step 7:

[0239] The user reviews the proposed plan and enters feedback, such as "Are there any other activities that fit within the budget?", into the chat interface.

[0240] Step 8:

[0241] The device receives the user's feedback and sends it to the server, which then regenerates the plan based on the feedback and emotion recognition results. For example, the server may search again for and suggest a plan that includes more relaxation activities within the budget.

[0242] Step 9:

[0243] The server uses an emotion recognition engine to instantly detect when the user is feeling dissatisfied (e.g., "I'm tired" or "I want to relax more") and automatically generates and re-proposes an alternative plan.

[0244] Step 10:

[0245] After the user finally confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, transportation, activities, and meals. For example, the server checks availability and reserves a room at a certain hot spring inn, and also makes reservations for transportation services and delicious restaurants.

[0246] Step 11:

[0247] Once the reservation is complete, the server compiles the reservation confirmation information and notifies the user, for example, sending a message saying, "Your reservation is complete. You have secured a stay at the Hakone hot spring inn and a relaxing massage."

[0248] In this way, users can create, regenerate, confirm and book travel plans quickly and efficiently using conversational AI chat and an emotion recognition engine.

[0249] Example 2

[0250] 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."

[0251] Conventional travel planning systems have difficulty making suggestions that accurately reflect the user's wishes and feelings. It has also been difficult to provide effective feedback in real time based on the information entered by the user and automatically generate and book optimal travel plans. As a result, users have had to spend a lot of time and effort planning their trips.

[0252] 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.

[0253] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the preferences using a natural language processing engine, means for determining the user's emotions using an emotion recognition engine, means for generating candidate travel plans based on the analysis and emotion recognition, means for collecting the latest information by crawling external information sources, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans. This makes it possible to generate and reserve optimal travel plans that reflect the user's preferences and emotions.

[0254] "User" refers to an individual who uses the system to input travel preferences, propose travel plans, and make reservations.

[0255] "Travel Desires" is information describing the wishes and requirements regarding the trip that the user is planning.

[0256] A "natural language processing engine" is a technology that analyzes text data entered by a user and extracts its meaning and important keywords.

[0257] An "emotion recognition engine" is a technology that determines emotions from user input and past feedback and makes appropriate suggestions based on those emotions.

[0258] "Candidate travel plans" are multiple travel plan options generated based on the user's travel wishes and emotion recognition.

[0259] "External information sources" are sources for collecting the latest information from external websites, booking sites, blogs, etc. other than the database.

[0260] "Crawling" is a technique for automatically collecting information on the web.

[0261] "Feedback" refers to opinions and requests made by the user regarding the presented travel plan.

[0262] "Reservation means" refers to a method and system for actually reserving accommodation, transportation, activities, and meals based on a confirmed plan.

[0263] This invention is a system that allows users to input their travel preferences through an interactive AI chat interface, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[0264] Hardware and software used

[0265] This system uses the following major hardware and software:

[0266] Device: The device used by the user (smartphone, tablet, PC, etc.)

[0267] Server: A cloud server for high-performance computing (e.g., AWS (registered trademark) EC2 instance)

[0268] Natural language processing engine: uses spaCy and BERT as models

[0269] Emotion recognition engine: Uses IBM Watson(R) and Microsoft(R) Azure(R) Emotion API

[0270] Database: A database for searching suitable travel plan candidates

[0271] Crawling tools: Web scraping tools such as BeautifulSoup

[0272] System Operation Overview

[0273] 1. User Input

[0274] Using the device, users input their travel preferences through an interactive AI chat interface.

[0275] Example: A user opens their smartphone and types into an interactive chat box, "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place that's accessible by train."

[0276] 2. Analysis using natural language processing

[0277] The server passes the user's input to a natural language processing engine to extract specific keywords and phrases.

[0278] Example: Extract keywords such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[0279] 3. Emotion recognition

[0280] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[0281] Example: From keywords such as "I want to refresh myself" and "I'm tired," it is determined that the user wants to relax.

[0282] 4. Generate a plan

[0283] The server generates multiple travel plan candidates based on the user's wishes and emotion recognition results.

[0284] The server accesses the database to search for accommodations including relaxation and hot springs, while simultaneously crawling the latest information from external booking sites and blogs to create a list of candidates.

[0285] 5. Suggestions and Feedback

[0286] The server sends the generated itinerary candidates to the terminal and presents them to the user, who then checks them and inputs feedback.

[0287] Example: A user checks a plan such as "Stay at a hot spring inn and get a massage package" displayed on a device and sends feedback such as "Can't it be a little cheaper?"

[0288] 6. Regeneration

[0289] The server receives the user's feedback and generates the plan again.

[0290] The server analyzes the feedback and re-searches the database to find cheaper accommodations within the budget.

[0291] 7. Emotion-Based Re-Proposal

[0292] The server uses an emotion recognition engine to instantly generate and propose alternative plans if the user is dissatisfied.

[0293] Example: In response to a user's input "I want to relax," the system generates and suggests new plans that include massages and meditation sessions.

[0294] 8. Confirmation and Booking Procedures

[0295] Once the user approves the plan, the device sends the information to the server, which then begins the reservation process.

[0296] Example: The server accesses the reservation site of a selected hot spring inn, checks room availability, and makes reservations, as well as confirms reservations for activities and meals.

[0297] 9. Providing reservation confirmation information

[0298] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[0299] Example: Generate a reservation completion notification and send a message to the user's smartphone saying, "Your reservation has been completed. Accommodation at a hot spring inn and a relaxing massage have been reserved."

[0300] This system allows a user to easily create a desired travel plan in a relatively short time and receive optimal suggestions based on their emotions. In this way, the invention can be implemented.

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Step 1:

[0303] Users use the device to input their travel preferences through an interactive AI chat interface.

[0304] Input: Text about the user's travel desires (e.g., "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place accessible by train.")

[0305] Output: Input text data

[0306] Specific behavior:

[0307] Users open the app on their smartphone and enter their travel wishes in text form into an interactive chat box.

[0308] Step 2:

[0309] The terminal receives the user's input and sends it to the server.

[0310] Input: Text data entered by the user

[0311] Output: Text data sent to the server

[0312] Specific behavior:

[0313] Once the user has completed the input, the terminal transfers the text data to the server in real time.

[0314] Step 3:

[0315] The server receives the text data and analyzes it using a natural language processing engine.

[0316] Input: User's text data

[0317] Output: Extracted keywords (e.g., "hot spring trip," "next month," "budget 100,000 yen," "train")

[0318] Data processing or data calculation:

[0319] The server uses a natural language processing engine (e.g., spaCy or BERT) to analyze the text data and extract important keywords.

[0320] Specific behavior:

[0321] The server runs an NLP engine to analyze the user's input and extract the required information.

[0322] Step 4:

[0323] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[0324] Input: Extracted keywords

[0325] Output: Determined emotion (e.g., "I want to feel refreshed")

[0326] Data processing or data calculation:

[0327] The server uses an emotion recognition engine (e.g., IBM Watson or Microsoft Azure Emotion API) to determine the user's emotions from the extracted keywords.

[0328] Specific behavior:

[0329] The server activates an emotion recognition engine and determines the emotion based on the analysis results.

[0330] Step 5:

[0331] The server generates travel plan candidates based on the determined emotions and keywords.

[0332] Input: Keywords and emotion judgment results

[0333] Output: Travel plan candidate list

[0334] Data processing or data calculation:

[0335] The server accesses the database to search for travel plans that match the user's desired conditions, and also collects additional information by crawling the latest information from external booking sites and blogs.

[0336] Specific behavior:

[0337] The server collects suitable travel plan candidates from a database or external information sources and creates a candidate list.

[0338] Step 6:

[0339] The server transmits the generated travel plan candidates to the terminal and presents them to the user.

[0340] Input: Travel plan candidate list

[0341] Output: Plan candidates presented to the user

[0342] Specific behavior:

[0343] The server transmits the list of travel plan candidates to the terminal and displays it to the user.

[0344] Step 7:

[0345] The user reviews the presented travel plan and enters feedback.

[0346] Input: Feedback text (e.g. "Can you make it a little cheaper?")

[0347] Output: Feedback data

[0348] Specific behavior:

[0349] The user checks the proposed plan on the terminal and enters feedback regarding satisfaction.

[0350] Step 8:

[0351] The terminal receives the user's feedback and transmits it to the server.

[0352] Input: User feedback data

[0353] Output: Feedback data sent to the server

[0354] Specific behavior:

[0355] Once the feedback is entered, the terminal transmits the data to the server.

[0356] Step 9:

[0357] The server regenerates the plan based on the feedback and emotion recognition results.

[0358] Input: Feedback data and emotion recognition results

[0359] Output: Regenerated itinerary candidate list

[0360] Data processing or data calculation:

[0361] The server analyzes the feedback and emotion determination, searches the database again, and generates a new plan.

[0362] Specific behavior:

[0363] The server searches for and re-proposes new plans based on the feedback.

[0364] Step 10:

[0365] The server starts the reservation procedure based on the plan approved by the user.

[0366] Input: Approved Plan

[0367] Output: Reservation procedure execution

[0368] Specific behavior:

[0369] The server accesses an external reservation site to make reservations for accommodation and transportation.

[0370] Step 11:

[0371] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[0372] Input: Reservation completion information

[0373] Output: Reservation confirmation information sent to the user

[0374] Specific behavior:

[0375] Once the reservation is complete, the server generates a confirmation message and notifies the user.

[0376] The above are the specific processing steps of this system.

[0377] (Application example 2)

[0378] 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."

[0379] While recent food delivery services allow users to easily order meals, they rarely provide menu suggestions that take into account the user's mood or emotions. Furthermore, they lack the ability to provide personalized meal plans that reflect the user's emotions and preferences, leaving users seeking improved satisfaction. Furthermore, it is difficult to reflect user feedback in real time and continually provide optimal suggestions.

[0380] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving meal preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for analyzing the user's emotions using an emotion recognition engine, means for generating meal plan candidates based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated meal plan candidates to the user, means for regenerating a plan based on user feedback, and means for executing an order based on the confirmed plan. This makes it possible to provide a personalized meal plan that suits the user's mood and preferences.

[0381] "Meal preferences entered by the user" refers to specific requests and conditions for meals provided by users using food delivery services, such as "dessert to relieve fatigue" or "healthy lunch."

[0382] A "natural language processing engine" is an artificial intelligence technology that analyzes text data entered by a user and understands its meaning and intent.

[0383] An "emotion recognition engine" is a system that analyzes and determines emotions and moods based on user input and behavior.

[0384] "Meal plan candidates" are a list of menu and restaurant suggestions that best suit the user's desires and feelings, generated based on the analysis results.

[0385] "Means of crawling external sources and collecting the latest information" refers to technologies and methods for automatically obtaining the latest data from web pages and APIs.

[0386] "User feedback" is information indicating the user's evaluation of the proposed meal plan and any additional requests.

[0387] A "means for regenerating a plan" is a system or method for regenerating a new optimal meal plan based on user feedback.

[0388] The "means for executing an order based on the confirmed plan" refers to a system or method for actually placing a food delivery order based on the meal plan approved by the user.

[0389] "Menu" means a list of dishes and / or food and drink items available for food delivery.

[0390] A "restaurant" is a food and beverage establishment that serves food.

[0391] A "dining place" is a specific place where food is eaten.

[0392] "Cooking activities" are the process of cooking and related events and experiences.

[0393] "Dessert" is a sweet dish or confectionery served at the end of a meal.

[0394] This invention is a system that provides personalized meal plans that reflect the user's preferences for food delivery. This system uses an emotion recognition engine and a natural language processing engine to analyze text data entered by the user, and generates and presents meal plans based on the latest information.

[0395] User Interface

[0396] Users use a smartphone app to input their meal preferences through an interactive AI chat interface. For example, they might input, "I'd like a dessert to soothe my tiredness." This input data is then sent to the server.

[0397] Natural Language Processing and Emotion Recognition

[0398] The server performs natural language processing using TextBlob and Hugging Face transformers packages to analyze the input text from the user. At this time, it extracts keywords (e.g., "soothing" and "dessert") from the input text and uses an emotion recognition engine to determine the user's mood and emotions (e.g., "relaxed" and "tired").

[0399] Plan generation and proposal

[0400] The server generates meal plan candidates based on the analysis results. This involves crawling external information to gather the latest menu and restaurant information. The generated meal plan candidates are optimized based on the user's mood and preferences. For example, if a user requests a "dessert that will energize them," energy bars and smoothies will be suggested.

[0401] User Feedback and Regeneration

[0402] The generated meal plan candidates are presented to the user via a smartphone app. The user reviews the suggestions and enters their feedback. This feedback is also analyzed by the emotion recognition engine, and the meal plan is regenerated as necessary. For example, if the user provides feedback such as "Is there a dessert that is a little less sweet?", a new suggestion is automatically generated.

[0403] Order confirmation and execution

[0404] Once the user approves the final meal plan, the server executes the specific order, using the delivery service's API to send the order information to the restaurant or dining establishment and confirm it.

[0405] Hardware and software used

[0406] The system includes the following hardware and software:

[0407] Hardware: User's smartphone, server

[0408] Software: TextBlob, Hugging Face transformers, Delivery Service API

[0409] Processing example

[0410] For example, the following prompt sentences are used:

[0411] "I'm looking for a quick lunch right now, any recommendations?"

[0412] "I'm tired, do you have any desserts to cheer me up?"

[0413] This allows the system to suggest personalized menus that correspond to the user's specific desires and emotions.

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] A user launches a smartphone application and inputs their meal preferences in text format through the conversational AI chat interface. At this time, the user inputs a prompt such as, "I'm tired, so do you have any desserts that will cheer me up?" The input text data is sent to the server. A specific example of input is, "I'd like to eat a refreshing lunch."

[0417] Step 2:

[0418] The server analyzes the user's input text using natural language processing engines such as TextBlob and Hugging Face transformers. During the analysis process, keywords (e.g., "dessert" or "energetic") are extracted from the input text and their meaning is understood. The input is the user's text data, and the output is the extracted keywords and their related information.

[0419] Step 3:

[0420] The server uses an emotion recognition engine to analyze the user's emotional state from the user's input text. For example, it determines that the user is feeling tired from the keyword "tired." In this process, the server determines the user's emotion based on the input text data and obtains emotional state data as output.

[0421] Step 4:

[0422] The server generates optimal meal plan candidates based on the results of natural language processing and emotion recognition. During this process, it crawls information within the database and external sources to collect the latest menu and restaurant information. For example, if a user is looking for an "energizing dessert," energy bars and smoothies are generated as candidates. The input is the analysis results, and the output is the generated meal plan candidates.

[0423] Step 5:

[0424] The server presents the generated meal plan candidates to the user through a smartphone application, adjusting the order and content of the recommended menus and restaurants based on the user's emotional state. The input is the meal plan candidates, and the output is the information presented to the user.

[0425] Step 6:

[0426] The user reviews the proposed meal plan options and enters their feedback. This feedback can include specific requests and ratings, such as "Is there a cheaper option?" or "I don't like this menu." The input is the user's feedback data, and the output is that feedback information.

[0427] Step 7:

[0428] The server analyzes the user's feedback and reconstructs the meal plan using an emotion recognition engine and a reconstruction engine. The reconstructed plan is adjusted to meet the user's additional requests, for example, by re-suggesting cheaper options or different menu items. The input is the feedback data, and the output is a reconstructed meal plan candidate.

[0429] Step 8:

[0430] Once the user approves the final meal plan, the server uses the delivery service API to place the order. The order information is sent to the restaurant or food establishment and the process is confirmed. The input is the confirmed meal plan information, and the output is order confirmation data.

[0431] Step 9:

[0432] Once the order is complete, the server compiles the information and sends a confirmation to the user via a smartphone application. For example, it sends a message saying, "Your order is complete! Your energizing smoothie will be delivered shortly." The input is the order result data, and the output is the notification data to the user.

[0433] 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.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0435] 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.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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."

[0449] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and books all destinations, transportation, accommodation, activities, and meals all at once.

[0450] User Input

[0451] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance.").

