system

Abstract

Provide a system. [Solution] A means for collecting reservation status and availability information of accommodation facilities; A method to calculate the accommodation rating score using an AI algorithm, A means for receiving user input conditions and filtering relevant accommodations to suggest an optimal combination; A means for booking the accommodation plan selected by the user; A system including:

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] There are hotels that are not registered on accommodation reservation sites, or that are mistakenly displayed as "no availability" due to limitations on existing sites, resulting in accommodations that are not used despite actually having availability. This results in a shortage of accommodations in certain areas, making it difficult for users to find suitable accommodations. Furthermore, accommodation operators are unable to effectively utilize vacant rooms, making it difficult to maximize profits. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting accommodation reservation status and availability information, a means for calculating accommodation rating scores using an AI algorithm, a means for receiving user input criteria and filtering related accommodations to suggest optimal combinations, and a means for reserving the accommodation plan selected by the user. This system uses web scraping technology to collect information on unregistered accommodations and uses AI to analyze it to suggest optimal accommodation plans to users. In particular, by proposing a combination of multiple accommodations, the problem of a shortage of accommodations can be resolved. This allows users to easily find suitable accommodations and allows accommodation providers to effectively utilize available rooms.

[0006] "Accommodation" means a building or room for temporary accommodation of travelers or guests.

[0007] "Reservation status" refers to rooms already reserved at the accommodation facility and detailed information about those reservations.

[0008] "Availability information" refers to data that indicates available rooms at accommodation facilities and their availability status.

[0009] "Means of collection" refers to the methods and tools used to acquire and store data.

[0010] The "rating score" is a numerical value that uses an AI algorithm to evaluate various characteristics of an accommodation facility.

[0011] An "AI algorithm" is a calculation method that uses artificial intelligence technology to analyze data and make judgments and predictions.

[0012] "Input conditions" are specific requests or requirements that a user makes to the system.

[0013] "Filtering" is the process of selecting data or information based on specific criteria.

[0014] A "means for proposing combinations" refers to a method or system for presenting the optimal set from multiple options.

[0015] "Means for making reservations" refers to the method or system for officially making reservations for the accommodation plan selected by the user.

[0016] "Web scraping technology" is a programming technique for automatically collecting public information on the Internet. [Brief explanation of the drawings]

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

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

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

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

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

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

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

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0038] The present invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0039] 1. Data Collection

[0040] The server collects real-time reservation status and availability information from accommodation booking sites via API. It also automatically collects data from unregistered accommodations using web scraping technology and stores it in a database for analysis. In the process, it obtains detailed information such as the accommodation's name, address, price, availability, and available services.

[0041] 2. Data Analysis

[0042] The server uses an AI algorithm to calculate a rating score for each accommodation based on the collected data. This AI algorithm uses criteria such as price, reviews, location, and services provided. The analysis results are stored in a database and can be used according to user requests.

[0043] 3. User request input

[0044] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. The entered conditions are sent to the server via the device.

[0045] 4. Conditional Filtering

[0046] The server filters the accommodation information in its database based on the user's criteria. AI, in particular, calculates the optimal combination of rates and availability, and can propose plans combining multiple rooms or different accommodations. For example, it can suggest a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0047] 5. Providing the optimal plan

[0048] The server creates an accommodation plan that best suits the user's requirements from the filtering results and displays it on the terminal. The terminal also displays detailed information about each hotel (e.g., hotel name, address, price, details of available rooms, etc.) and specific suggestions for staying in multiple rooms or accommodations.

[0049] 6. User plan selection

[0050] The user checks the displayed accommodation plans and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent to the server.

[0051] 7. Reservation Procedure

[0052] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (e.g., reservation confirmation number, check-in procedure, etc.) on the terminal.

[0053] Specific examples

[0054] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0055] 1. Data Collection

[0056] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0057] 2. Data Analysis

[0058] The server analyzes this data using AI and calculates a rating score for each hotel.

[0059] 3. User request input

[0060] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[0061] 4. Conditional Filtering

[0062] The server optimizes the combination of multiple hotels and room types based on the input conditions and extracts available plans.

[0063] 5. Providing the optimal plan

[0064] The server will present the terminal with a plan that combines, for example, a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C.

[0065] 6. User plan selection

[0066] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[0067] 7. Reservation Procedure

[0068] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0069] In this way, users can easily find suitable accommodations, and accommodation providers can also make effective use of vacant rooms.

[0070] The processing flow will be explained below.

[0071] Step 1: Data collection

[0072] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs. For unregistered accommodations, the server then uses web scraping technology to extract the necessary information from official websites and public databases. The collected data includes details such as hotel names, addresses, rates, availability, and available services.

[0073] Step 2: Data analysis

[0074] The server inputs the data collected in step 1 into an AI algorithm to calculate a rating score for each accommodation. This rating score takes into consideration a comprehensive range of evaluation criteria, including price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent filtering.

[0075] Step 3: Entering User Requests

[0076] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0077] Step 4: Conditional filtering

[0078] The server filters the accommodation information in the database based on the conditions received from the user in step 3. During filtering, the AI ​​calculates the optimal combination of rates and availability, and prepares to propose combination plans for multiple rooms or different accommodations. For example, it can propose a combination of a triple room and a double room, or rooms available on the same day at different hotels.

[0079] Step 5: Presenting the optimal plan

[0080] The server creates an accommodation plan that best suits the user's requirements based on the filtering results of step 4. This plan includes detailed information about each hotel (hotel name, address, price, available room details, etc.) and specific suggestions for staying in multiple rooms or accommodations. The created plan is sent to the terminal and displayed to the user.

[0081] Step 6: User Plan Selection

[0082] The user checks the optimal plans displayed on the terminal and selects the desired plan. The selected plan is confirmed by clicking the "Reserve" button, and the information is sent from the terminal to the server.

[0083] Step 7: Booking process

[0084] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from each accommodation facility and stores this reservation information in a database. The server also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0085] Step 8: Confirmation and follow-up

[0086] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[0087] Example 1

[0088] 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."

[0089] Conventional accommodation reservation systems have limitations in obtaining reservation status and availability information, and it is difficult to calculate accurate evaluation scores. As a result, they are not capable of proposing optimal accommodation plans for users, and it is difficult for accommodation operators to effectively utilize available rooms. Furthermore, there is no flexible way to combine multiple accommodations and room types, so they are unable to meet the diverse needs of users.

[0090] 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.

[0091] In this invention, the server includes means for collecting accommodation reservation status and availability information, means for collecting detailed information about accommodations using web scraping technology, means for calculating accommodation rating scores using an AI algorithm, means for receiving user input conditions and filtering related accommodations from a database, means for proposing optimal combinations of multiple accommodations and room types, means for reserving the accommodation plan selected by the user, and means for notifying the user of confirmed reservation information. This allows users to easily find the optimal accommodation plan and enables accommodation providers to efficiently utilize available rooms.

[0092] "Accommodation facility" means a facility that provides a building or place for guests to stay.

[0093] "Reservation status" is information that indicates the current reservation status at a specific accommodation facility.

[0094] "Availability information" is information that indicates the status of rooms or spaces currently available at a particular accommodation facility.

[0095] "Web scraping technology" is a technology that automatically collects information that is publicly available on the Internet.

[0096] The "rating score" is a number that indicates the overall rating of an accommodation facility calculated based on various evaluation criteria.

[0097] An "AI algorithm" is a set of steps or calculation methods used to solve a specific problem using artificial intelligence technology.