[0452] Analysis using natural language processing

[0453] The server uses a natural language processing (NLP) engine to analyze the input text and extract necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," and "car").

[0454] Generate a plan

[0455] The server generates travel plan suggestions based on the extracted information. Specifically, the server searches a database for possible destinations, transportation options, accommodations, activities, and meals. It also crawls external information sources (reservation sites, individual store homepages, personal blogs, etc.) to collect the latest information.

[0456] Suggestions and Feedback

[0457] The server sends the generated travel plan candidates to the terminal and presents them to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or, "Do you have a cheaper plan?"

[0458] regeneration

[0459] The server receives user feedback and regenerates the plan as necessary. For example, it can search for more reasonably priced accommodation and propose a new plan.

[0460] Confirmation and booking process

[0461] Once the user confirms the plan, the server handles the reservation process on their behalf, specifically checking availability of accommodations, making reservations, securing parking spaces for their car, and making reservations for activities and meals.

[0462] Providing reservation confirmation information

[0463] Once the reservation is complete, the server compiles the information and notifies the user, for example, by sending a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Onsen Ryokan have been reserved."

[0464] Specific examples

[0465] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate and suggest travel destinations (e.g., Okinawa), transportation methods (airplane), accommodations (resort hotels), activities (snorkeling, beaches, etc.), and meals (restaurants within the resort). Once the user confirms and approves the plan, the server will complete the reservation process and notify the user of the completed information.

[0466] The system allows users to effortlessly create comprehensive travel plans and complete reservations efficiently.

[0467] The processing flow will be explained below.

[0468] Step 1:

[0469] The user inputs their travel wishes through the chat interface on the device. For example, they might input a sentence like, "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0470] Step 2:

[0471] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[0472] Step 3:

[0473] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car."

[0474] Step 4:

[0475] The server uses the extracted information to search for suitable destinations, transportation options, accommodations, activities, and dining options. In addition to the information in the database, it also crawls external information sources (reservation sites, individual store homepages, blogs, etc.) to collect the latest information.

[0476] Step 5:

[0477] The server generates a travel plan including possible destinations (e.g., Hakone), transportation options (private car), accommodations (XX hot spring inn), activities (hot springs, massage), and meals (△△ soba restaurant).

[0478] Step 6:

[0479] The server sends the generated travel plan to the terminal and presents it to the user, who then checks the plan and enters feedback.

[0480] Step 7:

[0481] The device receives the user's feedback and sends it to the server. The server regenerates the plan based on the feedback. For example, if the user requests a cheaper plan, the server will search for and suggest cheaper accommodations within the user's budget.

[0482] Step 8:

[0483] After the user confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, activities, and meals. For example, the user accesses the reservation website for a certain hot spring inn, checks room availability, and confirms the reservation.

[0484] Step 9:

[0485] The server verifies that the reservations are complete and aggregates all reservations to generate a reservation confirmation, which includes accommodation confirmation, activity reservation numbers, meal reservation details, etc.

[0486] Step 10:

[0487] The server sends the reservation confirmation information to the terminal and notifies the user, who receives the confirmation message through the terminal and can reconfirm or change the reservation as necessary.

[0488] In this way, users can efficiently create, confirm, and book travel plans using interactive AI chat.

[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] Planning and booking a trip can be a time-consuming process, requiring users to check multiple websites and services. This wastes time and makes it difficult for users to find the best plan. The objective of this invention is to provide a system that allows users to easily and efficiently plan a trip and make reservations all at once.

[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 means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling databases and external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans, thereby enabling users to efficiently and easily plan their trips and make reservations all at once.

[0494] "User" means any person or entity that uses the System to plan and book travel.

[0495] A "terminal" is a device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0496] The "interactive AI chat interface" is a user interface that allows users to input their travel preferences in natural language and interact with the system.

[0497] A "natural language processing engine" is a technology used to analyze user input and extract important information. Specifically, it is a text analysis technology.

[0498] "Candidate travel plans" are multiple travel plan options generated based on user input.

[0499] A "database" is an information collection that stores travel-related information and is a source of information that the server accesses to generate potential travel plans.

[0500] "Crawling external sources" refers to the process of automatically searching public sources on the Internet to gather the latest information.

[0501] "Feedback" refers to the user's evaluation or opinion on the proposed travel plans.

[0502] "Booking Process" means the process by which a reservation is made for some or all elements of a trip (e.g., accommodation, transportation, activities, meals, etc.).

[0503] "Booking in one go" means completing the booking for all elements of a travel plan in a single operation.

[0504] This invention is a system that can propose and book travel plans in one go based on travel preferences entered by a user. The system includes a server, a terminal, and a natural language processing engine that analyzes user input.

[0505] User Input

[0506] The device can be a smartphone, tablet, or personal computer. This device is equipped with an interactive AI chat interface, which allows users to input their travel preferences in natural language. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0507] Analysis using natural language processing

[0508] The server analyzes the input text received from the user using a natural language processing engine (e.g., Google NLP, spaCy) to extract necessary information, such as the type of trip, budget, and preferred means of transportation.

[0509] Generate a plan

[0510] The server generates a candidate itinerary based on the extracted information. At this stage, the server searches for relevant information from a database (e.g., MySQL, MongoDB) and also uses a web crawler (e.g., Beautiful Soup, Scrapy) to gather up-to-date information from external sources. The generated candidate itinerary includes suggestions for destinations, transportation, accommodations, activities, and meals.

[0511] Suggestions and Feedback

[0512] The server sends the generated travel plan candidates to the terminal. The user can review the plans through the terminal and provide feedback. For example, feedback such as "I'd like that plan," or "Is there a cheaper plan?" is possible.

[0513] regeneration

[0514] The server receives user feedback and regenerates plans as needed. For example, if a user requests a lower budget, the server will search again and present a more reasonable plan.

[0515] Confirmation and booking process

[0516] Once the user confirms their plan, the server handles all the booking procedures, including checking availability of accommodation, reserving a parking space for the user's car, and booking activities and meals.

[0517] Providing reservation confirmation information

[0518] Once the reservation procedure is complete, the server will send a summary of the reservation confirmation information to the user, such as a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Hot Springs Inn have been reserved."

[0519] Specific examples

[0520] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate a travel plan with Okinawa as the destination, suggesting airplanes, resort hotels, snorkeling and beach activities, restaurants within the resort, etc. Once the user confirms and approves the plan, the server will carry out all reservation procedures and notify the user of the completed information.

[0521] This system allows users to plan their trips in one place and complete reservations efficiently, significantly reducing the amount of work required in the planning stage of a trip.

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] A user opens a conversational AI chat interface using a device

[0525] A user opens an interactive AI chat interface using a smartphone, tablet, or personal computer. The interface is provided through a browser or a dedicated application. The input is the details of the user's desired trip (e.g., "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."), and the output is that this information is sent to the server.

[0526] Step 2:

[0527] The server receives the user's input

[0528] The server receives the user's input text through an HTTP request, which is stored on the server and passed to the next processing step. The input is the user's desired travel details, and the output is the user's desired data in text format.

[0529] Step 3:

[0530] The server launches a natural language processing engine to analyze the input text.

[0531] The server launches a natural language processing engine (e.g., Google NLP, spaCy) and analyzes the user's input text. The analysis extracts the necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," "car"). The input is the user's desired data in text format, and the output is the analyzed information (a list of keywords and phrases).

[0532] Step 4:

[0533] The server generates travel plan candidates

[0534] The server generates itinerary suggestions based on the extracted information. Specifically, it searches for relevant information from databases (e.g., MySQL, MongoDB) and collects up-to-date information from external sources using web crawlers (e.g., Beautiful Soup, Scrapy). The input is the parsed information, and the output is the generated itinerary suggestions (suggestions for destinations, transportation, accommodation, activities, and meals).

[0535] Step 5:

[0536] The server sends the generated travel plan candidates to the terminal.

[0537] The server sends the generated travel plan candidates to the terminal in JSON format via the REST API. The input is the travel plan candidates, and the output is the travel plan information sent to the terminal.

[0538] Step 6:

[0539] The device displays the proposed travel plan to the user.

[0540] The terminal displays the received travel plan candidates on a user interface. The user can review the plans and provide feedback. The input is the travel plan information sent to the terminal, and the output is the user's feedback.

[0541] Step 7:

[0542] The server receives user feedback and regenerates the plan as needed.

[0543] The server receives feedback from the user and regenerates the itinerary as needed, for example, re-searching for reasonably priced accommodations and generating a new itinerary. The input is the user's feedback, and the output is the regenerated itinerary candidates.

[0544] Step 8:

[0545] Once the user confirms the plan, the server starts the reservation process.

[0546] Once the user finalizes their travel plans, the server initiates the reservation process, including checking and reserving accommodations, reserving parking spaces for their vehicle, and booking activities and meals. The input is the finalized travel plans, and the output is reservation confirmation information.

[0547] Step 9:

[0548] The server collects the reservation confirmation information and notifies the user.

[0549] Once the reservation procedure is completed, the server sends the reservation confirmation information to the user via email or in-app notification. The input is the reservation confirmation information, and the output is a notification message to the user.

[0550] (Application example 1)

[0551] 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."

[0552] Modern travel planning is problematic because it requires a lot of time and effort. It is particularly cumbersome for users, as it requires separate reservations for destinations, transportation, accommodations, activities, and meals. Furthermore, various payments must be made each time a reservation is made, often complicating the process. For this reason, there is a demand for a system that automatically proposes comprehensive travel plans and smoothly handles everything from reservations to payments.

[0553] 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.

[0554] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating a plan based on user feedback, means for making a reservation based on the confirmed plan, and means for making an electronic payment when making the reservation. This allows the user to plan, book, and pay for all of their travel plans through a single interface.

[0555] "User input" is text data that describes travel wishes and conditions in natural language.

[0556] A "natural language processing engine" is a software system that analyzes user input and extracts requirements and information from it.

[0557] "Candidate itineraries" are suggested options for travel destinations, transportation, accommodations, activities, meals, etc., based on analyzed user input.

[0558] "External sources" refers to the latest information collected from various websites, databases, APIs, etc. on the Internet.

[0559] "Crawling" refers to the process of automatically collecting data from external sources over the Internet.

[0560] "User feedback" refers to the evaluation and additional information provided by the user regarding the proposed travel plan.

[0561] "Regeneration" is the process of recreating a travel plan based on user feedback.

[0562] "Reservation Facility" means a system or process that fulfills reservations for accommodation, transportation, activities, and meals according to a confirmed travel plan.

[0563] "Electronic payment" refers to online payment procedures using credit cards, digital wallets, etc.

[0564] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and reserves destinations, transportation, accommodations, activities, and meals all at once. This system is composed of users, a server, and terminals.

[0565] 1. User Input and Natural Language Processing

[0566] First, a user uses a device such as a smartphone or tablet to input their travel preferences into a conversational AI chat interface. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's somewhere I can drive to." This interface runs on the device.

[0567] The server analyzes the user's input using a natural language processing engine (e.g., SpaCy or Google NLP API) and extracts information such as the travel destination, budget, transportation method, and duration.

[0568] 2. Generate a travel plan

[0569] The server uses the analyzed information to crawl databases and external sources (e.g., travel booking sites and blogs) to generate itinerary suggestions, including suggested destinations, transportation options, accommodations, activities, and meals.

[0570] The generated travel plan candidates are sent back to the terminal and presented to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or "Is there a cheaper plan?"

[0571] 3. Regenerate the plan

[0572] The server can regenerate the plan as needed based on user feedback, for example, by finding more affordable accommodations or additional activities, and then regenerating and presenting the itinerary.

[0573] 4. Reservations and Electronic Payments

[0574] Once the user confirms the plan, the server checks the availability of accommodations, reserves transportation, reserves parking for the user's car, and makes reservations for activities and meals all at once. In the process of carrying out this confirmed reservation procedure, the reservation payment is made using an electronic payment service (e.g., Stripe or PayPal). This allows the user to simplify the complicated procedures and complete the reservation and payment all at once.

[0575] Hardware and software used

[0576] Hardware: Smartphones, tablets, computers

[0577] Software: Natural language processing engine (SpaCy, Google NLP API), conversational AI chat interface, travel database, electronic payment services (Stripe, PayPal)

[0578] Specific examples

[0579] For example, if a user enters "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to fly," the following steps will be executed:

[0580] 1. Natural language processing: Extract "resort," "parent and child," "three-day weekend," "under 100,000 yen," and "airplane" from the input text.

[0581] 2. Travel plan generation: Based on these conditions, search for resorts, transportation (airplane), accommodations, activities (snorkeling, beaches, etc.), and dining options.

[0582] 3. Feedback and Regeneration: Receive feedback from users and provide an even more optimal plan.

[0583] 4. Reservation and payment: Once the user confirms the plan, they enter their payment information on the terminal to complete the reservation process.

[0584] Example prompts to input to a generative AI model:

[0585] User input: I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it should be somewhere I can drive to.

[0586] AI Prompt: Enter your travel destination, budget, mode of transportation, and any specific activity or accommodation preferences.

[0587] This system allows users to easily plan all of their travel plans through a single interface, and smoothly complete reservations and payments.

[0588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0589] Step 1:

[0590] User Input

[0591] Users use an interactive AI chat interface on their smartphone or tablet to input their travel preferences. For example, they might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance." This input is sent to the device as text data.

[0592] Step 2:

[0593] Natural Language Processing

[0594] The server receives the user's input text and analyzes it using a natural language processing engine (for example, SpaCy or Google NLP API). The input is natural language text about travel preferences, and the output is structured data that extracts information such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car." This structured data is then passed on to the next step.

[0595] Step 3:

[0596] Travel plan generation

[0597] The server crawls databases and external information sources (e.g., travel booking sites and blogs) based on the structured data to generate potential itineraries. In this step, the structured data is used as input, and a potential itinerary is generated as output, including destinations, transportation options, accommodations, activities, and meals. This results in multiple potential itineraries.

[0598] Step 4:

[0599] Plan presentation

[0600] The server sends the generated itinerary candidates to the terminal and presents them to the user. The input is the data of the generated itinerary candidates, and the output is the itinerary candidates displayed on the user's terminal. The user can check it and provide feedback.

[0601] Step 5:

[0602] Feedback capture and regeneration

[0603] The user provides feedback on the proposed itinerary. For example, they may respond with, "Is there a cheaper plan?" This feedback is sent to the server. The server analyzes the received feedback and regenerates the itinerary as necessary. The input is the user's feedback and the previous itinerary candidate, and the output is the regenerated new itinerary candidate.

[0604] Step 6:

[0605] Reservation confirmed

[0606] Once the user confirms their travel plans, the server checks availability of accommodations, reserves transportation, reserves parking spaces for their car, and makes reservations for activities and meals all at once. In this step, the server receives the user's confirmed travel plans as input and generates reservation-related data as output.

[0607] Step 7:

[0608] Electronic Payment

[0609] The server uses an electronic payment service (such as Stripe or PayPal) to process payment for the reservation when the reservation is made. The input is the user's payment information and confirmed travel plans, and the output is a reservation confirmation that is notified to the user. At this point, the user can confirm that the reservation and payment have been completed.

[0610] 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.

[0611] This invention is a system that allows users to input their travel preferences via an interactive AI chat, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[0612] User Input

[0613] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I want to go on a refreshing hot spring trip with my friends next month. My budget is 100,000 yen, and I'd prefer a place that's accessible by train.").

[0614] Analysis using natural language processing

[0615] The server passes the user's input to a natural language processing (NLP) engine, which extracts necessary information from the input text (e.g., "hot spring trip," "next month," "budget of 100,000 yen," "train").

[0616] emotion recognition

[0617] Based on the extracted information, the server simultaneously uses an emotion recognition engine to determine emotions from user input and past feedback. For example, if a user uses keywords such as "I want to refresh myself" or "I'm tired," the server recognizes that relaxation is important.

[0618] Generate a plan

[0619] The server generates suitable destination, transportation, accommodation, activity, and dining suggestions based on the extracted information and emotion recognition results. Specifically, it searches for suggestions within the database and crawls external information sources (reservation sites, store homepages, blogs, etc.) to collect the latest information.