[0098] "User" refers to an individual or group who uses the System to search for and reserve accommodation plans.

[0099] A "database" is a collection of data that stores collected information in an organized manner and allows for efficient search and analysis.

[0100] "Filtering" is the process of sorting data based on specific conditions and extracting the necessary information.

[0101] "Accommodation plan" refers to a specific proposal that combines accommodation facilities and their terms of use.

[0102] A "reservation request" is information sent by a user to convey the intention to make a reservation to an accommodation facility.

[0103] "Reservation confirmation information" is information for notifying the user that the reservation has been confirmed by the accommodation facility.

[0104] This invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0105] The server uses the APIs of multiple accommodation booking sites to collect information on accommodation reservation status and availability. For example, the server uses Python's Requests library to send GET requests to the API endpoints of each booking site and retrieve data in JSON format. For unregistered accommodations, the server also uses Python's BeautifulSoup and Requests library to automatically collect data using web scraping technology. During this process, detailed information such as the accommodation's name, address, price, availability, and available services is obtained and stored in a database.

[0106] The server uses an AI algorithm to calculate an accommodation rating score based on the collected data. Specifically, it uses Python's Scikit-learn to calculate the rating score based on evaluation criteria such as price, reviews, location, and services provided. This allows accommodations with high ratings to be suggested to users with higher priority.

[0107] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. These conditions are collected by a front-end user interface using React and sent to the server via an HTTP POST request.

[0108] The server filters the accommodation information in the database based on the conditions received from the user. It uses SQL queries to filter and extract the accommodations that best fit the user's conditions. The AI ​​model then calculates the optimal combination of rates and availability, and creates plans combining multiple accommodations and room types. For example, Python's Pandas library can be used to efficiently find accommodations that meet the conditions.

[0109] The server then generates an accommodation plan that best suits the user's requirements based on the filtering results and displays it on the device. Using React and MUI, the device dynamically displays hotel names, addresses, rates, and detailed information about available rooms. Plan examples include "combining a triple room at Hotel A with a double room at Hotel B" and "staying in two separate rooms at Hotel C."

[0110] The user selects the desired plan from the displayed accommodation plans. The selection is made by clicking the "Book" button in React, and the selected plan information is sent to the server via an Ajax request.

[0111] Finally, the server sends a reservation request to each relevant accommodation based on the details of the accommodation plan selected by the user. Specifically, it uses the Python Requests library to send a POST request to each accommodation's API and receives a confirmation response. This information is stored in a database and the user is notified that the reservation has been confirmed. The front-end displays the reservation confirmation number and check-in details.

[0112] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0113] 1. The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0114] 2. The server analyzes this data using Scikit-learn and calculates a rating score for each hotel.

[0115] 3. The user enters conditions such as "budget is within 10,000 yen per person per night, and the date of stay is next weekend" into the terminal and submits the request.

[0116] 4. The server uses SQL queries and the Pandas library to optimize the hotel combinations and room types based on the input criteria and extract available deals.

[0117] 5. The server presents the terminal with options such as "a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[0118] 6. The user selects the plan they like best from the plans presented and clicks "Book."

[0119] 7. The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0120] This system allows users to easily find the best accommodation plan and enables accommodation providers to make effective use of available rooms.

[0121] Example prompt sentence:

[0122] "Please suggest the best accommodation plan for a group of five people looking to stay in Tokyo. The budget is 10,000 yen per person per night, and the stay is next weekend."

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The server collects accommodation reservation status and availability information. To do this, it uses the accommodation booking site's API to send a GET request and retrieve data in JSON format. It also uses Python's BeautifulSoup and Requests libraries to web scrape information on unregistered accommodations. The collected data includes the accommodation's name, address, price, availability, and available services, and is stored in a database.

[0126] Input: API request parameters and website URL

[0127] Output: JSON data containing details of accommodations and scraping results

[0128] Step 2:

[0129] The server analyzes the collected data using an AI algorithm and calculates an evaluation score for each accommodation. Specifically, it uses Python's Scikit-learn to calculate the evaluation score based on evaluation criteria such as price, reviews, location, and services provided. The lower the price and the higher the reviews, the higher the score. The analysis results are stored in a database.

[0130] Input: Accommodation details stored in the database

[0131] Output: Rating score for each accommodation

[0132] Step 3:

[0133] The user uses a terminal to input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.). The user enters the information into a front-end form using React, which is then sent to the server via an HTTP POST request.

[0134] Input: desired accommodation conditions (date of stay, number of guests, budget, desired area, specific services, etc.)

[0135] Output: User's accommodation preferences sent to the server

[0136] Step 4:

[0137] The server filters the accommodation information in the database based on the user's desired accommodations, using an SQL query to extract accommodations that match the user's criteria, and then uses Python's Pandas library to manipulate the results, including price and availability, to calculate the optimal accommodation combination.

[0138] Input: User's desired accommodation conditions, accommodation information in the database

[0139] Output: The best accommodation candidates that meet the user's criteria

[0140] Step 5:

[0141] The server creates the optimal accommodation plan based on the filtering results and sends it to the front end. The accommodation plan is dynamically displayed on the terminal using React and MUI. Detailed information includes the hotel name, address, price, and available room details, and multiple combinations of accommodations and room types are presented.

[0142] Input: Best accommodation options

[0143] Output: The best accommodation plan displayed on the device

[0144] Step 6:

[0145] The user uses the React interface to select the desired accommodation plan from the displayed options. When the user clicks the "Book" button, the selected plan information is sent to the server via an Ajax request.

[0146] Input: The accommodation plan selected by the user

[0147] Output: Selected plan information sent to the server

[0148] Step 7:

[0149] The server sends a reservation request to each relevant accommodation facility based on the details of the accommodation plan selected by the user. Using the Python Requests library, it sends a POST request to each accommodation facility's API and receives a confirmed reservation response. The final reservation information is saved in a database and a confirmation notification is sent to the user. The reservation confirmation number and check-in procedure details are displayed on the terminal.

[0150] Input: Accommodation plan information selected by the user

[0151] Output: Confirmation response from the accommodation and notification to the user

[0152] (Application example 1)

[0153] 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."

[0154] Optimizing accommodation reservations and food delivery is important for increasing user convenience while also efficiently utilizing provider resources. However, previous systems did not adequately collect real-time reservation status and availability information, or propose optimal accommodation and delivery plans. Furthermore, it was difficult to collect information on unregistered accommodations and restaurants, making it difficult to provide users with a large number of options. Furthermore, systems lacked the functionality to optimize the combination of multiple accommodations and restaurants, making it impossible to meet the diverse needs of users.

[0155] 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.

[0156] In this invention, the server includes: means for collecting accommodation reservation status and availability information; means for calculating accommodation rating scores using an AI algorithm; means for receiving user input conditions and filtering related accommodations to suggest optimal combinations; means for reserving the user's selected accommodation plan; means for collecting order conditions and analyzing restaurant menu information and delivery status; means for presenting optimal restaurants and delivery plans based on the user's input order conditions; and means for notifying delivery personnel of the user's selected order. This allows users to find optimal accommodation plans and delivery plans in real time, while also allowing providers to efficiently utilize their resources and effectively collect information on unregistered accommodations and restaurants. Furthermore, it is possible to optimize combinations of multiple accommodations and restaurants to meet diverse user needs.

[0157] "Accommodation facilities" are facilities that provide accommodation services to guests, including hotels, inns, guest houses, etc.