[0620] Suggestions and Feedback

[0621] The server sends the generated travel plan candidates to the terminal and presents them to the user. Based on the output of the emotion recognition engine, the server adjusts the order and content of the suggestions taking the user's emotions into account. The user can then review them and enter feedback.

[0622] regeneration

[0623] The device receives the user's feedback and sends it to the server. The server then regenerates the plan based on the feedback and emotion recognition results. For example, if the user requests a cheaper accommodation, the server will re-search and suggest cheaper accommodation options within the budget.

[0624] Emotion-based re-proposal

[0625] The server uses an emotion recognition engine to instantly detect when a user is feeling dissatisfied and automatically generate and propose alternative plans. For example, if a user emphasizes that they are "tired" or "want to relax," the server will present a plan that is more focused on relaxation.

[0626] Confirmation and booking process

[0627] After the user confirms and approves the plan, the device sends the information to the server, which then initiates the reservation process for the selected accommodation, activities, and meals. For example, the user accesses a reservation site for a hot spring inn, checks room availability, and makes a reservation, as well as other activity and meal reservations.

[0628] Providing reservation confirmation information

[0629] Once the reservation is complete, the server compiles the information and sends a confirmation message to the user, such as "Your reservation is complete. You have also secured a stay at a hot spring inn and a relaxing massage."

[0630] Specific examples

[0631] For example, if a user inputs, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere accessible by train," the server will use natural language processing and emotion recognition to determine that the user has a strong desire to refresh themselves. As a result, it will suggest a hot spring trip plan focused on relaxation (for example, including a massage and meditation session in addition to the hot springs). If the user approves, the server will complete the reservation process for that plan and provide the user with reservation confirmation information.

[0632] The system aims to enable users to easily and effectively create, confirm, and book travel plans. In addition, by utilizing an emotion recognition engine, it is possible to make suggestions that more accurately reflect the user's emotions and needs.

[0633] The processing flow will be explained below.

[0634] Step 1:

[0635] The user accesses the chat interface on the device and inputs their travel wishes. For example, they might input a sentence like, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere I can get to by train."

[0636] Step 2:

[0637] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[0638] Step 3:

[0639] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[0640] Step 4:

[0641] The server uses an emotion recognition engine to determine the user's emotions based on the extracted information. For example, if keywords such as "refreshed" or "tired" are included, the server will recognize that the user values ​​relaxation.

[0642] Step 5:

[0643] The server uses the extracted information and emotion recognition results to generate suitable destination, transportation, accommodation, activity, and dining suggestions. For example, the server crawls databases and external information sources to suggest hot spring inns in Hakone, relaxing massage plans, and delicious local restaurants.

[0644] Step 6:

[0645] The server sends the generated travel plan (e.g., destination Hakone, transportation by train, accommodation at XX hot spring inn, activities at massage, meals at △△ restaurant) to the terminal and presents it to the user. Here, the server adjusts the content and order of suggestions based on the output of the emotion recognition engine, taking into account the user's emotions.

[0646] Step 7:

[0647] The user reviews the proposed plan and enters feedback, such as "Are there any other activities that fit within the budget?", into the chat interface.

[0648] Step 8:

[0649] The device receives the user's feedback and sends it to the server, which then regenerates the plan based on the feedback and emotion recognition results. For example, the server may search again for and suggest a plan that includes more relaxation activities within the budget.

[0650] Step 9:

[0651] The server uses an emotion recognition engine to instantly detect when the user is feeling dissatisfied (e.g., "I'm tired" or "I want to relax more") and automatically generates and re-proposes an alternative plan.

[0652] Step 10:

[0653] After the user finally confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, transportation, activities, and meals. For example, the server checks availability and reserves a room at a certain hot spring inn, and also makes reservations for transportation services and delicious restaurants.

[0654] Step 11:

[0655] Once the reservation is complete, the server compiles the reservation confirmation information and notifies the user, for example, sending a message saying, "Your reservation is complete. You have secured a stay at the Hakone hot spring inn and a relaxing massage."

[0656] In this way, users can create, regenerate, confirm and book travel plans quickly and efficiently using conversational AI chat and an emotion recognition engine.

[0657] Example 2

[0658] 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."

[0659] Conventional travel planning systems have difficulty making suggestions that accurately reflect the user's wishes and feelings. It has also been difficult to provide effective feedback in real time based on the information entered by the user and automatically generate and book optimal travel plans. As a result, users have had to spend a lot of time and effort planning their trips.

[0660] 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.

[0661] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the preferences using a natural language processing engine, means for determining the user's emotions using an emotion recognition engine, means for generating candidate travel plans based on the analysis and emotion recognition, means for collecting the latest information by crawling external information sources, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans. This makes it possible to generate and reserve optimal travel plans that reflect the user's preferences and emotions.

[0662] "User" refers to an individual who uses the system to input travel preferences, propose travel plans, and make reservations.

[0663] "Travel Desires" is information describing the wishes and requirements regarding the trip that the user is planning.

[0664] A "natural language processing engine" is a technology that analyzes text data entered by a user and extracts its meaning and important keywords.

[0665] An "emotion recognition engine" is a technology that determines emotions from user input and past feedback and makes appropriate suggestions based on those emotions.

[0666] "Candidate travel plans" are multiple travel plan options generated based on the user's travel wishes and emotion recognition.

[0667] "External information sources" are sources for collecting the latest information from external websites, booking sites, blogs, etc. other than the database.

[0668] "Crawling" is a technique for automatically collecting information on the web.

[0669] "Feedback" refers to opinions and requests made by the user regarding the presented travel plan.

[0670] "Reservation means" refers to a method and system for actually reserving accommodation, transportation, activities, and meals based on a confirmed plan.

[0671] This invention is a system that allows users to input their travel preferences through an interactive AI chat interface, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[0672] Hardware and software used

[0673] This system uses the following major hardware and software:

[0674] Device: The device used by the user (smartphone, tablet, PC, etc.)

[0675] Server: Cloud server for high performance computing (e.g. AWS EC2 instance)

[0676] Natural language processing engine: uses spaCy and BERT as models

[0677] Emotion recognition engine: Uses IBM Watson and Microsoft Azure Emotion API

[0678] Database: A database for searching suitable travel plan candidates

[0679] Crawling tools: Web scraping tools such as BeautifulSoup

[0680] System Operation Overview

[0681] 1. User Input

[0682] Using the device, users input their travel preferences through an interactive AI chat interface.

[0683] Example: A user opens their smartphone and types into an interactive chat box, "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place that's accessible by train."

[0684] 2. Analysis using natural language processing

[0685] The server passes the user's input to a natural language processing engine to extract specific keywords and phrases.

[0686] Example: Extract keywords such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[0687] 3. Emotion recognition

[0688] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[0689] Example: From keywords such as "I want to refresh myself" and "I'm tired," it is determined that the user wants to relax.

[0690] 4. Generate a plan

[0691] The server generates multiple travel plan candidates based on the user's wishes and emotion recognition results.

[0692] The server accesses the database to search for accommodations including relaxation and hot springs, while simultaneously crawling the latest information from external booking sites and blogs to create a list of candidates.

[0693] 5. Suggestions and Feedback

[0694] The server sends the generated itinerary candidates to the terminal and presents them to the user, who then checks them and inputs feedback.

[0695] Example: A user checks a plan such as "Stay at a hot spring inn and get a massage package" displayed on a device and sends feedback such as "Can't it be a little cheaper?"

[0696] 6. Regeneration

[0697] The server receives the user's feedback and generates the plan again.

[0698] The server analyzes the feedback and re-searches the database to find cheaper accommodations within the budget.

[0699] 7. Emotion-Based Re-Proposal

[0700] The server uses an emotion recognition engine to instantly generate and propose alternative plans if the user is dissatisfied.

[0701] Example: In response to a user's input "I want to relax," the system generates and suggests new plans that include massages and meditation sessions.

[0702] 8. Confirmation and Booking Procedures

[0703] Once the user approves the plan, the device sends the information to the server, which then begins the reservation process.

[0704] Example: The server accesses the reservation site of a selected hot spring inn, checks room availability, and makes reservations, as well as confirms reservations for activities and meals.

[0705] 9. Providing reservation confirmation information

[0706] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[0707] Example: Generate a reservation completion notification and send a message to the user's smartphone saying, "Your reservation has been completed. Accommodation at a hot spring inn and a relaxing massage have been reserved."

[0708] This system allows a user to easily create a desired travel plan in a relatively short time and receive optimal suggestions based on their emotions. In this way, the invention can be implemented.

[0709] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0710] Step 1:

[0711] Users use the device to input their travel preferences through an interactive AI chat interface.

[0712] Input: Text about the user's travel desires (e.g., "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place accessible by train.")

[0713] Output: Input text data

[0714] Specific behavior:

[0715] Users open the app on their smartphone and enter their travel wishes in text form into an interactive chat box.

[0716] Step 2:

[0717] The terminal receives the user's input and sends it to the server.

[0718] Input: Text data entered by the user

[0719] Output: Text data sent to the server

[0720] Specific behavior:

[0721] Once the user has completed the input, the terminal transfers the text data to the server in real time.

[0722] Step 3:

[0723] The server receives the text data and analyzes it using a natural language processing engine.

[0724] Input: User's text data

[0725] Output: Extracted keywords (e.g., "hot spring trip," "next month," "budget 100,000 yen," "train")

[0726] Data processing or data calculation:

[0727] The server uses a natural language processing engine (e.g., spaCy or BERT) to analyze the text data and extract important keywords.

[0728] Specific behavior:

[0729] The server runs an NLP engine to analyze the user's input and extract the required information.

[0730] Step 4:

[0731] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[0732] Input: Extracted keywords

[0733] Output: Determined emotion (e.g., "I want to feel refreshed")

[0734] Data processing or data calculation:

[0735] The server uses an emotion recognition engine (e.g., IBM Watson or Microsoft Azure Emotion API) to determine the user's emotions from the extracted keywords.

[0736] Specific behavior:

[0737] The server activates an emotion recognition engine and determines the emotion based on the analysis results.

[0738] Step 5:

[0739] The server generates travel plan candidates based on the determined emotions and keywords.

[0740] Input: Keywords and emotion judgment results

[0741] Output: Travel plan candidate list

[0742] Data processing or data calculation:

[0743] The server accesses the database to search for travel plans that match the user's desired conditions, and also collects additional information by crawling the latest information from external booking sites and blogs.

[0744] Specific behavior:

[0745] The server collects suitable travel plan candidates from a database or external information sources and creates a candidate list.

[0746] Step 6:

[0747] The server transmits the generated travel plan candidates to the terminal and presents them to the user.

[0748] Input: Travel plan candidate list

[0749] Output: Plan candidates presented to the user

[0750] Specific behavior:

[0751] The server transmits the list of travel plan candidates to the terminal and displays it to the user.

[0752] Step 7:

[0753] The user reviews the presented travel plan and enters feedback.

[0754] Input: Feedback text (e.g. "Can you make it a little cheaper?")

[0755] Output: Feedback data

[0756] Specific behavior:

[0757] The user checks the proposed plan on the terminal and enters feedback regarding satisfaction.

[0758] Step 8:

[0759] The terminal receives the user's feedback and transmits it to the server.

[0760] Input: User feedback data

[0761] Output: Feedback data sent to the server

[0762] Specific behavior:

[0763] Once the feedback is entered, the terminal transmits the data to the server.

[0764] Step 9:

[0765] The server regenerates the plan based on the feedback and emotion recognition results.

[0766] Input: Feedback data and emotion recognition results

[0767] Output: Regenerated itinerary candidate list

[0768] Data processing or data calculation:

[0769] The server analyzes the feedback and emotion determination, searches the database again, and generates a new plan.

[0770] Specific behavior:

[0771] The server searches for and re-proposes new plans based on the feedback.

[0772] Step 10:

[0773] The server starts the reservation procedure based on the plan approved by the user.

[0774] Input: Approved Plan

[0775] Output: Reservation procedure execution

[0776] Specific behavior:

[0777] The server accesses an external reservation site to make reservations for accommodation and transportation.

[0778] Step 11:

[0779] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[0780] Input: Reservation completion information

[0781] Output: Reservation confirmation information sent to the user

[0782] Specific behavior:

[0783] Once the reservation is complete, the server generates a confirmation message and notifies the user.

[0784] The above are the specific processing steps of this system.

[0785] (Application example 2)

[0786] 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."

[0787] While recent food delivery services allow users to easily order meals, they rarely provide menu suggestions that take into account the user's mood or emotions. Furthermore, they lack the ability to provide personalized meal plans that reflect the user's emotions and preferences, leaving users seeking improved satisfaction. Furthermore, it is difficult to reflect user feedback in real time and continually provide optimal suggestions.

[0788] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving meal preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for analyzing the user's emotions using an emotion recognition engine, means for generating meal plan candidates based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated meal plan candidates to the user, means for regenerating a plan based on user feedback, and means for executing an order based on the confirmed plan. This makes it possible to provide a personalized meal plan that suits the user's mood and preferences.

[0789] "Meal preferences entered by the user" refers to specific requests and conditions for meals provided by users using food delivery services, such as "dessert to relieve fatigue" or "healthy lunch."

[0790] A "natural language processing engine" is an artificial intelligence technology that analyzes text data entered by a user and understands its meaning and intent.

[0791] An "emotion recognition engine" is a system that analyzes and determines emotions and moods based on user input and behavior.

[0792] "Meal plan candidates" are a list of menu and restaurant suggestions that best suit the user's desires and feelings, generated based on the analysis results.

[0793] "Means of crawling external sources and collecting the latest information" refers to technologies and methods for automatically obtaining the latest data from web pages and APIs.

[0794] "User feedback" is information indicating the user's evaluation of the proposed meal plan and any additional requests.

[0795] A "means for regenerating a plan" is a system or method for regenerating a new optimal meal plan based on user feedback.

[0796] The "means for executing an order based on the confirmed plan" refers to a system or method for actually placing a food delivery order based on the meal plan approved by the user.

[0797] "Menu" means a list of dishes and / or food and drink items available for food delivery.

[0798] A "restaurant" is a food and beverage establishment that serves food.

[0799] A "dining place" is a specific place where food is eaten.

[0800] "Cooking activities" are the process of cooking and related events and experiences.

[0801] "Dessert" is a sweet dish or confectionery served at the end of a meal.

[0802] This invention is a system that provides personalized meal plans that reflect the user's preferences for food delivery. This system uses an emotion recognition engine and a natural language processing engine to analyze text data entered by the user, and generates and presents meal plans based on the latest information.

[0803] User Interface

[0804] Users use a smartphone app to input their meal preferences through an interactive AI chat interface. For example, they might input, "I'd like a dessert to soothe my tiredness." This input data is then sent to the server.

[0805] Natural Language Processing and Emotion Recognition

[0806] The server performs natural language processing using TextBlob and Hugging Face transformers packages to analyze the input text from the user. At this time, it extracts keywords (e.g., "soothing" and "dessert") from the input text and uses an emotion recognition engine to determine the user's mood and emotions (e.g., "relaxed" and "tired").

[0807] Plan generation and proposal

[0808] The server generates meal plan candidates based on the analysis results. This involves crawling external information to gather the latest menu and restaurant information. The generated meal plan candidates are optimized based on the user's mood and preferences. For example, if a user requests a "dessert that will energize them," energy bars and smoothies will be suggested.

[0809] User Feedback and Regeneration

[0810] The generated meal plan candidates are presented to the user via a smartphone app. The user reviews the suggestions and enters their feedback. This feedback is also analyzed by the emotion recognition engine, and the meal plan is regenerated as necessary. For example, if the user provides feedback such as "Is there a dessert that is a little less sweet?", a new suggestion is automatically generated.

[0811] Order confirmation and execution

[0812] Once the user approves the final meal plan, the server executes the specific order, using the delivery service's API to send the order information to the restaurant or dining establishment and confirm it.

[0813] Hardware and software used

[0814] The system includes the following hardware and software:

[0815] Hardware: User's smartphone, server

[0816] Software: TextBlob, Hugging Face transformers, Delivery Service API

[0817] Processing example

[0818] For example, the following prompt sentences are used:

[0819] "I'm looking for a quick lunch right now, any recommendations?"

[0820] "I'm tired, do you have any desserts to cheer me up?"

[0821] This allows the system to suggest personalized menus that correspond to the user's specific desires and emotions.

[0822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0823] Step 1:

[0824] A user launches a smartphone application and inputs their meal preferences in text format through the conversational AI chat interface. At this time, the user inputs a prompt such as, "I'm tired, so do you have any desserts that will cheer me up?" The input text data is sent to the server. A specific example of input is, "I'd like to eat a refreshing lunch."

[0825] Step 2:

[0826] The server analyzes the user's input text using natural language processing engines such as TextBlob and Hugging Face transformers. During the analysis process, keywords (e.g., "dessert" or "energetic") are extracted from the input text and their meaning is understood. The input is the user's text data, and the output is the extracted keywords and their related information.

[0827] Step 3:

[0828] The server uses an emotion recognition engine to analyze the user's emotional state from the user's input text. For example, it determines that the user is feeling tired from the keyword "tired." In this process, the server determines the user's emotion based on the input text data and obtains emotional state data as output.

[0829] Step 4:

[0830] The server generates optimal meal plan candidates based on the results of natural language processing and emotion recognition. During this process, it crawls information within the database and external sources to collect the latest menu and restaurant information. For example, if a user is looking for an "energizing dessert," energy bars and smoothies are generated as candidates. The input is the analysis results, and the output is the generated meal plan candidates.

[0831] Step 5:

[0832] The server presents the generated meal plan candidates to the user through a smartphone application, adjusting the order and content of the recommended menus and restaurants based on the user's emotional state. The input is the meal plan candidates, and the output is the information presented to the user.

[0833] Step 6:

[0834] The user reviews the proposed meal plan options and enters their feedback. This feedback can include specific requests and ratings, such as "Is there a cheaper option?" or "I don't like this menu." The input is the user's feedback data, and the output is that feedback information.

[0835] Step 7:

[0836] The server analyzes the user's feedback and reconstructs the meal plan using an emotion recognition engine and a reconstruction engine. The reconstructed plan is adjusted to meet the user's additional requests, for example, by re-suggesting cheaper options or different menu items. The input is the feedback data, and the output is a reconstructed meal plan candidate.

[0837] Step 8:

[0838] Once the user approves the final meal plan, the server uses the delivery service API to place the order. The order information is sent to the restaurant or food establishment and the process is confirmed. The input is the confirmed meal plan information, and the output is order confirmation data.

[0839] Step 9:

[0840] Once the order is complete, the server compiles the information and sends a confirmation to the user via a smartphone application. For example, it sends a message saying, "Your order is complete! Your energizing smoothie will be delivered shortly." The input is the order result data, and the output is the notification data to the user.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] [Third embodiment]

[0845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0846] 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.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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).

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and books all destinations, transportation, accommodation, activities, and meals all at once.

[0858] User Input

[0859] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance.").

[0860] Analysis using natural language processing

[0861] The server uses a natural language processing (NLP) engine to analyze the input text and extract necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," and "car").

[0862] Generate a plan

[0863] The server generates travel plan suggestions based on the extracted information. Specifically, the server searches a database for possible destinations, transportation options, accommodations, activities, and meals. It also crawls external information sources (reservation sites, individual store homepages, personal blogs, etc.) to collect the latest information.

[0864] Suggestions and Feedback

[0865] The server sends the generated travel plan candidates to the terminal and presents them to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or, "Do you have a cheaper plan?"

[0866] regeneration

[0867] The server receives user feedback and regenerates the plan as necessary. For example, it can search for more reasonably priced accommodation and propose a new plan.

[0868] Confirmation and booking process

[0869] Once the user confirms the plan, the server handles the reservation process on their behalf, specifically checking availability of accommodations, making reservations, securing parking spaces for their car, and making reservations for activities and meals.

[0870] Providing reservation confirmation information

[0871] Once the reservation is complete, the server compiles the information and notifies the user, for example, by sending a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Onsen Ryokan have been reserved."

[0872] Specific examples

[0873] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate and suggest travel destinations (e.g., Okinawa), transportation methods (airplane), accommodations (resort hotels), activities (snorkeling, beaches, etc.), and meals (restaurants within the resort). Once the user confirms and approves the plan, the server will complete the reservation process and notify the user of the completed information.

[0874] The system allows users to effortlessly create comprehensive travel plans and complete reservations efficiently.

[0875] The processing flow will be explained below.

[0876] Step 1:

[0877] The user inputs their travel wishes through the chat interface on the device. For example, they might input a sentence like, "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0878] Step 2:

[0879] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[0880] Step 3:

[0881] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car."

[0882] Step 4:

[0883] The server uses the extracted information to search for suitable destinations, transportation options, accommodations, activities, and dining options. In addition to the information in the database, it also crawls external information sources (reservation sites, individual store homepages, blogs, etc.) to collect the latest information.

[0884] Step 5:

[0885] The server generates a travel plan including possible destinations (e.g., Hakone), transportation options (private car), accommodations (XX hot spring inn), activities (hot springs, massage), and meals (△△ soba restaurant).

[0886] Step 6:

[0887] The server sends the generated travel plan to the terminal and presents it to the user, who then checks the plan and enters feedback.

[0888] Step 7:

[0889] The device receives the user's feedback and sends it to the server. The server regenerates the plan based on the feedback. For example, if the user requests a cheaper plan, the server will search for and suggest cheaper accommodations within the user's budget.

[0890] Step 8:

[0891] After the user confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, activities, and meals. For example, the user accesses the reservation website for a certain hot spring inn, checks room availability, and confirms the reservation.

[0892] Step 9:

[0893] The server verifies that the reservations are complete and aggregates all reservations to generate a reservation confirmation, which includes accommodation confirmation, activity reservation numbers, meal reservation details, etc.

[0894] Step 10:

[0895] The server sends the reservation confirmation information to the terminal and notifies the user, who receives the confirmation message through the terminal and can reconfirm or change the reservation as necessary.

[0896] In this way, users can efficiently create, confirm, and book travel plans using interactive AI chat.

[0897] Example 1

[0898] 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."

[0899] Planning and booking a trip can be a time-consuming process, requiring users to check multiple websites and services. This wastes time and makes it difficult for users to find the best plan. The objective of this invention is to provide a system that allows users to easily and efficiently plan a trip and make reservations all at once.

[0900] 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.

[0901] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling databases and external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans, thereby enabling users to efficiently and easily plan their trips and make reservations all at once.

[0902] "User" means any person or entity that uses the System to plan and book travel.

[0903] A "terminal" is a device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0904] The "interactive AI chat interface" is a user interface that allows users to input their travel preferences in natural language and interact with the system.

[0905] A "natural language processing engine" is a technology used to analyze user input and extract important information. Specifically, it is a text analysis technology.

[0906] "Candidate travel plans" are multiple travel plan options generated based on user input.

[0907] A "database" is an information collection that stores travel-related information and is a source of information that the server accesses to generate potential travel plans.

[0908] "Crawling external sources" refers to the process of automatically searching public sources on the Internet to gather the latest information.

[0909] "Feedback" refers to the user's evaluation or opinion on the proposed travel plans.

[0910] "Booking Process" means the process by which a reservation is made for some or all elements of a trip (e.g., accommodation, transportation, activities, meals, etc.).

[0911] "Booking in one go" means completing the booking for all elements of a travel plan in a single operation.

[0912] This invention is a system that can propose and book travel plans in one go based on travel preferences entered by a user. The system includes a server, a terminal, and a natural language processing engine that analyzes user input.

[0913] User Input

[0914] The device can be a smartphone, tablet, or personal computer. This device is equipped with an interactive AI chat interface, which allows users to input their travel preferences in natural language. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[0915] Analysis using natural language processing

[0916] The server analyzes the input text received from the user using a natural language processing engine (e.g., Google NLP, spaCy) to extract necessary information, such as the type of trip, budget, and preferred means of transportation.

[0917] Generate a plan

[0918] The server generates a candidate itinerary based on the extracted information. At this stage, the server searches for relevant information from a database (e.g., MySQL, MongoDB) and also uses a web crawler (e.g., Beautiful Soup, Scrapy) to gather up-to-date information from external sources. The generated candidate itinerary includes suggestions for destinations, transportation, accommodations, activities, and meals.

[0919] Suggestions and Feedback

[0920] The server sends the generated travel plan candidates to the terminal. The user can review the plans through the terminal and provide feedback. For example, feedback such as "I'd like that plan," or "Is there a cheaper plan?" is possible.

[0921] regeneration

[0922] The server receives user feedback and regenerates plans as needed. For example, if a user requests a lower budget, the server will search again and present a more reasonable plan.

[0923] Confirmation and booking process

[0924] Once the user confirms their plan, the server handles all the booking procedures, including checking availability of accommodation, reserving a parking space for the user's car, and booking activities and meals.

[0925] Providing reservation confirmation information

[0926] Once the reservation procedure is complete, the server will send a summary of the reservation confirmation information to the user, such as a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Hot Springs Inn have been reserved."

[0927] Specific examples

[0928] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate a travel plan with Okinawa as the destination, suggesting airplanes, resort hotels, snorkeling and beach activities, restaurants within the resort, etc. Once the user confirms and approves the plan, the server will carry out all reservation procedures and notify the user of the completed information.

[0929] This system allows users to plan their trips in one place and complete reservations efficiently, significantly reducing the amount of work required in the planning stage of a trip.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] A user opens a conversational AI chat interface using a device

[0933] A user opens an interactive AI chat interface using a smartphone, tablet, or personal computer. The interface is provided through a browser or a dedicated application. The input is the details of the user's desired trip (e.g., "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."), and the output is that this information is sent to the server.

[0934] Step 2:

[0935] The server receives the user's input

[0936] The server receives the user's input text through an HTTP request, which is stored on the server and passed to the next processing step. The input is the user's desired travel details, and the output is the user's desired data in text format.

[0937] Step 3:

[0938] The server launches a natural language processing engine to analyze the input text.

[0939] The server launches a natural language processing engine (e.g., Google NLP, spaCy) and analyzes the user's input text. The analysis extracts the necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," "car"). The input is the user's desired data in text format, and the output is the analyzed information (a list of keywords and phrases).

[0940] Step 4:

[0941] The server generates travel plan candidates

[0942] The server generates itinerary suggestions based on the extracted information. Specifically, it searches for relevant information from databases (e.g., MySQL, MongoDB) and collects up-to-date information from external sources using web crawlers (e.g., Beautiful Soup, Scrapy). The input is the parsed information, and the output is the generated itinerary suggestions (suggestions for destinations, transportation, accommodation, activities, and meals).

[0943] Step 5:

[0944] The server sends the generated travel plan candidates to the terminal.

[0945] The server sends the generated travel plan candidates to the terminal in JSON format via the REST API. The input is the travel plan candidates, and the output is the travel plan information sent to the terminal.

[0946] Step 6:

[0947] The device displays the proposed travel plan to the user.

[0948] The terminal displays the received travel plan candidates on a user interface. The user can review the plans and provide feedback. The input is the travel plan information sent to the terminal, and the output is the user's feedback.

[0949] Step 7:

[0950] The server receives user feedback and regenerates the plan as needed.

[0951] The server receives feedback from the user and regenerates the itinerary as needed, for example, re-searching for reasonably priced accommodations and generating a new itinerary. The input is the user's feedback, and the output is the regenerated itinerary candidates.

[0952] Step 8:

[0953] Once the user confirms the plan, the server starts the reservation process.

[0954] Once the user finalizes their travel plans, the server initiates the reservation process, including checking and reserving accommodations, reserving parking spaces for their vehicle, and booking activities and meals. The input is the finalized travel plans, and the output is reservation confirmation information.

[0955] Step 9:

[0956] The server collects the reservation confirmation information and notifies the user.

[0957] Once the reservation procedure is completed, the server sends the reservation confirmation information to the user via email or in-app notification. The input is the reservation confirmation information, and the output is a notification message to the user.

[0958] (Application example 1)

[0959] 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."

[0960] Modern travel planning is problematic because it requires a lot of time and effort. It is particularly cumbersome for users, as it requires separate reservations for destinations, transportation, accommodations, activities, and meals. Furthermore, various payments must be made each time a reservation is made, often complicating the process. For this reason, there is a demand for a system that automatically proposes comprehensive travel plans and smoothly handles everything from reservations to payments.

[0961] 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.

[0962] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating a plan based on user feedback, means for making a reservation based on the confirmed plan, and means for making an electronic payment when making the reservation. This allows the user to plan, book, and pay for all of their travel plans through a single interface.

[0963] "User input" is text data that describes travel wishes and conditions in natural language.

[0964] A "natural language processing engine" is a software system that analyzes user input and extracts requirements and information from it.

[0965] "Candidate itineraries" are suggested options for travel destinations, transportation, accommodations, activities, meals, etc., based on analyzed user input.

[0966] "External sources" refers to the latest information collected from various websites, databases, APIs, etc. on the Internet.

[0967] "Crawling" refers to the process of automatically collecting data from external sources over the Internet.

[0968] "User feedback" refers to the evaluation and additional information provided by the user regarding the proposed travel plan.

[0969] "Regeneration" is the process of recreating a travel plan based on user feedback.

[0970] "Reservation Facility" means a system or process that fulfills reservations for accommodation, transportation, activities, and meals according to a confirmed travel plan.

[0971] "Electronic payment" refers to online payment procedures using credit cards, digital wallets, etc.

[0972] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and reserves destinations, transportation, accommodations, activities, and meals all at once. This system is composed of users, a server, and terminals.

[0973] 1. User Input and Natural Language Processing

[0974] First, a user uses a device such as a smartphone or tablet to input their travel preferences into a conversational AI chat interface. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's somewhere I can drive to." This interface runs on the device.

[0975] The server analyzes the user's input using a natural language processing engine (e.g., SpaCy or Google NLP API) and extracts information such as the travel destination, budget, transportation method, and duration.

[0976] 2. Generate a travel plan

[0977] The server uses the analyzed information to crawl databases and external sources (e.g., travel booking sites and blogs) to generate itinerary suggestions, including suggested destinations, transportation options, accommodations, activities, and meals.

[0978] The generated travel plan candidates are sent back to the terminal and presented to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or "Is there a cheaper plan?"

[0979] 3. Regenerate the plan

[0980] The server can regenerate the plan as needed based on user feedback, for example, by finding more affordable accommodations or additional activities, and then regenerating and presenting the itinerary.

[0981] 4. Reservations and Electronic Payments

[0982] Once the user confirms the plan, the server checks the availability of accommodations, reserves transportation, reserves parking for the user's car, and makes reservations for activities and meals all at once. In the process of carrying out this confirmed reservation procedure, the reservation payment is made using an electronic payment service (e.g., Stripe or PayPal). This allows the user to simplify the complicated procedures and complete the reservation and payment all at once.

[0983] Hardware and software used

[0984] Hardware: Smartphones, tablets, computers

[0985] Software: Natural language processing engine (SpaCy, Google NLP API), conversational AI chat interface, travel database, electronic payment services (Stripe, PayPal)

[0986] Specific examples

[0987] For example, if a user enters "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to fly," the following steps will be executed:

[0988] 1. Natural language processing: Extract "resort," "parent and child," "three-day weekend," "under 100,000 yen," and "airplane" from the input text.

[0989] 2. Travel plan generation: Based on these conditions, search for resorts, transportation (airplane), accommodations, activities (snorkeling, beaches, etc.), and dining options.

[0990] 3. Feedback and Regeneration: Receive feedback from users and provide an even more optimal plan.

[0991] 4. Reservation and payment: Once the user confirms the plan, they enter their payment information on the terminal to complete the reservation process.

[0992] Example prompts to input to a generative AI model:

[0993] User input: I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it should be somewhere I can drive to.

[0994] AI Prompt: Enter your travel destination, budget, mode of transportation, and any specific activity or accommodation preferences.