[0158] "Reservation status" is information that indicates the degree to which available resources such as accommodations and restaurants are occupied during a specific period or date and time.

[0159] "Availability information" is information that indicates whether accommodation facilities, restaurants, etc. are available for use during a specific period or date and time.

[0160] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and solve a specific problem.

[0161] "User" means an individual or corporation that uses the system to make accommodation reservations or use food delivery services.

[0162] "Input conditions" are requests and wishes that the user provides to the system, and include reservation date, budget, type of meal, and the like.

[0163] "Filtering" is the process of selecting information that matches specific conditions from a large amount of information.

[0164] An "accommodation plan" is a plan for using accommodation facilities proposed in response to a user's request, and includes the price, room type, service content, and the like.

[0165] "Order conditions" refer to the wishes and requests that a user provides to the food delivery system, including the type of food, budget, delivery time, etc.

[0166] "Eating establishments" are establishments that serve meals, and include restaurants, cafes, fast food restaurants, etc.

[0167] "Delivery status" is information that indicates the current order processing status and delivery area when a restaurant provides food delivery services.

[0168] A "delivery plan" is a delivery plan from a restaurant proposed based on the user's order conditions, and includes the price, delivery time, delivery route, etc.

[0169] "Web scraping" is a technique for automatically collecting information from web pages on the Internet.

[0170] An "unregistered accommodation" is an accommodation that has not been pre-registered in the system.

[0171] An "unregistered restaurant" is a restaurant that has not been pre-registered in the system.

[0172] The present invention relates to an AI system that optimizes accommodation reservations and food delivery, and is composed of three entities: a server, a terminal, and a user. Specific embodiments are described below.

[0173] System configuration

[0174] 1. Data Collection

[0175] The server collects information on accommodation reservations and availability, as well as restaurant menus and delivery status in real time. Specifically, it collects information through APIs, and for unregistered accommodations and restaurants, it uses web scraping technology (e.g., BeautifulSoup, Selenium).

[0176] 2. Data Analysis

[0177] Based on the collected data, the server performs analysis using AI algorithms (e.g., Scikit-learn, TENSORFLOW (registered trademark)). The analysis includes calculating the accommodation's rating score and the optimal delivery route.

[0178] 3. User request input

[0179] Users input the conditions for booking accommodation or ordering delivery using devices such as smartphones or PCs, using an interface developed using React Native and Flutter (registered trademark).

[0180] 4. Conditional Filtering

[0181] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price, time, etc.

[0182] 5. Providing the optimal plan

[0183] The server creates accommodation and delivery plans that best suit the user's needs from the filtered results and displays them on the user's device.

[0184] 6. User Choice

[0185] The user selects one of the plans presented and presses the confirm button.

[0186] 7. Booking and Ordering Procedures

[0187] The server sends a reservation request and order to the accommodation facility or restaurant based on the selected plan, and stores the confirmed information in a database.

[0188] Hardware and software used

[0189] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[0190] Data Collection: RESTful API, BeautifulSoup, Selenium

[0191] Data analysis: Scikit-learn, TensorFlow

[0192] User Interface: React Native, Flutter

[0193] Specific examples

[0194] For example, if a group of five people are looking for accommodation in Tokyo and want to order dinner delivery for that night, here's how it works:

[0195] 1. The server collects information on accommodations and delivery restaurants in the Tokyo area.

[0196] 2. Analyze the collected data using AI to optimize accommodation rating scores and delivery plans.

[0197] 3. Users enter their accommodation and delivery requirements into the app.

[0198] 4. The server then filters the most suitable accommodation and delivery plans based on this information.

[0199] 5. The filtered results are presented to the user, who selects and confirms the desired plan.

[0200] 6. The server sends reservation and order requests to accommodations and restaurants based on the selected plan.

[0201] Prompt Sentence Examples

[0202] "Design an AI system that collects information on delivery restaurants in the Tokyo area and provides the optimal delivery plan based on the user's order criteria. The system should have the ability to suggest the optimal restaurant and menu combination based on the user's criteria, such as type of food, budget, and delivery time."

[0203] As described above, the present invention provides a system that optimizes accommodation reservations and food delivery, thereby significantly improving user convenience.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1: Data collection

[0206] The server collects accommodation reservation status and availability information, restaurant menu information, and delivery status in real time via API. It also uses web scraping technology to collect information on unregistered accommodations and restaurants. Specifically, it sends API requests and stores the received data in a database in JSON format. Web scraping uses BeautifulSoup and Selenium to analyze the necessary information from web pages and save it in a database.

[0207] Input: API request, web page URL

[0208] Output: Latest information on accommodations and restaurants (JSON format)

[0209] Step 2: Data analysis

[0210] The server analyzes the collected data using AI algorithms (e.g., Scikit-learn, TensorFlow). Specifically, it calculates an evaluation score based on the accommodation's price, reviews, location, and services provided. It also calculates the optimal delivery route and time.

[0211] Input: Collected accommodation and restaurant data

[0212] Output: Evaluation score, optimal delivery route

[0213] Step 3: User Request Input

[0214] Users use their smartphones or PCs to input the conditions for booking accommodation or ordering delivery (e.g., accommodation dates, budget, type of food, delivery time), and the input data is sent from the device to the server.

[0215] Input: Accommodation and order conditions entered by the user

[0216] Output: Request data sent to the server

[0217] Step 4: Conditional filtering

[0218] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price and time, narrowing down the options from a large number of options to the best plan.

[0219] Input: User request data, accommodation and restaurant data in the database

[0220] Output: Optimal accommodation and delivery plan candidates

[0221] Step 5: Presenting the optimal plan

[0222] The server then creates the best accommodation and delivery plan for the user based on the filtered results and displays it on the user's device. The displayed plan includes detailed information about each facility (e.g., name, address, price, reviews), the best delivery time, and menu details.

[0223] Input: Best accommodation and delivery plan candidates

[0224] Output: Accommodation plan and delivery plan presented

[0225] Step 6: Selecting Users

[0226] The user selects the desired plan from the presented options and presses the confirm button. The selected data is sent from the terminal to the server.

[0227] Input: User's choice

[0228] Output: Confirmed booking and order data

[0229] Step 7: Booking and ordering process

[0230] The server sends reservation requests and orders to accommodations and restaurants based on the user's selection. It receives reservation and order confirmation information via API and stores it in a database. At the same time, it sends a confirmation notification of the reservation and order to the user's device.

[0231] Input: Confirmed reservation and order data

[0232] Output: Reservation and order confirmation information, notification to user

[0233] 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.

[0234] The present invention combines an emotion engine with an AI system for optimizing accommodation use, and can be specifically implemented as follows.

[0235] 1. Data Collection

[0236] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server also uses web scraping technology to collect information. The data collected during the collection process includes detailed information such as hotel name, address, rates, availability, and available services.

[0237] 2. Data Analysis

[0238] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation facility. This rating score is a comprehensive score based on criteria such as price, reviews, location, and services provided, and the analysis results are stored in a database.

[0239] 3. User request input

[0240] Users use devices such as smartphones or PCs to input their desired accommodation conditions, including the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0241] 4. Manipulating the Emotion Engine

[0242] Emotion data is acquired through the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. Based on this evaluation, the server dynamically adjusts the accommodation plan it proposes.

[0243] 5. Conditional Filtering

[0244] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0245] 6. Providing the optimal plan

[0246] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results, and displays it on the terminal. The terminal also displays detailed information about each hotel (such as the hotel name, address, price, and details of available rooms), as well as specific suggestions for staying in multiple rooms or at multiple accommodations.