[0995] This system allows users to easily plan all of their travel plans through a single interface, and smoothly complete reservations and payments.

[0996] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0997] Step 1:

[0998] User Input

[0999] Users use an interactive AI chat interface on their smartphone or tablet to input their travel preferences. For example, they might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance." This input is sent to the device as text data.

[1000] Step 2:

[1001] Natural Language Processing

[1002] The server receives the user's input text and analyzes it using a natural language processing engine (for example, SpaCy or Google NLP API). The input is natural language text about travel preferences, and the output is structured data that extracts information such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car." This structured data is then passed on to the next step.

[1003] Step 3:

[1004] Travel plan generation

[1005] The server crawls databases and external information sources (e.g., travel booking sites and blogs) based on the structured data to generate potential itineraries. In this step, the structured data is used as input, and a potential itinerary is generated as output, including destinations, transportation options, accommodations, activities, and meals. This results in multiple potential itineraries.

[1006] Step 4:

[1007] Plan presentation

[1008] The server sends the generated itinerary candidates to the terminal and presents them to the user. The input is the data of the generated itinerary candidates, and the output is the itinerary candidates displayed on the user's terminal. The user can check it and provide feedback.

[1009] Step 5:

[1010] Feedback capture and regeneration

[1011] The user provides feedback on the proposed itinerary. For example, they may respond with, "Is there a cheaper plan?" This feedback is sent to the server. The server analyzes the received feedback and regenerates the itinerary as necessary. The input is the user's feedback and the previous itinerary candidate, and the output is the regenerated new itinerary candidate.

[1012] Step 6:

[1013] Reservation confirmed

[1014] Once the user confirms their travel plans, the server checks availability of accommodations, reserves transportation, reserves parking spaces for their car, and makes reservations for activities and meals all at once. In this step, the server receives the user's confirmed travel plans as input and generates reservation-related data as output.

[1015] Step 7:

[1016] Electronic Payment

[1017] The server uses an electronic payment service (such as Stripe or PayPal) to process payment for the reservation when the reservation is made. The input is the user's payment information and confirmed travel plans, and the output is a reservation confirmation that is notified to the user. At this point, the user can confirm that the reservation and payment have been completed.

[1018] 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.

[1019] This invention is a system that allows users to input their travel preferences via an interactive AI chat, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[1020] User Input

[1021] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I want to go on a refreshing hot spring trip with my friends next month. My budget is 100,000 yen, and I'd prefer a place that's accessible by train.").

[1022] Analysis using natural language processing

[1023] The server passes the user's input to a natural language processing (NLP) engine, which extracts necessary information from the input text (e.g., "hot spring trip," "next month," "budget of 100,000 yen," "train").

[1024] emotion recognition

[1025] Based on the extracted information, the server simultaneously uses an emotion recognition engine to determine emotions from user input and past feedback. For example, if a user uses keywords such as "I want to refresh myself" or "I'm tired," the server recognizes that relaxation is important.

[1026] Generate a plan

[1027] The server generates suitable destination, transportation, accommodation, activity, and dining suggestions based on the extracted information and emotion recognition results. Specifically, it searches for suggestions within the database and crawls external information sources (reservation sites, store homepages, blogs, etc.) to collect the latest information.

[1028] Suggestions and Feedback

[1029] The server sends the generated travel plan candidates to the terminal and presents them to the user. Based on the output of the emotion recognition engine, the server adjusts the order and content of the suggestions taking the user's emotions into account. The user can then review them and enter feedback.

[1030] regeneration

[1031] The device receives the user's feedback and sends it to the server. The server then regenerates the plan based on the feedback and emotion recognition results. For example, if the user requests a cheaper accommodation, the server will re-search and suggest cheaper accommodation options within the budget.

[1032] Emotion-based re-proposal

[1033] The server uses an emotion recognition engine to instantly detect when a user is feeling dissatisfied and automatically generate and propose alternative plans. For example, if a user emphasizes that they are "tired" or "want to relax," the server will present a plan that is more focused on relaxation.

[1034] Confirmation and booking process

[1035] After the user confirms and approves the plan, the device sends the information to the server, which then initiates the reservation process for the selected accommodation, activities, and meals. For example, the user accesses a reservation site for a hot spring inn, checks room availability, and makes a reservation, as well as other activity and meal reservations.

[1036] Providing reservation confirmation information

[1037] Once the reservation is complete, the server compiles the information and sends a confirmation message to the user, such as "Your reservation is complete. You have also secured a stay at a hot spring inn and a relaxing massage."

[1038] Specific examples

[1039] For example, if a user inputs, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere accessible by train," the server will use natural language processing and emotion recognition to determine that the user has a strong desire to refresh themselves. As a result, it will suggest a hot spring trip plan focused on relaxation (for example, including a massage and meditation session in addition to the hot springs). If the user approves, the server will complete the reservation process for that plan and provide the user with reservation confirmation information.

[1040] The system aims to enable users to easily and effectively create, confirm, and book travel plans. In addition, by utilizing an emotion recognition engine, it is possible to make suggestions that more accurately reflect the user's emotions and needs.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The user accesses the chat interface on the device and inputs their travel wishes. For example, they might input a sentence like, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere I can get to by train."

[1044] Step 2:

[1045] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[1046] Step 3:

[1047] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[1048] Step 4:

[1049] The server uses an emotion recognition engine to determine the user's emotions based on the extracted information. For example, if keywords such as "refreshed" or "tired" are included, the server will recognize that the user values ​​relaxation.

[1050] Step 5:

[1051] The server uses the extracted information and emotion recognition results to generate suitable destination, transportation, accommodation, activity, and dining suggestions. For example, the server crawls databases and external information sources to suggest hot spring inns in Hakone, relaxing massage plans, and delicious local restaurants.

[1052] Step 6:

[1053] The server sends the generated travel plan (e.g., destination Hakone, transportation by train, accommodation at XX hot spring inn, activities at massage, meals at △△ restaurant) to the terminal and presents it to the user. Here, the server adjusts the content and order of suggestions based on the output of the emotion recognition engine, taking into account the user's emotions.

[1054] Step 7:

[1055] The user reviews the proposed plan and enters feedback, such as "Are there any other activities that fit within the budget?", into the chat interface.

[1056] Step 8:

[1057] The device receives the user's feedback and sends it to the server, which then regenerates the plan based on the feedback and emotion recognition results. For example, the server may search again for and suggest a plan that includes more relaxation activities within the budget.

[1058] Step 9:

[1059] The server uses an emotion recognition engine to instantly detect when the user is feeling dissatisfied (e.g., "I'm tired" or "I want to relax more") and automatically generates and re-proposes an alternative plan.

[1060] Step 10:

[1061] After the user finally confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, transportation, activities, and meals. For example, the server checks availability and reserves a room at a certain hot spring inn, and also makes reservations for transportation services and delicious restaurants.

[1062] Step 11:

[1063] Once the reservation is complete, the server compiles the reservation confirmation information and notifies the user, for example, sending a message saying, "Your reservation is complete. You have secured a stay at the Hakone hot spring inn and a relaxing massage."

[1064] In this way, users can create, regenerate, confirm and book travel plans quickly and efficiently using conversational AI chat and an emotion recognition engine.

[1065] Example 2

[1066] 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."

[1067] Conventional travel planning systems have difficulty making suggestions that accurately reflect the user's wishes and feelings. It has also been difficult to provide effective feedback in real time based on the information entered by the user and automatically generate and book optimal travel plans. As a result, users have had to spend a lot of time and effort planning their trips.

[1068] 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.

[1069] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the preferences using a natural language processing engine, means for determining the user's emotions using an emotion recognition engine, means for generating candidate travel plans based on the analysis and emotion recognition, means for collecting the latest information by crawling external information sources, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans. This makes it possible to generate and reserve optimal travel plans that reflect the user's preferences and emotions.

[1070] "User" refers to an individual who uses the system to input travel preferences, propose travel plans, and make reservations.

[1071] "Travel Desires" is information describing the wishes and requirements regarding the trip that the user is planning.

[1072] A "natural language processing engine" is a technology that analyzes text data entered by a user and extracts its meaning and important keywords.

[1073] An "emotion recognition engine" is a technology that determines emotions from user input and past feedback and makes appropriate suggestions based on those emotions.

[1074] "Candidate travel plans" are multiple travel plan options generated based on the user's travel wishes and emotion recognition.

[1075] "External information sources" are sources for collecting the latest information from external websites, booking sites, blogs, etc. other than the database.

[1076] "Crawling" is a technique for automatically collecting information on the web.

[1077] "Feedback" refers to opinions and requests made by the user regarding the presented travel plan.

[1078] "Reservation means" refers to a method and system for actually reserving accommodation, transportation, activities, and meals based on a confirmed plan.

[1079] This invention is a system that allows users to input their travel preferences through an interactive AI chat interface, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[1080] Hardware and software used

[1081] This system uses the following major hardware and software:

[1082] Device: The device used by the user (smartphone, tablet, PC, etc.)

[1083] Server: Cloud server for high performance computing (e.g. AWS EC2 instance)

[1084] Natural language processing engine: uses spaCy and BERT as models

[1085] Emotion recognition engine: Uses IBM Watson and Microsoft Azure Emotion API

[1086] Database: A database for searching suitable travel plan candidates

[1087] Crawling tools: Web scraping tools such as BeautifulSoup

[1088] System Operation Overview

[1089] 1. User Input

[1090] Using the device, users input their travel preferences through an interactive AI chat interface.

[1091] Example: A user opens their smartphone and types into an interactive chat box, "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place that's accessible by train."

[1092] 2. Analysis using natural language processing

[1093] The server passes the user's input to a natural language processing engine to extract specific keywords and phrases.

[1094] Example: Extract keywords such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[1095] 3. Emotion recognition

[1096] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[1097] Example: From keywords such as "I want to refresh myself" and "I'm tired," it is determined that the user wants to relax.

[1098] 4. Generate a plan

[1099] The server generates multiple travel plan candidates based on the user's wishes and emotion recognition results.

[1100] The server accesses the database to search for accommodations including relaxation and hot springs, while simultaneously crawling the latest information from external booking sites and blogs to create a list of candidates.

[1101] 5. Suggestions and Feedback

[1102] The server sends the generated itinerary candidates to the terminal and presents them to the user, who then checks them and inputs feedback.

[1103] Example: A user checks a plan such as "Stay at a hot spring inn and get a massage package" displayed on a device and sends feedback such as "Can't it be a little cheaper?"

[1104] 6. Regeneration

[1105] The server receives the user's feedback and generates the plan again.

[1106] The server analyzes the feedback and re-searches the database to find cheaper accommodations within the budget.

[1107] 7. Emotion-Based Re-Proposal

[1108] The server uses an emotion recognition engine to instantly generate and propose alternative plans if the user is dissatisfied.

[1109] Example: In response to a user's input "I want to relax," the system generates and suggests new plans that include massages and meditation sessions.

[1110] 8. Confirmation and Booking Procedures

[1111] Once the user approves the plan, the device sends the information to the server, which then begins the reservation process.

[1112] Example: The server accesses the reservation site of a selected hot spring inn, checks room availability, and makes reservations, as well as confirms reservations for activities and meals.

[1113] 9. Providing reservation confirmation information

[1114] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[1115] Example: Generate a reservation completion notification and send a message to the user's smartphone saying, "Your reservation has been completed. Accommodation at a hot spring inn and a relaxing massage have been reserved."

[1116] This system allows a user to easily create a desired travel plan in a relatively short time and receive optimal suggestions based on their emotions. In this way, the invention can be implemented.

[1117] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1118] Step 1:

[1119] Users use the device to input their travel preferences through an interactive AI chat interface.

[1120] Input: Text about the user's travel desires (e.g., "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place accessible by train.")

[1121] Output: Input text data

[1122] Specific behavior:

[1123] Users open the app on their smartphone and enter their travel wishes in text form into an interactive chat box.

[1124] Step 2:

[1125] The terminal receives the user's input and sends it to the server.

[1126] Input: Text data entered by the user

[1127] Output: Text data sent to the server

[1128] Specific behavior:

[1129] Once the user has completed the input, the terminal transfers the text data to the server in real time.

[1130] Step 3:

[1131] The server receives the text data and analyzes it using a natural language processing engine.

[1132] Input: User's text data

[1133] Output: Extracted keywords (e.g., "hot spring trip," "next month," "budget 100,000 yen," "train")

[1134] Data processing or data calculation:

[1135] The server uses a natural language processing engine (e.g., spaCy or BERT) to analyze the text data and extract important keywords.

[1136] Specific behavior:

[1137] The server runs an NLP engine to analyze the user's input and extract the required information.

[1138] Step 4:

[1139] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[1140] Input: Extracted keywords

[1141] Output: Determined emotion (e.g., "I want to feel refreshed")

[1142] Data processing or data calculation:

[1143] The server uses an emotion recognition engine (e.g., IBM Watson or Microsoft Azure Emotion API) to determine the user's emotions from the extracted keywords.

[1144] Specific behavior:

[1145] The server activates an emotion recognition engine and determines the emotion based on the analysis results.

[1146] Step 5:

[1147] The server generates travel plan candidates based on the determined emotions and keywords.

[1148] Input: Keywords and emotion judgment results

[1149] Output: Travel plan candidate list

[1150] Data processing or data calculation:

[1151] The server accesses the database to search for travel plans that match the user's desired conditions, and also collects additional information by crawling the latest information from external booking sites and blogs.

[1152] Specific behavior:

[1153] The server collects suitable travel plan candidates from a database or external information sources and creates a candidate list.

[1154] Step 6:

[1155] The server transmits the generated travel plan candidates to the terminal and presents them to the user.

[1156] Input: Travel plan candidate list

[1157] Output: Plan candidates presented to the user

[1158] Specific behavior:

[1159] The server transmits the list of travel plan candidates to the terminal and displays it to the user.

[1160] Step 7:

[1161] The user reviews the presented travel plan and enters feedback.

[1162] Input: Feedback text (e.g. "Can you make it a little cheaper?")

[1163] Output: Feedback data

[1164] Specific behavior:

[1165] The user checks the proposed plan on the terminal and enters feedback regarding satisfaction.

[1166] Step 8:

[1167] The terminal receives the user's feedback and transmits it to the server.

[1168] Input: User feedback data

[1169] Output: Feedback data sent to the server

[1170] Specific behavior:

[1171] Once the feedback is entered, the terminal transmits the data to the server.

[1172] Step 9:

[1173] The server regenerates the plan based on the feedback and emotion recognition results.

[1174] Input: Feedback data and emotion recognition results

[1175] Output: Regenerated itinerary candidate list

[1176] Data processing or data calculation:

[1177] The server analyzes the feedback and emotion determination, searches the database again, and generates a new plan.

[1178] Specific behavior:

[1179] The server searches for and re-proposes new plans based on the feedback.

[1180] Step 10:

[1181] The server starts the reservation procedure based on the plan approved by the user.

[1182] Input: Approved Plan

[1183] Output: Reservation procedure execution

[1184] Specific behavior:

[1185] The server accesses an external reservation site to make reservations for accommodation and transportation.

[1186] Step 11:

[1187] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[1188] Input: Reservation completion information

[1189] Output: Reservation confirmation information sent to the user

[1190] Specific behavior:

[1191] Once the reservation is complete, the server generates a confirmation message and notifies the user.

[1192] The above are the specific processing steps of this system.

[1193] (Application example 2)

[1194] 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."

[1195] While recent food delivery services allow users to easily order meals, they rarely provide menu suggestions that take into account the user's mood or emotions. Furthermore, they lack the ability to provide personalized meal plans that reflect the user's emotions and preferences, leaving users seeking improved satisfaction. Furthermore, it is difficult to reflect user feedback in real time and continually provide optimal suggestions.

[1196] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving meal preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for analyzing the user's emotions using an emotion recognition engine, means for generating meal plan candidates based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated meal plan candidates to the user, means for regenerating a plan based on user feedback, and means for executing an order based on the confirmed plan. This makes it possible to provide a personalized meal plan that suits the user's mood and preferences.