[0247] 7. User plan selection

[0248] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0249] 8. Reservation Procedure

[0250] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0251] 9. Confirmation and follow-up

[0252] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[0253] Specific examples

[0254] For example, if a family of five is looking for accommodation in Tokyo:

[0255] 1. Data Collection

[0256] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0257] 2. Data Analysis

[0258] The server analyzes this data using AI and calculates a rating score for each hotel.

[0259] 3. User request input

[0260] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[0261] 4. Manipulating the Emotion Engine

[0262] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0263] 5. Conditional Filtering

[0264] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[0265] 6. Providing the optimal plan

[0266] For example, the server may present a plan that combines a triple room at Hotel A with a double room at Hotel B, or a plan that splits the stay into two rooms at Hotel C.

[0267] 7. User plan selection

[0268] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[0269] 8. Reservation Procedure

[0270] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0271] 9. Confirmation and follow-up

[0272] The server sends reminders as the date of your stay approaches and collects feedback after your stay to suggest better plans for your next stay.

[0273] In this way, users can more easily find the accommodation that best suits their emotional state, and accommodation providers can also make effective use of available rooms.

[0274] The processing flow will be explained below.

[0275] Step 1: Data collection

[0276] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server uses web scraping technology to extract information from official websites and public databases. The collected data includes hotel names, addresses, rates, availability, and services offered.

[0277] Step 2: Data analysis

[0278] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is calculated based on price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent conditional filtering.

[0279] Step 3: User Request Input

[0280] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0281] Step 4: Manipulating the Emotion Engine

[0282] The user inputs facial expressions and voice through the device, which are analyzed by the emotion engine, which evaluates the user's emotional state in real time and sends this data to the server.

[0283] Step 5: Conditional filtering

[0284] The server filters accommodation information in the database based on the user's input criteria and emotional data. The AI ​​calculates combination plans for multiple rooms and different accommodations, taking into account price, availability, and the user's emotional state. For example, it suggests combinations of triple and double rooms, or rooms available on the same day at different hotels.

[0285] Step 6: Present the optimal plan

[0286] The server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and sends it to the terminal.The terminal displays detailed information about each hotel (hotel name, address, price, details of available rooms) and accommodation plans divided into multiple rooms and accommodation facilities.

[0287] Step 7: User Plan Selection

[0288] The user browses the accommodation plans presented and selects the most suitable one. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0289] Step 8: Booking process

[0290] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from the accommodation facility and stores this reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0291] Step 9: Confirmation and follow-up

[0292] The server sends reminders when the date of stay approaches and assists with check-in and check-out procedures. Feedback is collected after the stay and used to calculate a rating score for the next stay, allowing the server to suggest plans that will increase satisfaction.

[0293] Example 2

[0294] 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."

[0295] Conventional accommodation reservation systems have the problem of low user satisfaction because they simply propose optimal plans mechanically without considering the user's emotional state. Furthermore, conventional systems have difficulty proposing combinations of multiple accommodations, and are limited in the plans they can propose that meet specific conditions.

[0296] 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.

[0297] In this invention, the server includes means for collecting reservation status and availability information of accommodations, means for calculating evaluation scores of accommodations using an AI algorithm, means for receiving user input conditions and filtering related accommodations to propose an optimal combination, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting proposals based on the data, and means for reserving the accommodation plan selected by the user. This makes it possible to dynamically propose optimal accommodation plans according to the user's emotional state.

[0298] "Accommodation facilities" refers to facilities provided for users to stay overnight, including hotels, inns, private lodgings, guesthouses, etc.

[0299] "Reservation status" is information that indicates whether or not an accommodation facility has been booked on a specific date and time, and the availability of reservations.

[0300] "Availability information" refers to information on whether an accommodation facility has available rooms within a specified period, including the number and type of available rooms.

[0301] An "AI algorithm" refers to a computational method that uses artificial intelligence, a process that analyzes data and makes decisions to solve specific problems.

[0302] The "rating score" is a comprehensive rating calculated based on multiple evaluation criteria for the accommodation facility.

[0303] "User" refers to a person who uses this system to make reservations for accommodation.

[0304] "Input conditions" are requirements such as the date of stay, number of guests, budget, desired area, and specific services that a user specifies when reserving accommodation facilities.

[0305] "Filtering" is the process of sorting data based on input conditions and extracting only information that meets specific conditions.

[0306] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing the user's facial expressions and voice input.

[0307] "Dynamic adjustment" refers to changing the system's output and recommendations in real time based on changing conditions or new data.

[0308] An "accommodation plan" is a specific proposal for the use of an accommodation facility, and includes the date of stay, number of guests, price, room type, etc.

[0309] "Reservation" refers to the process of confirming the accommodation plan selected by the user and reserving accommodation at the accommodation facility based on that plan.

[0310] The present invention is an AI system developed to optimize accommodation use, and is characterized by taking into account the emotional state of the user. This system is specifically implemented according to the following procedure.

[0311] First, the server uses an API to collect real-time reservation status and availability information from accommodation booking sites. Furthermore, for unregistered accommodations, information is obtained using web scraping technology. This collection process uses technologies such as the Python requests library, Scrapy, and BeautifulSoup. Specific data collected includes the accommodation's name, address, price, availability, and available services.

[0312] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation using machine learning libraries such as TensorFlow and Scikit-learn. The score is calculated based on factors such as price, reviews, location, and services offered, and the analysis results are stored in an SQL database.

[0313] Users input their desired accommodation requirements using devices such as smartphones or PCs. An HTML form is used for input, and includes the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent asynchronously to the server via JavaScript (registered trademark) (AJAX).

[0314] Next, the user inputs their emotional state through facial expression and voice recognition technology, using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. This evaluation result is sent to the server for further processing.

[0315] The server filters the accommodation information in the database based on the user's input criteria and emotion data. The filtering is performed using the Pandas library, and the server calculates the optimal combination of price and availability. For example, it can suggest a combination of triple and double rooms, or rooms available on the same day at different hotels.

[0316] The server then generates an accommodation plan based on the filtered results to provide feedback to the user, which is displayed using HTML and CSS on the device, showing detailed information about each accommodation (name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[0317] The user checks the accommodation plans presented and selects the one they want. This is done using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[0318] The server then sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0319] Finally, the server sends reminders to the user as their check-in date approaches, using an SMS API such as Twilio, and collects feedback from the user after their stay, which is used to calculate future rating scores.

[0320] Specific examples

[0321] For example, if a family of five is looking for accommodation in Tokyo, the following process will occur:

[0322] 1. Data Collection

[0323] The server collects the latest information on accommodations in the Tokyo area using APIs and web scraping technology.

[0324] 2. Data Analysis

[0325] The server analyzes the collected data using an AI algorithm and calculates the accommodation's rating score.

[0326] 3. User request input

[0327] The user enters desired accommodation conditions, such as "2023-12-20, 5 people, under 50,000 yen, Tokyo, breakfast included," into the HTML form on the terminal and submits it.

[0328] 4. Manipulating the Emotion Engine

[0329] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0330] 5. Conditional Filtering

[0331] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[0332] 6. Providing the optimal plan

[0333] The server will display suggestions on the terminal, such as "a plan combining a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[0334] 7. User plan selection

[0335] The user selects the most suitable plan from the proposed plans and clicks "Reserve" to notify the server.

[0336] 8. Reservation Procedure

[0337] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0338] 9. Confirmation and follow-up

[0339] The server sends reminders as the date of the stay approaches and collects feedback after the stay.

[0340] Example prompt: "I'd like to stay in Tokyo with my family of five. My budget is under 50,000 yen per night, and I'd like a place with easy access to tourist spots. Could you suggest some accommodation plans I could use as a reference?"

[0341] In this way, the present invention allows users to easily find the most suitable accommodation that suits their emotional state, and also allows accommodation providers to efficiently utilize vacant rooms.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1:

[0344] Data collection

[0345] The server collects real-time reservation status and availability information through the API of the accommodation booking site. For unregistered accommodations, it also uses web scraping technology to obtain information. Specifically, it uses Python's requests library to obtain data from the API and BeautifulSoup to extract the necessary information from the web page. This allows it to collect information such as hotel name, address, price, availability, and available services.

[0346] Input: API endpoint URL, web page URL

[0347] Output: Detailed information about the accommodation (hotel name, address, price, availability, services)

[0348] Step 2:

[0349] Data analysis

[0350] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation. Machine learning libraries such as TensorFlow and Scikit-learn are used to calculate the rating score. Specific rating criteria include price, reviews, location, and services offered. An overall score is calculated by weighting each rating criterion, and the analysis results are stored in an SQL database.

[0351] Input: Collected accommodation details

[0352] Output: Accommodation information with rating scores

[0353] Step 3:

[0354] User request input

[0355] Users use their devices (smartphones or PCs) to enter their desired accommodation requirements. Specifically, they use an HTML form to enter the dates of stay, number of guests, budget, desired area, specific services, etc. The entered data is sent asynchronously to the server using JavaScript (AJAX).

[0356] Input: User's desired accommodation conditions (date of stay, number of people, budget, area, services)

[0357] Output: User's accommodation preferences sent to the server

[0358] Step 4:

[0359] Manipulating the Emotion Engine

[0360] Users provide emotional data through facial expressions and voice. This data is acquired using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The evaluation results are sent to the server for further processing.

[0361] Input: User's facial expression data, voice data

[0362] Output: Parsed emotional state data

[0363] Step 5:

[0364] Conditional Filtering

[0365] The server filters the accommodation information in the database based on the user's input criteria and emotion data. It uses the Pandas library to narrow down the data that matches the criteria and calculates the optimal price and available room combination. For example, it generates plans that suggest combinations of triple and double rooms, or rooms available on the same day at different hotels.

[0366] Input: User's accommodation preferences, analyzed emotional state data, accommodation information with rating scores

[0367] Output: Filtered adaptive accommodation plans

[0368] Step 6:

[0369] Providing the optimal plan

[0370] The server generates an accommodation plan based on the filtered results to provide feedback to the user. This information is displayed on the device using HTML and CSS, showing detailed information about each accommodation (accommodation name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[0371] Input: Filtered accommodation plans

[0372] Output: The best accommodation plan displayed on the device

[0373] Step 7:

[0374] User plan selection

[0375] The user checks the accommodation plans presented and selects the plan they want. This selection is made using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[0376] Input: The optimal accommodation plan displayed on the device

[0377] Output: User selections sent to the server

[0378] Step 8:

[0379] Reservation procedure

[0380] The server sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0381] Input: User selection information sent to the server

[0382] Output: Reservation request to the accommodation, confirmation of reservation to the user

[0383] Step 9:

[0384] Acknowledgments and follow-up

[0385] The server sends reminders to users as their stay approaches using an SMS API such as Twilio, and collects feedback from users after their stay, which is used to calculate future rating scores.

[0386] Input: Reservation information stored on the server

[0387] Output: Reminder notification to user, feedback from user

[0388] (Application example 2)

[0389] 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."

[0390] Conventional accommodation reservation systems were unable to present optimal accommodation plans that took into account the user's emotional state, making it difficult to meet the user's latent needs. Furthermore, because there was no dynamic feedback based on emotions, the proposed plans did not necessarily increase user satisfaction. This led to user abandonment and dissatisfaction, and accommodation operators lost opportunities to effectively utilize available rooms.

[0391] The specification process 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 collecting reservation status and availability information of accommodation facilities, means for calculating accommodation facility evaluation scores using an AI algorithm, means for receiving user input conditions and filtering related accommodation facilities to propose optimal combinations, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting the proposal content based on the emotional state, and means for reserving the accommodation plan selected by the user. This enables users to find more satisfying accommodation plans and increases opportunities for accommodation facility operators to effectively utilize available rooms.

[0392] "Accommodation reservation status" is information indicating whether or not a specific accommodation facility currently has reservations and the number of reservations.

[0393] "Vacancy information" is information indicating rooms currently available for reservation at a specific accommodation facility and the availability of each room.

[0394] "Means" refers to methods or technical devices used to achieve a particular purpose.

[0395] The "accommodation rating score" is a comprehensive rating calculated using multiple evaluation criteria such as the accommodation's price, reviews, location, and services offered.

[0396] An "AI algorithm" refers to a computational procedure or model used to solve a specific problem using artificial intelligence.

[0397] "User input conditions" refers to information that the user inputs to the system regarding the accommodation conditions desired by the user, such as the date of stay, number of guests, budget, desired area, etc.

[0398] "Related accommodations" refers to accommodations that may be suitable based on the user's input criteria.

[0399] "Filtering" refers to the process of extracting information that meets specific conditions from a large amount of information.

[0400] The "optimal combination" refers to the result of selecting from multiple options the one that best suits the user's conditions and emotional state.

[0401] "Obtaining emotional data from a user's facial expressions and voice" refers to the process of analyzing a user's facial expressions and tone of voice to identify the user's current emotional state.

[0402] "Dynamic adjustment of suggestion content based on emotional state" refers to using acquired emotional data to make optimal suggestions in real time according to the user's current mood and emotions.

[0403] An "accommodation plan" refers to a plan including specific facility names, rooms, and available services that the system proposes for a user's accommodation.

[0404] "Reservation" refers to the process of officially reserving accommodation based on the accommodation plan selected by the user.

[0405] The present invention combines an AI system with an emotion engine to optimize accommodation use. This system can be specifically implemented as follows.

[0406] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs, and also uses web scraping technology to collect information from unregistered accommodations. The collected data includes detailed information such as the accommodation's name, address, price, availability, and available services.

[0407] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is a comprehensive score based on factors such as price, reviews, location, and services provided. The analysis results are then stored in a database.

[0408] Users input their desired accommodation requirements using a device such as a smartphone or PC. These requirements include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0409] The emotion engine acquires emotional data from the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The emotion evaluation results are used to dynamically adjust the accommodation plans proposed by the server.

[0410] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0411] Next, the server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and displays it on the terminal, along with detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[0412] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0413] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0414] Finally, the server can optionally send reminders as the date of the stay approaches, assist with check-in and check-out procedures, and even collect feedback after the stay, which will be used to calculate future rating scores.

[0415] This system makes it easier for users to find the accommodation that best suits their emotional state, and also enables accommodation providers to make effective use of vacant rooms.

[0416] As a concrete example, let's consider the case where a family of five is looking for a place to stay in Tokyo. The server collects a large amount of up-to-date information on accommodations in the Tokyo area, analyzes this data using AI, and calculates a rating score for each hotel. The user inputs the conditions for accommodation in Tokyo for five people (e.g., budget, dates of stay) into the device and submits it. The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0417] The server optimizes the combination of multiple hotels and room types based on the user's requirements and emotional data, and extracts available plans. For example, a plan combining a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C, are possible. The user selects the plan they like best from the presented plans and clicks "Book" to notify the server. The server then sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information. The server sends reminders as the check-in date approaches and collects feedback after the stay to suggest better plans for the next time.