[1197] "Meal preferences entered by the user" refers to specific requests and conditions for meals provided by users using food delivery services, such as "dessert to relieve fatigue" or "healthy lunch."

[1198] A "natural language processing engine" is an artificial intelligence technology that analyzes text data entered by a user and understands its meaning and intent.

[1199] An "emotion recognition engine" is a system that analyzes and determines emotions and moods based on user input and behavior.

[1200] "Meal plan candidates" are a list of menu and restaurant suggestions that best suit the user's desires and feelings, generated based on the analysis results.

[1201] "Means of crawling external sources and collecting the latest information" refers to technologies and methods for automatically obtaining the latest data from web pages and APIs.

[1202] "User feedback" is information indicating the user's evaluation of the proposed meal plan and any additional requests.

[1203] A "means for regenerating a plan" is a system or method for regenerating a new optimal meal plan based on user feedback.

[1204] The "means for executing an order based on the confirmed plan" refers to a system or method for actually placing a food delivery order based on the meal plan approved by the user.

[1205] "Menu" means a list of dishes and / or food and drink items available for food delivery.

[1206] A "restaurant" is a food and beverage establishment that serves food.

[1207] A "dining place" is a specific place where food is eaten.

[1208] "Cooking activities" are the process of cooking and related events and experiences.

[1209] "Dessert" is a sweet dish or confectionery served at the end of a meal.

[1210] This invention is a system that provides personalized meal plans that reflect the user's preferences for food delivery. This system uses an emotion recognition engine and a natural language processing engine to analyze text data entered by the user, and generates and presents meal plans based on the latest information.

[1211] User Interface

[1212] Users use a smartphone app to input their meal preferences through an interactive AI chat interface. For example, they might input, "I'd like a dessert to soothe my tiredness." This input data is then sent to the server.

[1213] Natural Language Processing and Emotion Recognition

[1214] The server performs natural language processing using TextBlob and Hugging Face transformers packages to analyze the input text from the user. At this time, it extracts keywords (e.g., "soothing" and "dessert") from the input text and uses an emotion recognition engine to determine the user's mood and emotions (e.g., "relaxed" and "tired").

[1215] Plan generation and proposal

[1216] The server generates meal plan candidates based on the analysis results. This involves crawling external information to gather the latest menu and restaurant information. The generated meal plan candidates are optimized based on the user's mood and preferences. For example, if a user requests a "dessert that will energize them," energy bars and smoothies will be suggested.

[1217] User Feedback and Regeneration

[1218] The generated meal plan candidates are presented to the user via a smartphone app. The user reviews the suggestions and enters their feedback. This feedback is also analyzed by the emotion recognition engine, and the meal plan is regenerated as necessary. For example, if the user provides feedback such as "Is there a dessert that is a little less sweet?", a new suggestion is automatically generated.

[1219] Order confirmation and execution

[1220] Once the user approves the final meal plan, the server executes the specific order, using the delivery service's API to send the order information to the restaurant or dining establishment and confirm it.

[1221] Hardware and software used

[1222] The system includes the following hardware and software:

[1223] Hardware: User's smartphone, server

[1224] Software: TextBlob, Hugging Face transformers, Delivery Service API

[1225] Processing example

[1226] For example, the following prompt sentences are used:

[1227] "I'm looking for a quick lunch right now, any recommendations?"

[1228] "I'm tired, do you have any desserts to cheer me up?"

[1229] This allows the system to suggest personalized menus that correspond to the user's specific desires and emotions.

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] A user launches a smartphone application and inputs their meal preferences in text format through the conversational AI chat interface. At this time, the user inputs a prompt such as, "I'm tired, so do you have any desserts that will cheer me up?" The input text data is sent to the server. A specific example of input is, "I'd like to eat a refreshing lunch."

[1233] Step 2:

[1234] The server analyzes the user's input text using natural language processing engines such as TextBlob and Hugging Face transformers. During the analysis process, keywords (e.g., "dessert" or "energetic") are extracted from the input text and their meaning is understood. The input is the user's text data, and the output is the extracted keywords and their related information.

[1235] Step 3:

[1236] The server uses an emotion recognition engine to analyze the user's emotional state from the user's input text. For example, it determines that the user is feeling tired from the keyword "tired." In this process, the server determines the user's emotion based on the input text data and obtains emotional state data as output.

[1237] Step 4:

[1238] The server generates optimal meal plan candidates based on the results of natural language processing and emotion recognition. During this process, it crawls information within the database and external sources to collect the latest menu and restaurant information. For example, if a user is looking for an "energizing dessert," energy bars and smoothies are generated as candidates. The input is the analysis results, and the output is the generated meal plan candidates.

[1239] Step 5:

[1240] The server presents the generated meal plan candidates to the user through a smartphone application, adjusting the order and content of the recommended menus and restaurants based on the user's emotional state. The input is the meal plan candidates, and the output is the information presented to the user.

[1241] Step 6:

[1242] The user reviews the proposed meal plan options and enters their feedback. This feedback can include specific requests and ratings, such as "Is there a cheaper option?" or "I don't like this menu." The input is the user's feedback data, and the output is that feedback information.

[1243] Step 7:

[1244] The server analyzes the user's feedback and reconstructs the meal plan using an emotion recognition engine and a reconstruction engine. The reconstructed plan is adjusted to meet the user's additional requests, for example, by re-suggesting cheaper options or different menu items. The input is the feedback data, and the output is a reconstructed meal plan candidate.

[1245] Step 8:

[1246] Once the user approves the final meal plan, the server uses the delivery service API to place the order. The order information is sent to the restaurant or food establishment and the process is confirmed. The input is the confirmed meal plan information, and the output is order confirmation data.

[1247] Step 9:

[1248] Once the order is complete, the server compiles the information and sends a confirmation to the user via a smartphone application. For example, it sends a message saying, "Your order is complete! Your energizing smoothie will be delivered shortly." The input is the order result data, and the output is the notification data to the user.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] [Fourth embodiment]

[1253] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1254] 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.

[1255] 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).

[1256] 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.

[1257] 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.

[1258] 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).

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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."

[1266] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and books all destinations, transportation, accommodation, activities, and meals all at once.

[1267] User Input

[1268] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance.").

[1269] Analysis using natural language processing

[1270] The server uses a natural language processing (NLP) engine to analyze the input text and extract necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," and "car").

[1271] Generate a plan

[1272] The server generates travel plan suggestions based on the extracted information. Specifically, the server searches a database for possible destinations, transportation options, accommodations, activities, and meals. It also crawls external information sources (reservation sites, individual store homepages, personal blogs, etc.) to collect the latest information.

[1273] Suggestions and Feedback

[1274] The server sends the generated travel plan candidates to the terminal and presents them to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or, "Do you have a cheaper plan?"

[1275] regeneration

[1276] The server receives user feedback and regenerates the plan as necessary. For example, it can search for more reasonably priced accommodation and propose a new plan.

[1277] Confirmation and booking process

[1278] Once the user confirms the plan, the server handles the reservation process on their behalf, specifically checking availability of accommodations, making reservations, securing parking spaces for their car, and making reservations for activities and meals.

[1279] Providing reservation confirmation information

[1280] Once the reservation is complete, the server compiles the information and notifies the user, for example, by sending a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Onsen Ryokan have been reserved."

[1281] Specific examples

[1282] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate and suggest travel destinations (e.g., Okinawa), transportation methods (airplane), accommodations (resort hotels), activities (snorkeling, beaches, etc.), and meals (restaurants within the resort). Once the user confirms and approves the plan, the server will complete the reservation process and notify the user of the completed information.

[1283] The system allows users to effortlessly create comprehensive travel plans and complete reservations efficiently.

[1284] The processing flow will be explained below.

[1285] Step 1:

[1286] The user inputs their travel wishes through the chat interface on the device. For example, they might input a sentence like, "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[1287] Step 2:

[1288] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[1289] Step 3:

[1290] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car."

[1291] Step 4:

[1292] The server uses the extracted information to search for suitable destinations, transportation options, accommodations, activities, and dining options. In addition to the information in the database, it also crawls external information sources (reservation sites, individual store homepages, blogs, etc.) to collect the latest information.

[1293] Step 5:

[1294] The server generates a travel plan including possible destinations (e.g., Hakone), transportation options (private car), accommodations (XX hot spring inn), activities (hot springs, massage), and meals (△△ soba restaurant).

[1295] Step 6:

[1296] The server sends the generated travel plan to the terminal and presents it to the user, who then checks the plan and enters feedback.

[1297] Step 7:

[1298] The device receives the user's feedback and sends it to the server. The server regenerates the plan based on the feedback. For example, if the user requests a cheaper plan, the server will search for and suggest cheaper accommodations within the user's budget.

[1299] Step 8:

[1300] After the user confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, activities, and meals. For example, the user accesses the reservation website for a certain hot spring inn, checks room availability, and confirms the reservation.

[1301] Step 9:

[1302] The server verifies that the reservations are complete and aggregates all reservations to generate a reservation confirmation, which includes accommodation confirmation, activity reservation numbers, meal reservation details, etc.

[1303] Step 10:

[1304] The server sends the reservation confirmation information to the terminal and notifies the user, who receives the confirmation message through the terminal and can reconfirm or change the reservation as necessary.

[1305] In this way, users can efficiently create, confirm, and book travel plans using interactive AI chat.

[1306] Example 1

[1307] 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."

[1308] Planning and booking a trip can be a time-consuming process, requiring users to check multiple websites and services. This wastes time and makes it difficult for users to find the best plan. The objective of this invention is to provide a system that allows users to easily and efficiently plan a trip and make reservations all at once.

[1309] 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.

[1310] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling databases and external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans, thereby enabling users to efficiently and easily plan their trips and make reservations all at once.

[1311] "User" means any person or entity that uses the System to plan and book travel.

[1312] A "terminal" is a device used by a user, and includes a smartphone, tablet, personal computer, etc.

[1313] The "interactive AI chat interface" is a user interface that allows users to input their travel preferences in natural language and interact with the system.

[1314] A "natural language processing engine" is a technology used to analyze user input and extract important information. Specifically, it is a text analysis technology.

[1315] "Candidate travel plans" are multiple travel plan options generated based on user input.

[1316] A "database" is an information collection that stores travel-related information and is a source of information that the server accesses to generate potential travel plans.

[1317] "Crawling external sources" refers to the process of automatically searching public sources on the Internet to gather the latest information.

[1318] "Feedback" refers to the user's evaluation or opinion on the proposed travel plans.

[1319] "Booking Process" means the process by which a reservation is made for some or all elements of a trip (e.g., accommodation, transportation, activities, meals, etc.).

[1320] "Booking in one go" means completing the booking for all elements of a travel plan in a single operation.

[1321] This invention is a system that can propose and book travel plans in one go based on travel preferences entered by a user. The system includes a server, a terminal, and a natural language processing engine that analyzes user input.

[1322] User Input

[1323] The device can be a smartphone, tablet, or personal computer. This device is equipped with an interactive AI chat interface, which allows users to input their travel preferences in natural language. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."

[1324] Analysis using natural language processing

[1325] The server analyzes the input text received from the user using a natural language processing engine (e.g., Google NLP, spaCy) to extract necessary information, such as the type of trip, budget, and preferred means of transportation.

[1326] Generate a plan

[1327] The server generates a candidate itinerary based on the extracted information. At this stage, the server searches for relevant information from a database (e.g., MySQL, MongoDB) and also uses a web crawler (e.g., Beautiful Soup, Scrapy) to gather up-to-date information from external sources. The generated candidate itinerary includes suggestions for destinations, transportation, accommodations, activities, and meals.

[1328] Suggestions and Feedback

[1329] The server sends the generated travel plan candidates to the terminal. The user can review the plans through the terminal and provide feedback. For example, feedback such as "I'd like that plan," or "Is there a cheaper plan?" is possible.

[1330] regeneration

[1331] The server receives user feedback and regenerates plans as needed. For example, if a user requests a lower budget, the server will search again and present a more reasonable plan.

[1332] Confirmation and booking process

[1333] Once the user confirms their plan, the server handles all the booking procedures, including checking availability of accommodation, reserving a parking space for the user's car, and booking activities and meals.

[1334] Providing reservation confirmation information

[1335] Once the reservation procedure is complete, the server will send a summary of the reservation confirmation information to the user, such as a message saying, "Your reservation is complete. Accommodation and massage at the Hakone Hot Springs Inn have been reserved."

[1336] Specific examples

[1337] For example, if a user inputs "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to use an airplane," the server will generate a travel plan with Okinawa as the destination, suggesting airplanes, resort hotels, snorkeling and beach activities, restaurants within the resort, etc. Once the user confirms and approves the plan, the server will carry out all reservation procedures and notify the user of the completed information.

[1338] This system allows users to plan their trips in one place and complete reservations efficiently, significantly reducing the amount of work required in the planning stage of a trip.

[1339] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1340] Step 1:

[1341] A user opens a conversational AI chat interface using a device

[1342] A user opens an interactive AI chat interface using a smartphone, tablet, or personal computer. The interface is provided through a browser or a dedicated application. The input is the details of the user's desired trip (e.g., "I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance."), and the output is that this information is sent to the server.

[1343] Step 2:

[1344] The server receives the user's input

[1345] The server receives the user's input text through an HTTP request, which is stored on the server and passed to the next processing step. The input is the user's desired travel details, and the output is the user's desired data in text format.

[1346] Step 3:

[1347] The server launches a natural language processing engine to analyze the input text.

[1348] The server launches a natural language processing engine (e.g., Google NLP, spaCy) and analyzes the user's input text. The analysis extracts the necessary information (e.g., "hot spring trip," "weekend," "budget of 50,000 yen," "car"). The input is the user's desired data in text format, and the output is the analyzed information (a list of keywords and phrases).

[1349] Step 4:

[1350] The server generates travel plan candidates

[1351] The server generates itinerary suggestions based on the extracted information. Specifically, it searches for relevant information from databases (e.g., MySQL, MongoDB) and collects up-to-date information from external sources using web crawlers (e.g., Beautiful Soup, Scrapy). The input is the parsed information, and the output is the generated itinerary suggestions (suggestions for destinations, transportation, accommodation, activities, and meals).

[1352] Step 5:

[1353] The server sends the generated travel plan candidates to the terminal.

[1354] The server sends the generated travel plan candidates to the terminal in JSON format via the REST API. The input is the travel plan candidates, and the output is the travel plan information sent to the terminal.

[1355] Step 6:

[1356] The device displays the proposed travel plan to the user.

[1357] The terminal displays the received travel plan candidates on a user interface. The user can review the plans and provide feedback. The input is the travel plan information sent to the terminal, and the output is the user's feedback.

[1358] Step 7:

[1359] The server receives user feedback and regenerates the plan as needed.

[1360] The server receives feedback from the user and regenerates the itinerary as needed, for example, re-searching for reasonably priced accommodations and generating a new itinerary. The input is the user's feedback, and the output is the regenerated itinerary candidates.

[1361] Step 8:

[1362] Once the user confirms the plan, the server starts the reservation process.

[1363] Once the user finalizes their travel plans, the server initiates the reservation process, including checking and reserving accommodations, reserving parking spaces for their vehicle, and booking activities and meals. The input is the finalized travel plans, and the output is reservation confirmation information.

[1364] Step 9:

[1365] The server collects the reservation confirmation information and notifies the user.

[1366] Once the reservation procedure is completed, the server sends the reservation confirmation information to the user via email or in-app notification. The input is the reservation confirmation information, and the output is a notification message to the user.

[1367] (Application example 1)

[1368] 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."

[1369] Modern travel planning is problematic because it requires a lot of time and effort. It is particularly cumbersome for users, as it requires separate reservations for destinations, transportation, accommodations, activities, and meals. Furthermore, various payments must be made each time a reservation is made, often complicating the process. For this reason, there is a demand for a system that automatically proposes comprehensive travel plans and smoothly handles everything from reservations to payments.

[1370] 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.

[1371] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for generating candidate travel plans based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated candidate travel plans to the user, means for regenerating a plan based on user feedback, means for making a reservation based on the confirmed plan, and means for making an electronic payment when making the reservation. This allows the user to plan, book, and pay for all of their travel plans through a single interface.

[1372] "User input" is text data that describes travel wishes and conditions in natural language.

[1373] A "natural language processing engine" is a software system that analyzes user input and extracts requirements and information from it.

[1374] "Candidate itineraries" are suggested options for travel destinations, transportation, accommodations, activities, meals, etc., based on analyzed user input.

[1375] "External sources" refers to the latest information collected from various websites, databases, APIs, etc. on the Internet.

[1376] "Crawling" refers to the process of automatically collecting data from external sources over the Internet.

[1377] "User feedback" refers to the evaluation and additional information provided by the user regarding the proposed travel plan.

[1378] "Regeneration" is the process of recreating a travel plan based on user feedback.

[1379] "Reservation Facility" means a system or process that fulfills reservations for accommodation, transportation, activities, and meals according to a confirmed travel plan.

[1380] "Electronic payment" refers to online payment procedures using credit cards, digital wallets, etc.

[1381] This invention is a system that allows users to input their travel preferences in natural language via an interactive AI chat, and then suggests and reserves destinations, transportation, accommodations, activities, and meals all at once. This system is composed of users, a server, and terminals.

[1382] 1. User Input and Natural Language Processing

[1383] First, a user uses a device such as a smartphone or tablet to input their travel preferences into a conversational AI chat interface. For example, a user might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's somewhere I can drive to." This interface runs on the device.

[1384] The server analyzes the user's input using a natural language processing engine (e.g., SpaCy or Google NLP API) and extracts information such as the travel destination, budget, transportation method, and duration.

[1385] 2. Generate a travel plan

[1386] The server uses the analyzed information to crawl databases and external sources (e.g., travel booking sites and blogs) to generate itinerary suggestions, including suggested destinations, transportation options, accommodations, activities, and meals.

[1387] The generated travel plan candidates are sent back to the terminal and presented to the user. The user can review them and provide feedback. For example, they can respond with, "I'd like that plan, please," or "Is there a cheaper plan?"

[1388] 3. Regenerate the plan

[1389] The server can regenerate the plan as needed based on user feedback, for example, by finding more affordable accommodations or additional activities, and then regenerating and presenting the itinerary.

[1390] 4. Reservations and Electronic Payments

[1391] Once the user confirms the plan, the server checks the availability of accommodations, reserves transportation, reserves parking for the user's car, and makes reservations for activities and meals all at once. In the process of carrying out this confirmed reservation procedure, the reservation payment is made using an electronic payment service (e.g., Stripe or PayPal). This allows the user to simplify the complicated procedures and complete the reservation and payment all at once.

[1392] Hardware and software used

[1393] Hardware: Smartphones, tablets, computers

[1394] Software: Natural language processing engine (SpaCy, Google NLP API), conversational AI chat interface, travel database, electronic payment services (Stripe, PayPal)

[1395] Specific examples

[1396] For example, if a user enters "I want to go to a resort that my family and I can enjoy during the three-day weekend in October. My budget is under 100,000 yen, and I want to fly," the following steps will be executed:

[1397] 1. Natural language processing: Extract "resort," "parent and child," "three-day weekend," "under 100,000 yen," and "airplane" from the input text.

[1398] 2. Travel plan generation: Based on these conditions, search for resorts, transportation (airplane), accommodations, activities (snorkeling, beaches, etc.), and dining options.

[1399] 3. Feedback and Regeneration: Receive feedback from users and provide an even more optimal plan.

[1400] 4. Reservation and payment: Once the user confirms the plan, they enter their payment information on the terminal to complete the reservation process.

[1401] Example prompts to input to a generative AI model:

[1402] User input: I want to go on a hot spring trip this weekend. My budget is 50,000 yen, and it should be somewhere I can drive to.

[1403] AI Prompt: Enter your travel destination, budget, mode of transportation, and any specific activity or accommodation preferences.

[1404] This system allows users to easily plan all of their travel plans through a single interface, and smoothly complete reservations and payments.

[1405] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1406] Step 1:

[1407] User Input

[1408] Users use an interactive AI chat interface on their smartphone or tablet to input their travel preferences. For example, they might input, "I'd like to go on a hot spring trip this weekend. My budget is 50,000 yen, and it's within driving distance." This input is sent to the device as text data.

[1409] Step 2:

[1410] Natural Language Processing

[1411] The server receives the user's input text and analyzes it using a natural language processing engine (for example, SpaCy or Google NLP API). The input is natural language text about travel preferences, and the output is structured data that extracts information such as "hot spring trip," "weekend," "budget of 50,000 yen," and "car." This structured data is then passed on to the next step.

[1412] Step 3:

[1413] Travel plan generation

[1414] The server crawls databases and external information sources (e.g., travel booking sites and blogs) based on the structured data to generate potential itineraries. In this step, the structured data is used as input, and a potential itinerary is generated as output, including destinations, transportation options, accommodations, activities, and meals. This results in multiple potential itineraries.

[1415] Step 4:

[1416] Plan presentation

[1417] The server sends the generated itinerary candidates to the terminal and presents them to the user. The input is the data of the generated itinerary candidates, and the output is the itinerary candidates displayed on the user's terminal. The user can check it and provide feedback.

[1418] Step 5:

[1419] Feedback capture and regeneration

[1420] The user provides feedback on the proposed itinerary. For example, they may respond with, "Is there a cheaper plan?" This feedback is sent to the server. The server analyzes the received feedback and regenerates the itinerary as necessary. The input is the user's feedback and the previous itinerary candidate, and the output is the regenerated new itinerary candidate.

[1421] Step 6:

[1422] Reservation confirmed

[1423] Once the user confirms their travel plans, the server checks availability of accommodations, reserves transportation, reserves parking spaces for their car, and makes reservations for activities and meals all at once. In this step, the server receives the user's confirmed travel plans as input and generates reservation-related data as output.

[1424] Step 7:

[1425] Electronic Payment

[1426] The server uses an electronic payment service (such as Stripe or PayPal) to process payment for the reservation when the reservation is made. The input is the user's payment information and confirmed travel plans, and the output is a reservation confirmation that is notified to the user. At this point, the user can confirm that the reservation and payment have been completed.

[1427] 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.

[1428] This invention is a system that allows users to input their travel preferences via an interactive AI chat, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[1429] User Input

[1430] The terminal is installed on the user's device (smartphone, tablet, PC, etc.) and provides an interactive AI chat interface. The user uses this interface to input their travel preferences (e.g., "I want to go on a refreshing hot spring trip with my friends next month. My budget is 100,000 yen, and I'd prefer a place that's accessible by train.").

[1431] Analysis using natural language processing

[1432] The server passes the user's input to a natural language processing (NLP) engine, which extracts necessary information from the input text (e.g., "hot spring trip," "next month," "budget of 100,000 yen," "train").

[1433] emotion recognition

[1434] Based on the extracted information, the server simultaneously uses an emotion recognition engine to determine emotions from user input and past feedback. For example, if a user uses keywords such as "I want to refresh myself" or "I'm tired," the server recognizes that relaxation is important.

[1435] Generate a plan

[1436] The server generates suitable destination, transportation, accommodation, activity, and dining suggestions based on the extracted information and emotion recognition results. Specifically, it searches for suggestions within the database and crawls external information sources (reservation sites, store homepages, blogs, etc.) to collect the latest information.

[1437] Suggestions and Feedback

[1438] The server sends the generated travel plan candidates to the terminal and presents them to the user. Based on the output of the emotion recognition engine, the server adjusts the order and content of the suggestions taking the user's emotions into account. The user can then review them and enter feedback.

[1439] regeneration

[1440] The device receives the user's feedback and sends it to the server. The server then regenerates the plan based on the feedback and emotion recognition results. For example, if the user requests a cheaper accommodation, the server will re-search and suggest cheaper accommodation options within the budget.

[1441] Emotion-based re-proposal

[1442] The server uses an emotion recognition engine to instantly detect when a user is feeling dissatisfied and automatically generate and propose alternative plans. For example, if a user emphasizes that they are "tired" or "want to relax," the server will present a plan that is more focused on relaxation.

[1443] Confirmation and booking process

[1444] After the user confirms and approves the plan, the device sends the information to the server, which then initiates the reservation process for the selected accommodation, activities, and meals. For example, the user accesses a reservation site for a hot spring inn, checks room availability, and makes a reservation, as well as other activity and meal reservations.

[1445] Providing reservation confirmation information

[1446] Once the reservation is complete, the server compiles the information and sends a confirmation message to the user, such as "Your reservation is complete. You have also secured a stay at a hot spring inn and a relaxing massage."

[1447] Specific examples

[1448] For example, if a user inputs, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere accessible by train," the server will use natural language processing and emotion recognition to determine that the user has a strong desire to refresh themselves. As a result, it will suggest a hot spring trip plan focused on relaxation (for example, including a massage and meditation session in addition to the hot springs). If the user approves, the server will complete the reservation process for that plan and provide the user with reservation confirmation information.

[1449] The system aims to enable users to easily and effectively create, confirm, and book travel plans. In addition, by utilizing an emotion recognition engine, it is possible to make suggestions that more accurately reflect the user's emotions and needs.

[1450] The processing flow will be explained below.

[1451] Step 1:

[1452] The user accesses the chat interface on the device and inputs their travel wishes. For example, they might input a sentence like, "I want to go on a refreshing hot spring trip next month. My budget is 100,000 yen, and I'd like to go somewhere I can get to by train."

[1453] Step 2:

[1454] The device sends the user's input to the server, which passes it to a natural language processing (NLP) engine.

[1455] Step 3:

[1456] The server uses an NLP engine to analyze the user's input, extracting travel-related elements such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[1457] Step 4:

[1458] The server uses an emotion recognition engine to determine the user's emotions based on the extracted information. For example, if keywords such as "refreshed" or "tired" are included, the server will recognize that the user values ​​relaxation.

[1459] Step 5:

[1460] The server uses the extracted information and emotion recognition results to generate suitable destination, transportation, accommodation, activity, and dining suggestions. For example, the server crawls databases and external information sources to suggest hot spring inns in Hakone, relaxing massage plans, and delicious local restaurants.

[1461] Step 6:

[1462] The server sends the generated travel plan (e.g., destination Hakone, transportation by train, accommodation at XX hot spring inn, activities at massage, meals at △△ restaurant) to the terminal and presents it to the user. Here, the server adjusts the content and order of suggestions based on the output of the emotion recognition engine, taking into account the user's emotions.

[1463] Step 7:

[1464] The user reviews the proposed plan and enters feedback, such as "Are there any other activities that fit within the budget?", into the chat interface.

[1465] Step 8:

[1466] The device receives the user's feedback and sends it to the server, which then regenerates the plan based on the feedback and emotion recognition results. For example, the server may search again for and suggest a plan that includes more relaxation activities within the budget.

[1467] Step 9:

[1468] The server uses an emotion recognition engine to instantly detect when the user is feeling dissatisfied (e.g., "I'm tired" or "I want to relax more") and automatically generates and re-proposes an alternative plan.

[1469] Step 10:

[1470] After the user finally confirms and approves the plan, the device sends the information to the server, which then begins the reservation process for the selected accommodation, transportation, activities, and meals. For example, the server checks availability and reserves a room at a certain hot spring inn, and also makes reservations for transportation services and delicious restaurants.

[1471] Step 11:

[1472] Once the reservation is complete, the server compiles the reservation confirmation information and notifies the user, for example, sending a message saying, "Your reservation is complete. You have secured a stay at the Hakone hot spring inn and a relaxing massage."

[1473] In this way, users can create, regenerate, confirm and book travel plans quickly and efficiently using conversational AI chat and an emotion recognition engine.

[1474] Example 2

[1475] 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."

[1476] Conventional travel planning systems have difficulty making suggestions that accurately reflect the user's wishes and feelings. It has also been difficult to provide effective feedback in real time based on the information entered by the user and automatically generate and book optimal travel plans. As a result, users have had to spend a lot of time and effort planning their trips.

[1477] 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.

[1478] In this invention, the server includes means for receiving travel preferences input by a user, means for analyzing the preferences using a natural language processing engine, means for determining the user's emotions using an emotion recognition engine, means for generating candidate travel plans based on the analysis and emotion recognition, means for collecting the latest information by crawling external information sources, means for presenting the generated candidate travel plans to the user, means for regenerating plans based on user feedback, and means for making reservations based on the confirmed plans. This makes it possible to generate and reserve optimal travel plans that reflect the user's preferences and emotions.

[1479] "User" refers to an individual who uses the system to input travel preferences, propose travel plans, and make reservations.

[1480] "Travel Desires" is information describing the wishes and requirements regarding the trip that the user is planning.

[1481] A "natural language processing engine" is a technology that analyzes text data entered by a user and extracts its meaning and important keywords.

[1482] An "emotion recognition engine" is a technology that determines emotions from user input and past feedback and makes appropriate suggestions based on those emotions.

[1483] "Candidate travel plans" are multiple travel plan options generated based on the user's travel wishes and emotion recognition.

[1484] "External information sources" are sources for collecting the latest information from external websites, booking sites, blogs, etc. other than the database.

[1485] "Crawling" is a technique for automatically collecting information on the web.

[1486] "Feedback" refers to opinions and requests made by the user regarding the presented travel plan.

[1487] "Reservation means" refers to a method and system for actually reserving accommodation, transportation, activities, and meals based on a confirmed plan.

[1488] This invention is a system that allows users to input their travel preferences through an interactive AI chat interface, and then uses a system including an emotion recognition engine to suggest and book all destinations, transportation, accommodation, activities, and meals all at once.

[1489] Hardware and software used

[1490] This system uses the following major hardware and software:

[1491] Device: The device used by the user (smartphone, tablet, PC, etc.)

[1492] Server: Cloud server for high performance computing (e.g. AWS EC2 instance)

[1493] Natural language processing engine: uses spaCy and BERT as models

[1494] Emotion recognition engine: Uses IBM Watson and Microsoft Azure Emotion API

[1495] Database: A database for searching suitable travel plan candidates

[1496] Crawling tools: Web scraping tools such as BeautifulSoup

[1497] System Operation Overview

[1498] 1. User Input

[1499] Using the device, users input their travel preferences through an interactive AI chat interface.

[1500] Example: A user opens their smartphone and types into an interactive chat box, "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place that's accessible by train."

[1501] 2. Analysis using natural language processing

[1502] The server passes the user's input to a natural language processing engine to extract specific keywords and phrases.

[1503] Example: Extract keywords such as "hot spring trip," "next month," "budget of 100,000 yen," and "train."

[1504] 3. Emotion recognition

[1505] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[1506] Example: From keywords such as "I want to refresh myself" and "I'm tired," it is determined that the user wants to relax.

[1507] 4. Generate a plan

[1508] The server generates multiple travel plan candidates based on the user's wishes and emotion recognition results.

[1509] The server accesses the database to search for accommodations including relaxation and hot springs, while simultaneously crawling the latest information from external booking sites and blogs to create a list of candidates.

[1510] 5. Suggestions and Feedback

[1511] The server sends the generated itinerary candidates to the terminal and presents them to the user, who then checks them and inputs feedback.

[1512] Example: A user checks a plan such as "Stay at a hot spring inn and get a massage package" displayed on a device and sends feedback such as "Can't it be a little cheaper?"

[1513] 6. Regeneration

[1514] The server receives the user's feedback and generates the plan again.

[1515] The server analyzes the feedback and re-searches the database to find cheaper accommodations within the budget.

[1516] 7. Emotion-Based Re-Proposal

[1517] The server uses an emotion recognition engine to instantly generate and propose alternative plans if the user is dissatisfied.

[1518] Example: In response to a user's input "I want to relax," the system generates and suggests new plans that include massages and meditation sessions.

[1519] 8. Confirmation and Booking Procedures

[1520] Once the user approves the plan, the device sends the information to the server, which then begins the reservation process.

[1521] Example: The server accesses the reservation site of a selected hot spring inn, checks room availability, and makes reservations, as well as confirms reservations for activities and meals.