[0418] Examples of prompts for a generative AI model might include:

[0419] Prompt: "A user is trying to order a meal. Determine the user's emotional state from their facial expressions and suggest restaurants and meal options that best fit that emotion. Also consider the user's past dining history, reviews, and preferred flavors and types."

[0420] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0421] Step 1:

[0422] The server collects real-time reservation status and availability information from accommodation booking sites using APIs. This process involves obtaining real-time reservation data from designated accommodations as input and storing it in an internal database. For unregistered accommodations, the server uses web scraping technology to collect information and stores it in the internal database as well.

[0423] Step 2:

[0424] The server uses the collected data to calculate an accommodation rating score using an AI algorithm. Specifically, it uses an AI algorithm (e.g., a machine learning model) to calculate the rating score based on information such as price, reviews, location, and services provided. This score is saved in a database and can be used for subsequent filtering processes.

[0425] Step 3:

[0426] Users input their desired accommodation conditions using a device such as a smartphone or PC, and send them to the server from the device. These desired accommodation conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input data is received by the server and used for subsequent processing.

[0427] Step 4:

[0428] The device acquires emotional data from the user's facial expressions and voice input. Using an emotion engine, the device analyzes the acquired facial images and voice data to evaluate the user's emotional state in real time. The evaluation results are sent from the device to a server and used to dynamically adjust the content of suggestions.

[0429] Step 5:

[0430] The server filters accommodation information in the database based on the user's input criteria and emotional data. Specifically, it narrows down the accommodations that fit the input criteria and dynamically optimizes the suggestions based on the emotional data. Using an AI algorithm, it optimally combines information such as price and availability to prepare a proposal for a combination of multiple rooms or different accommodations.

[0431] Step 6:

[0432] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results and displays it on the device. This display includes detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[0433] Step 7:

[0434] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button on the terminal, and the information is sent from the terminal to the server.

[0435] Step 8:

[0436] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives reservation confirmation responses from each accommodation facility and stores the final reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0437] Step 9:

[0438] The server sends reminders as needed when the check-in date approaches, assists with check-in and check-out procedures, and collects feedback after the stay to be used in calculating future rating scores, improving the user experience and helping service providers improve customer satisfaction.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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."

[0455] The present invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0456] 1. Data Collection

[0457] The server collects real-time reservation status and availability information from accommodation booking sites via API. It also automatically collects data from unregistered accommodations using web scraping technology and stores it in a database for analysis. In the process, it obtains detailed information such as the accommodation's name, address, price, availability, and available services.

[0458] 2. Data Analysis

[0459] The server uses an AI algorithm to calculate a rating score for each accommodation based on the collected data. This AI algorithm uses criteria such as price, reviews, location, and services provided. The analysis results are stored in a database and can be used according to user requests.

[0460] 3. User request input

[0461] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. The entered conditions are sent to the server via the device.

[0462] 4. Conditional Filtering

[0463] The server filters the accommodation information in its database based on the user's criteria. AI, in particular, calculates the optimal combination of rates and availability, and can propose plans combining multiple rooms or different accommodations. For example, it can suggest a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0464] 5. Providing the optimal plan

[0465] The server creates an accommodation plan that best suits the user's requirements from the filtering results and displays it on the terminal. The terminal also displays detailed information about each hotel (e.g., hotel name, address, price, details of available rooms, etc.) and specific suggestions for staying in multiple rooms or accommodations.

[0466] 6. User plan selection

[0467] The user checks the displayed accommodation plans and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent to the server.

[0468] 7. Reservation Procedure

[0469] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (e.g., reservation confirmation number, check-in procedure, etc.) on the terminal.

[0470] Specific examples

[0471] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0472] 1. Data Collection

[0473] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0474] 2. Data Analysis

[0475] The server analyzes this data using AI and calculates a rating score for each hotel.

[0476] 3. User request input

[0477] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[0478] 4. Conditional Filtering

[0479] The server optimizes the combination of multiple hotels and room types based on the input conditions and extracts available plans.

[0480] 5. Providing the optimal plan

[0481] The server will present the terminal with a plan that combines, for example, a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C.

[0482] 6. User plan selection

[0483] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[0484] 7. Reservation Procedure

[0485] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0486] In this way, users can easily find suitable accommodations, and accommodation providers can also make effective use of vacant rooms.

[0487] The processing flow will be explained below.

[0488] Step 1: Data collection

[0489] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs. For unregistered accommodations, the server then uses web scraping technology to extract the necessary information from official websites and public databases. The collected data includes details such as hotel names, addresses, rates, availability, and available services.

[0490] Step 2: Data analysis

[0491] The server inputs the data collected in step 1 into an AI algorithm to calculate a rating score for each accommodation. This rating score takes into consideration a comprehensive range of evaluation criteria, including price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent filtering.

[0492] Step 3: Entering User Requests

[0493] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0494] Step 4: Conditional filtering

[0495] The server filters the accommodation information in the database based on the conditions received from the user in step 3. During filtering, the AI ​​calculates the optimal combination of rates and availability, and prepares to propose combination plans for multiple rooms or different accommodations. For example, it can propose a combination of a triple room and a double room, or rooms available on the same day at different hotels.

[0496] Step 5: Presenting the optimal plan

[0497] The server creates an accommodation plan that best suits the user's requirements based on the filtering results of step 4. This plan includes detailed information about each hotel (hotel name, address, price, available room details, etc.) and specific suggestions for staying in multiple rooms or accommodations. The created plan is sent to the terminal and displayed to the user.

[0498] Step 6: User Plan Selection

[0499] The user checks the optimal plans displayed on the terminal and selects the desired plan. The selected plan is confirmed by clicking the "Reserve" button, and the information is sent from the terminal to the server.

[0500] Step 7: Booking process

[0501] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from each accommodation facility and stores this reservation information in a database. The server also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0502] Step 8: Confirmation and follow-up

[0503] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[0504] Example 1

[0505] 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."

[0506] Conventional accommodation reservation systems have limitations in obtaining reservation status and availability information, and it is difficult to calculate accurate evaluation scores. As a result, they are not capable of proposing optimal accommodation plans for users, and it is difficult for accommodation operators to effectively utilize available rooms. Furthermore, there is no flexible way to combine multiple accommodations and room types, so they are unable to meet the diverse needs of users.

[0507] 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.

[0508] In this invention, the server includes means for collecting accommodation reservation status and availability information, means for collecting detailed information about accommodations using web scraping technology, means for calculating accommodation rating scores using an AI algorithm, means for receiving user input conditions and filtering related accommodations from a database, means for proposing optimal combinations of multiple accommodations and room types, means for reserving the accommodation plan selected by the user, and means for notifying the user of confirmed reservation information. This allows users to easily find the optimal accommodation plan and enables accommodation providers to efficiently utilize available rooms.

[0509] "Accommodation facility" means a facility that provides a building or place for guests to stay.

[0510] "Reservation status" is information that indicates the current reservation status at a specific accommodation facility.

[0511] "Availability information" is information that indicates the status of rooms or spaces currently available at a particular accommodation facility.

[0512] "Web scraping technology" is a technology that automatically collects information that is publicly available on the Internet.

[0513] The "rating score" is a number that indicates the overall rating of an accommodation facility calculated based on various evaluation criteria.

[0514] An "AI algorithm" is a set of steps or calculation methods used to solve a specific problem using artificial intelligence technology.

[0515] "User" refers to an individual or group who uses the System to search for and reserve accommodation plans.

[0516] A "database" is a collection of data that stores collected information in an organized manner and allows for efficient search and analysis.

[0517] "Filtering" is the process of sorting data based on specific conditions and extracting the necessary information.

[0518] "Accommodation plan" refers to a specific proposal that combines accommodation facilities and their terms of use.

[0519] A "reservation request" is information sent by a user to convey the intention to make a reservation to an accommodation facility.

[0520] "Reservation confirmation information" is information for notifying the user that the reservation has been confirmed by the accommodation facility.

[0521] This invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0522] The server uses the APIs of multiple accommodation booking sites to collect information on accommodation reservation status and availability. For example, the server uses Python's Requests library to send GET requests to the API endpoints of each booking site and retrieve data in JSON format. For unregistered accommodations, the server also uses Python's BeautifulSoup and Requests library to automatically collect data using web scraping technology. During this process, detailed information such as the accommodation's name, address, price, availability, and available services is obtained and stored in a database.

[0523] The server uses an AI algorithm to calculate an accommodation rating score based on the collected data. Specifically, it uses Python's Scikit-learn to calculate the rating score based on evaluation criteria such as price, reviews, location, and services provided. This allows accommodations with high ratings to be suggested to users with higher priority.

[0524] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. These conditions are collected by a front-end user interface using React and sent to the server via an HTTP POST request.

[0525] The server filters the accommodation information in the database based on the conditions received from the user. It uses SQL queries to filter and extract the accommodations that best fit the user's conditions. The AI ​​model then calculates the optimal combination of rates and availability, and creates plans combining multiple accommodations and room types. For example, Python's Pandas library can be used to efficiently find accommodations that meet the conditions.

[0526] The server then generates an accommodation plan that best suits the user's requirements based on the filtering results and displays it on the device. Using React and MUI, the device dynamically displays hotel names, addresses, rates, and detailed information about available rooms. Plan examples include "combining a triple room at Hotel A with a double room at Hotel B" and "staying in two separate rooms at Hotel C."

[0527] The user selects the desired plan from the displayed accommodation plans. The selection is made by clicking the "Book" button in React, and the selected plan information is sent to the server via an Ajax request.

[0528] Finally, the server sends a reservation request to each relevant accommodation based on the details of the accommodation plan selected by the user. Specifically, it uses the Python Requests library to send a POST request to each accommodation's API and receives a confirmation response. This information is stored in a database and the user is notified that the reservation has been confirmed. The front-end displays the reservation confirmation number and check-in details.

[0529] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0530] 1. The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0531] 2. The server analyzes this data using Scikit-learn and calculates a rating score for each hotel.

[0532] 3. The user enters conditions such as "budget is within 10,000 yen per person per night, and the date of stay is next weekend" into the terminal and submits the request.

[0533] 4. The server uses SQL queries and the Pandas library to optimize the hotel combinations and room types based on the input criteria and extract available deals.

[0534] 5. The server presents the terminal with options such as "a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[0535] 6. The user selects the plan they like best from the plans presented and clicks "Book."

[0536] 7. The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0537] This system allows users to easily find the best accommodation plan and enables accommodation providers to make effective use of available rooms.

[0538] Example prompt sentence:

[0539] "Please suggest the best accommodation plan for a group of five people looking to stay in Tokyo. The budget is 10,000 yen per person per night, and the stay is next weekend."

[0540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0541] Step 1:

[0542] The server collects accommodation reservation status and availability information. To do this, it uses the accommodation booking site's API to send a GET request and retrieve data in JSON format. It also uses Python's BeautifulSoup and Requests libraries to web scrape information on unregistered accommodations. The collected data includes the accommodation's name, address, price, availability, and available services, and is stored in a database.

[0543] Input: API request parameters and website URL

[0544] Output: JSON data containing details of accommodations and scraping results

[0545] Step 2:

[0546] The server analyzes the collected data using an AI algorithm and calculates an evaluation score for each accommodation. Specifically, it uses Python's Scikit-learn to calculate the evaluation score based on evaluation criteria such as price, reviews, location, and services provided. The lower the price and the higher the reviews, the higher the score. The analysis results are stored in a database.

[0547] Input: Accommodation details stored in the database

[0548] Output: Rating score for each accommodation

[0549] Step 3:

[0550] The user uses a terminal to input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.). The user enters the information into a front-end form using React, which is then sent to the server via an HTTP POST request.

[0551] Input: desired accommodation conditions (date of stay, number of guests, budget, desired area, specific services, etc.)

[0552] Output: User's accommodation preferences sent to the server

[0553] Step 4:

[0554] The server filters the accommodation information in the database based on the user's desired accommodations, using an SQL query to extract accommodations that match the user's criteria, and then uses Python's Pandas library to manipulate the results, including price and availability, to calculate the optimal accommodation combination.

[0555] Input: User's desired accommodation conditions, accommodation information in the database

[0556] Output: The best accommodation candidates that meet the user's criteria

[0557] Step 5:

[0558] The server creates the optimal accommodation plan based on the filtering results and sends it to the front end. The accommodation plan is dynamically displayed on the terminal using React and MUI. Detailed information includes the hotel name, address, price, and available room details, and multiple combinations of accommodations and room types are presented.

[0559] Input: Best accommodation options

[0560] Output: The best accommodation plan displayed on the device

[0561] Step 6:

[0562] The user uses the React interface to select the desired accommodation plan from the displayed options. When the user clicks the "Book" button, the selected plan information is sent to the server via an Ajax request.

[0563] Input: The accommodation plan selected by the user

[0564] Output: Selected plan information sent to the server

[0565] Step 7:

[0566] The server sends a reservation request to each relevant accommodation facility based on the details of the accommodation plan selected by the user. Using the Python Requests library, it sends a POST request to each accommodation facility's API and receives a confirmed reservation response. The final reservation information is saved in a database and a confirmation notification is sent to the user. The reservation confirmation number and check-in procedure details are displayed on the terminal.

[0567] Input: Accommodation plan information selected by the user

[0568] Output: Confirmation response from the accommodation and notification to the user

[0569] (Application example 1)

[0570] 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."

[0571] Optimizing accommodation reservations and food delivery is important for increasing user convenience while also efficiently utilizing provider resources. However, previous systems did not adequately collect real-time reservation status and availability information, or propose optimal accommodation and delivery plans. Furthermore, it was difficult to collect information on unregistered accommodations and restaurants, making it difficult to provide users with a large number of options. Furthermore, systems lacked the functionality to optimize the combination of multiple accommodations and restaurants, making it impossible to meet the diverse needs of users.

[0572] 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.

[0573] In this invention, the server includes: means for collecting accommodation reservation status and availability information; means for calculating accommodation rating scores using an AI algorithm; means for receiving user input conditions and filtering related accommodations to suggest optimal combinations; means for reserving the user's selected accommodation plan; means for collecting order conditions and analyzing restaurant menu information and delivery status; means for presenting optimal restaurants and delivery plans based on the user's input order conditions; and means for notifying delivery personnel of the user's selected order. This allows users to find optimal accommodation plans and delivery plans in real time, while also allowing providers to efficiently utilize their resources and effectively collect information on unregistered accommodations and restaurants. Furthermore, it is possible to optimize combinations of multiple accommodations and restaurants to meet diverse user needs.