[1522] 9. Providing reservation confirmation information

[1523] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[1524] Example: Generate a reservation completion notification and send a message to the user's smartphone saying, "Your reservation has been completed. Accommodation at a hot spring inn and a relaxing massage have been reserved."

[1525] This system allows a user to easily create a desired travel plan in a relatively short time and receive optimal suggestions based on their emotions. In this way, the invention can be implemented.

[1526] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] Users use the device to input their travel preferences through an interactive AI chat interface.

[1529] Input: Text about the user's travel desires (e.g., "Next month, I'd like to go on a refreshing hot spring trip with my friends. My budget is 100,000 yen, and I'd prefer a place accessible by train.")

[1530] Output: Input text data

[1531] Specific behavior:

[1532] Users open the app on their smartphone and enter their travel wishes in text form into an interactive chat box.

[1533] Step 2:

[1534] The terminal receives the user's input and sends it to the server.

[1535] Input: Text data entered by the user

[1536] Output: Text data sent to the server

[1537] Specific behavior:

[1538] Once the user has completed the input, the terminal transfers the text data to the server in real time.

[1539] Step 3:

[1540] The server receives the text data and analyzes it using a natural language processing engine.

[1541] Input: User's text data

[1542] Output: Extracted keywords (e.g., "hot spring trip," "next month," "budget 100,000 yen," "train")

[1543] Data processing or data calculation:

[1544] The server uses a natural language processing engine (e.g., spaCy or BERT) to analyze the text data and extract important keywords.

[1545] Specific behavior:

[1546] The server runs an NLP engine to analyze the user's input and extract the required information.

[1547] Step 4:

[1548] The server uses an emotion recognition engine to determine the user's emotions based on the extracted keywords.

[1549] Input: Extracted keywords

[1550] Output: Determined emotion (e.g., "I want to feel refreshed")

[1551] Data processing or data calculation:

[1552] The server uses an emotion recognition engine (e.g., IBM Watson or Microsoft Azure Emotion API) to determine the user's emotions from the extracted keywords.

[1553] Specific behavior:

[1554] The server activates an emotion recognition engine and determines the emotion based on the analysis results.

[1555] Step 5:

[1556] The server generates travel plan candidates based on the determined emotions and keywords.

[1557] Input: Keywords and emotion judgment results

[1558] Output: Travel plan candidate list

[1559] Data processing or data calculation:

[1560] The server accesses the database to search for travel plans that match the user's desired conditions, and also collects additional information by crawling the latest information from external booking sites and blogs.

[1561] Specific behavior:

[1562] The server collects suitable travel plan candidates from a database or external information sources and creates a candidate list.

[1563] Step 6:

[1564] The server transmits the generated travel plan candidates to the terminal and presents them to the user.

[1565] Input: Travel plan candidate list

[1566] Output: Plan candidates presented to the user

[1567] Specific behavior:

[1568] The server transmits the list of travel plan candidates to the terminal and displays it to the user.

[1569] Step 7:

[1570] The user reviews the presented travel plan and enters feedback.

[1571] Input: Feedback text (e.g. "Can you make it a little cheaper?")

[1572] Output: Feedback data

[1573] Specific behavior:

[1574] The user checks the proposed plan on the terminal and enters feedback regarding satisfaction.

[1575] Step 8:

[1576] The terminal receives the user's feedback and transmits it to the server.

[1577] Input: User feedback data

[1578] Output: Feedback data sent to the server

[1579] Specific behavior:

[1580] Once the feedback is entered, the terminal transmits the data to the server.

[1581] Step 9:

[1582] The server regenerates the plan based on the feedback and emotion recognition results.

[1583] Input: Feedback data and emotion recognition results

[1584] Output: Regenerated itinerary candidate list

[1585] Data processing or data calculation:

[1586] The server analyzes the feedback and emotion determination, searches the database again, and generates a new plan.

[1587] Specific behavior:

[1588] The server searches for and re-proposes new plans based on the feedback.

[1589] Step 10:

[1590] The server starts the reservation procedure based on the plan approved by the user.

[1591] Input: Approved Plan

[1592] Output: Reservation procedure execution

[1593] Specific behavior:

[1594] The server accesses an external reservation site to make reservations for accommodation and transportation.

[1595] Step 11:

[1596] The server compiles the reservation confirmation information and sends it to the terminal to notify the user.

[1597] Input: Reservation completion information

[1598] Output: Reservation confirmation information sent to the user

[1599] Specific behavior:

[1600] Once the reservation is complete, the server generates a confirmation message and notifies the user.

[1601] The above are the specific processing steps of this system.

[1602] (Application example 2)

[1603] 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."

[1604] While recent food delivery services allow users to easily order meals, they rarely provide menu suggestions that take into account the user's mood or emotions. Furthermore, they lack the ability to provide personalized meal plans that reflect the user's emotions and preferences, leaving users seeking improved satisfaction. Furthermore, it is difficult to reflect user feedback in real time and continually provide optimal suggestions.

[1605] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving meal preferences input by a user, means for analyzing the user's input using a natural language processing engine, means for analyzing the user's emotions using an emotion recognition engine, means for generating meal plan candidates based on the analysis, means for crawling external information sources to collect the latest information, means for presenting the generated meal plan candidates to the user, means for regenerating a plan based on user feedback, and means for executing an order based on the confirmed plan. This makes it possible to provide a personalized meal plan that suits the user's mood and preferences.

[1606] "Meal preferences entered by the user" refers to specific requests and conditions for meals provided by users using food delivery services, such as "dessert to relieve fatigue" or "healthy lunch."

[1607] A "natural language processing engine" is an artificial intelligence technology that analyzes text data entered by a user and understands its meaning and intent.

[1608] An "emotion recognition engine" is a system that analyzes and determines emotions and moods based on user input and behavior.

[1609] "Meal plan candidates" are a list of menu and restaurant suggestions that best suit the user's desires and feelings, generated based on the analysis results.

[1610] "Means of crawling external sources and collecting the latest information" refers to technologies and methods for automatically obtaining the latest data from web pages and APIs.

[1611] "User feedback" is information indicating the user's evaluation of the proposed meal plan and any additional requests.

[1612] A "means for regenerating a plan" is a system or method for regenerating a new optimal meal plan based on user feedback.

[1613] The "means for executing an order based on the confirmed plan" refers to a system or method for actually placing a food delivery order based on the meal plan approved by the user.

[1614] "Menu" means a list of dishes and / or food and drink items available for food delivery.

[1615] A "restaurant" is a food and beverage establishment that serves food.

[1616] A "dining place" is a specific place where food is eaten.

[1617] "Cooking activities" are the process of cooking and related events and experiences.

[1618] "Dessert" is a sweet dish or confectionery served at the end of a meal.

[1619] This invention is a system that provides personalized meal plans that reflect the user's preferences for food delivery. This system uses an emotion recognition engine and a natural language processing engine to analyze text data entered by the user, and generates and presents meal plans based on the latest information.

[1620] User Interface

[1621] Users use a smartphone app to input their meal preferences through an interactive AI chat interface. For example, they might input, "I'd like a dessert to soothe my tiredness." This input data is then sent to the server.

[1622] Natural Language Processing and Emotion Recognition

[1623] The server performs natural language processing using TextBlob and Hugging Face transformers packages to analyze the input text from the user. At this time, it extracts keywords (e.g., "soothing" and "dessert") from the input text and uses an emotion recognition engine to determine the user's mood and emotions (e.g., "relaxed" and "tired").

[1624] Plan generation and proposal

[1625] The server generates meal plan candidates based on the analysis results. This involves crawling external information to gather the latest menu and restaurant information. The generated meal plan candidates are optimized based on the user's mood and preferences. For example, if a user requests a "dessert that will energize them," energy bars and smoothies will be suggested.

[1626] User Feedback and Regeneration

[1627] The generated meal plan candidates are presented to the user via a smartphone app. The user reviews the suggestions and enters their feedback. This feedback is also analyzed by the emotion recognition engine, and the meal plan is regenerated as necessary. For example, if the user provides feedback such as "Is there a dessert that is a little less sweet?", a new suggestion is automatically generated.

[1628] Order confirmation and execution

[1629] Once the user approves the final meal plan, the server executes the specific order, using the delivery service's API to send the order information to the restaurant or dining establishment and confirm it.

[1630] Hardware and software used

[1631] The system includes the following hardware and software:

[1632] Hardware: User's smartphone, server

[1633] Software: TextBlob, Hugging Face transformers, Delivery Service API

[1634] Processing example

[1635] For example, the following prompt sentences are used:

[1636] "I'm looking for a quick lunch right now, any recommendations?"

[1637] "I'm tired, do you have any desserts to cheer me up?"

[1638] This allows the system to suggest personalized menus that correspond to the user's specific desires and emotions.

[1639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1640] Step 1:

[1641] A user launches a smartphone application and inputs their meal preferences in text format through the conversational AI chat interface. At this time, the user inputs a prompt such as, "I'm tired, so do you have any desserts that will cheer me up?" The input text data is sent to the server. A specific example of input is, "I'd like to eat a refreshing lunch."

[1642] Step 2:

[1643] The server analyzes the user's input text using natural language processing engines such as TextBlob and Hugging Face transformers. During the analysis process, keywords (e.g., "dessert" or "energetic") are extracted from the input text and their meaning is understood. The input is the user's text data, and the output is the extracted keywords and their related information.

[1644] Step 3:

[1645] The server uses an emotion recognition engine to analyze the user's emotional state from the user's input text. For example, it determines that the user is feeling tired from the keyword "tired." In this process, the server determines the user's emotion based on the input text data and obtains emotional state data as output.

[1646] Step 4:

[1647] The server generates optimal meal plan candidates based on the results of natural language processing and emotion recognition. During this process, it crawls information within the database and external sources to collect the latest menu and restaurant information. For example, if a user is looking for an "energizing dessert," energy bars and smoothies are generated as candidates. The input is the analysis results, and the output is the generated meal plan candidates.

[1648] Step 5:

[1649] The server presents the generated meal plan candidates to the user through a smartphone application, adjusting the order and content of the recommended menus and restaurants based on the user's emotional state. The input is the meal plan candidates, and the output is the information presented to the user.

[1650] Step 6:

[1651] The user reviews the proposed meal plan options and enters their feedback. This feedback can include specific requests and ratings, such as "Is there a cheaper option?" or "I don't like this menu." The input is the user's feedback data, and the output is that feedback information.

[1652] Step 7:

[1653] The server analyzes the user's feedback and reconstructs the meal plan using an emotion recognition engine and a reconstruction engine. The reconstructed plan is adjusted to meet the user's additional requests, for example, by re-suggesting cheaper options or different menu items. The input is the feedback data, and the output is a reconstructed meal plan candidate.

[1654] Step 8:

[1655] Once the user approves the final meal plan, the server uses the delivery service API to place the order. The order information is sent to the restaurant or food establishment and the process is confirmed. The input is the confirmed meal plan information, and the output is order confirmation data.

[1656] Step 9:

[1657] Once the order is complete, the server compiles the information and sends a confirmation to the user via a smartphone application. For example, it sends a message saying, "Your order is complete! Your energizing smoothie will be delivered shortly." The input is the order result data, and the output is the notification data to the user.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] FIG. 9 is a diagram illustrating 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 actions 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.

[1663] 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.

[1664] 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).

[1665] 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.

[1666] 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."

[1667] 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.

[1668] 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).

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] The following is further disclosed regarding the above embodiment.

[1680] (Claim 1)

[1681] a means for receiving travel preferences input by the user;

[1682] means for analyzing the user's input using a natural language processing engine;

[1683] means for generating candidate travel plans based on said analysis;

[1684] A means of crawling external information sources to collect the latest information;

[1685] means for presenting the generated travel plan candidates to a user;

[1686] means for regenerating the plan based on user feedback;

[1687] The system includes a means for executing reservations based on the confirmed plan.

[1688] (Claim 2)

[1689] The system of claim 1 , wherein the potential travel plans include suggestions for destinations, transportation, accommodations, activities, and meals.

[1690] (Claim 3)

[1691] 2. The system according to claim 1, wherein the reservation means makes reservations for all of accommodations, transportation, activities, and meals.

[1692] "Example 1"

[1693] (Claim 1)

[1694] a means for receiving travel preferences input by the user;

[1695] means for analyzing the user's input using a natural language processing engine;

[1696] means for generating candidate travel plans based on said analysis;

[1697] A means of crawling databases and external sources to gather up-to-date information;

[1698] means for presenting the generated travel plan candidates to a user;

[1699] means for regenerating the plan based on user feedback;

[1700] The system includes a means for executing reservations based on the confirmed plan.

[1701] (Claim 2)

[1702] The system of claim 1 , wherein the potential travel plans include suggestions for destinations, transportation, accommodations, activities, and meals.

[1703] (Claim 3)

[1704] 2. The system according to claim 1, wherein the reservation means makes reservations for all of accommodations, transportation, activities, and meals.

[1705] "Application Example 1"

[1706] (Claim 1)

[1707] a means for receiving travel preferences input by the user;

[1708] means for analyzing the user's input using a natural language processing engine;

[1709] means for generating candidate travel plans based on said analysis;

[1710] A means of crawling external information sources to collect the latest information;

[1711] means for presenting the generated travel plan candidates to a user;

[1712] means for regenerating the plan based on user feedback;

[1713] A means for making a reservation based on the confirmed plan;

[1714] means for making an electronic payment when making said reservation;

[1715] A system including:

[1716] (Claim 2)

[1717] The system of claim 1 , wherein the potential travel plans include suggestions for destinations, transportation, accommodations, activities, and meals.

[1718] (Claim 3)

[1719] 2. The system according to claim 1, wherein all reservations for accommodation, transportation, activities, and meals are made by said reservation means, and payment is made in one lump sum by said electronic payment means.

[1720] "Example 2: Combining Emotion Engines"

[1721] (Claim 1)

[1722] a means for receiving travel preferences input by the user;

[1723] means for analyzing the user's input using a natural language processing engine;

[1724] means for determining a user's emotion using an emotion recognition engine based on said analysis;

[1725] means for generating candidate itineraries based on the analysis and emotion recognition;

[1726] A means of crawling external information sources to collect the latest information;

[1727] means for presenting the generated travel plan candidates to a user;

[1728] means for regenerating the plan based on user feedback;

[1729] The system includes a means for executing reservations based on the confirmed plan.

[1730] (Claim 2)

[1731] The system of claim 1 , wherein the potential travel plans include suggestions for destinations, transportation, accommodations, activities, and meals.

[1732] (Claim 3)

[1733] 2. The system according to claim 1, wherein the reservation means makes reservations for all of accommodations, transportation, activities, and meals.

[1734] "Application example 2 when combining emotion engines"

[1735] (Claim 1)

[1736] means for receiving user-entered meal preferences;

[1737] means for analyzing the user's input using a natural language processing engine;

[1738] means for analyzing a user's emotions using an emotion recognition engine;

[1739] means for generating candidate meal plans based on said analysis;

[1740] A means of crawling external information sources to collect the latest information;

[1741] means for presenting the generated meal plan candidates to a user;

[1742] means for regenerating the plan based on user feedback;

[1743] The system includes a means for executing orders based on the confirmed plan.

[1744] (Claim 2)

[1745] 10. The system of claim 1, wherein the candidate meal plans include suggestions for menus, restaurants, dining locations, cooking activities, and desserts.

[1746] (Claim 3)

[1747] The system according to claim 1, wherein the ordering means orders all of the dining establishments, cooking itineraries, cooking activities, and desserts. [Explanation of symbols]

[1748] 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. a means for receiving travel preferences input by the user; means for analyzing the user's input using a natural language processing engine; means for generating candidate travel plans based on said analysis; A means of crawling external information sources to collect the latest information; means for presenting the generated travel plan candidates to a user; means for regenerating the plan based on user feedback; The system includes a means for executing reservations based on the confirmed plan.

2. The system of claim 1 , wherein the potential travel plans include destination, transportation, accommodation, activity, and dining suggestions.

3. 2. The system according to claim 1, wherein said reservation means makes reservations for all of accommodations, transportation, activities, and meals.

Images