[0574] "Accommodation facilities" are facilities that provide accommodation services to guests, including hotels, inns, guest houses, etc.

[0575] "Reservation status" is information that indicates the degree to which available resources such as accommodations and restaurants are occupied during a specific period or date and time.

[0576] "Availability information" is information that indicates whether accommodation facilities, restaurants, etc. are available for use during a specific period or date and time.

[0577] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and solve a specific problem.

[0578] "User" means an individual or corporation that uses the system to make accommodation reservations or use food delivery services.

[0579] "Input conditions" are requests and wishes that the user provides to the system, and include reservation date, budget, type of meal, and the like.

[0580] "Filtering" is the process of selecting information that matches specific conditions from a large amount of information.

[0581] An "accommodation plan" is a plan for using accommodation facilities proposed in response to a user's request, and includes the price, room type, service content, and the like.

[0582] "Order conditions" refer to the wishes and requests that a user provides to the food delivery system, including the type of food, budget, delivery time, etc.

[0583] "Eating establishments" are establishments that serve meals, and include restaurants, cafes, fast food restaurants, etc.

[0584] "Delivery status" is information that indicates the current order processing status and delivery area when a restaurant provides food delivery services.

[0585] A "delivery plan" is a delivery plan from a restaurant proposed based on the user's order conditions, and includes the price, delivery time, delivery route, etc.

[0586] "Web scraping" is a technique for automatically collecting information from web pages on the Internet.

[0587] An "unregistered accommodation" is an accommodation that has not been pre-registered in the system.

[0588] An "unregistered restaurant" is a restaurant that has not been pre-registered in the system.

[0589] The present invention relates to an AI system that optimizes accommodation reservations and food delivery, and is composed of three entities: a server, a terminal, and a user. Specific embodiments are described below.

[0590] System configuration

[0591] 1. Data Collection

[0592] The server collects information on accommodation reservations and availability, as well as restaurant menus and delivery status in real time. Specifically, it collects information through APIs, and for unregistered accommodations and restaurants, it uses web scraping technology (e.g., BeautifulSoup, Selenium).

[0593] 2. Data Analysis

[0594] The server uses AI algorithms (e.g., Scikit-learn, TensorFlow) to analyze the collected data, including calculating the accommodation's rating score and the optimal delivery route.

[0595] 3. User request input

[0596] Users input their hotel reservation and delivery order requirements using devices such as smartphones or PCs, using an interface developed using React Native and Flutter.

[0597] 4. Conditional Filtering

[0598] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price, time, etc.

[0599] 5. Providing the optimal plan

[0600] The server creates accommodation and delivery plans that best suit the user's needs from the filtered results and displays them on the user's device.

[0601] 6. User Choice

[0602] The user selects one of the plans presented and presses the confirm button.

[0603] 7. Booking and Ordering Procedures

[0604] The server sends a reservation request and order to the accommodation facility or restaurant based on the selected plan, and stores the confirmed information in a database.

[0605] Hardware and software used

[0606] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[0607] Data Collection: RESTful API, BeautifulSoup, Selenium

[0608] Data analysis: Scikit-learn, TensorFlow

[0609] User Interface: React Native, Flutter

[0610] Specific examples

[0611] For example, if a group of five people are looking for accommodation in Tokyo and want to order dinner delivery for that night, here's how it works:

[0612] 1. The server collects information on accommodations and delivery restaurants in the Tokyo area.

[0613] 2. Analyze the collected data using AI to optimize accommodation rating scores and delivery plans.

[0614] 3. Users enter their accommodation and delivery requirements into the app.

[0615] 4. The server then filters the most suitable accommodation and delivery plans based on this information.

[0616] 5. The filtered results are presented to the user, who selects and confirms the desired plan.

[0617] 6. The server sends reservation and order requests to accommodations and restaurants based on the selected plan.

[0618] Prompt Sentence Examples

[0619] "Design an AI system that collects information on delivery restaurants in the Tokyo area and provides the optimal delivery plan based on the user's order criteria. The system should have the ability to suggest the optimal restaurant and menu combination based on the user's criteria, such as type of food, budget, and delivery time."

[0620] As described above, the present invention provides a system that optimizes accommodation reservations and food delivery, thereby significantly improving user convenience.

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1: Data collection

[0623] The server collects accommodation reservation status and availability information, restaurant menu information, and delivery status in real time via API. It also uses web scraping technology to collect information on unregistered accommodations and restaurants. Specifically, it sends API requests and stores the received data in a database in JSON format. Web scraping uses BeautifulSoup and Selenium to analyze the necessary information from web pages and save it in a database.

[0624] Input: API request, web page URL

[0625] Output: Latest information on accommodations and restaurants (JSON format)

[0626] Step 2: Data analysis

[0627] The server analyzes the collected data using AI algorithms (e.g., Scikit-learn, TensorFlow). Specifically, it calculates an evaluation score based on the accommodation's price, reviews, location, and services provided. It also calculates the optimal delivery route and time.

[0628] Input: Collected accommodation and restaurant data

[0629] Output: Evaluation score, optimal delivery route

[0630] Step 3: User Request Input

[0631] Users use their smartphones or PCs to input the conditions for booking accommodation or ordering delivery (e.g., accommodation dates, budget, type of food, delivery time), and the input data is sent from the device to the server.

[0632] Input: Accommodation and order conditions entered by the user

[0633] Output: Request data sent to the server

[0634] Step 4: Conditional filtering

[0635] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price and time, narrowing down the options from a large number of options to the best plan.

[0636] Input: User request data, accommodation and restaurant data in the database

[0637] Output: Optimal accommodation and delivery plan candidates

[0638] Step 5: Presenting the optimal plan

[0639] The server then creates the best accommodation and delivery plan for the user based on the filtered results and displays it on the user's device. The displayed plan includes detailed information about each facility (e.g., name, address, price, reviews), the best delivery time, and menu details.

[0640] Input: Best accommodation and delivery plan candidates

[0641] Output: Accommodation plan and delivery plan presented

[0642] Step 6: Selecting Users

[0643] The user selects the desired plan from the presented options and presses the confirm button. The selected data is sent from the terminal to the server.

[0644] Input: User's choice

[0645] Output: Confirmed booking and order data

[0646] Step 7: Booking and ordering process

[0647] The server sends reservation requests and orders to accommodations and restaurants based on the user's selection. It receives reservation and order confirmation information via API and stores it in a database. At the same time, it sends a confirmation notification of the reservation and order to the user's device.

[0648] Input: Confirmed reservation and order data

[0649] Output: Reservation and order confirmation information, notification to user

[0650] 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.

[0651] The present invention combines an emotion engine with an AI system for optimizing accommodation use, and can be specifically implemented as follows.

[0652] 1. Data Collection

[0653] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server also uses web scraping technology to collect information. The data collected during the collection process includes detailed information such as hotel name, address, rates, availability, and available services.

[0654] 2. Data Analysis

[0655] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation facility. This rating score is a comprehensive score based on criteria such as price, reviews, location, and services provided, and the analysis results are stored in a database.

[0656] 3. User request input

[0657] Users use devices such as smartphones or PCs to input their desired accommodation conditions, including the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0658] 4. Manipulating the Emotion Engine

[0659] Emotion data is acquired through the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. Based on this evaluation, the server dynamically adjusts the accommodation plan it proposes.

[0660] 5. Conditional Filtering

[0661] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0662] 6. Providing the optimal plan

[0663] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results, and displays it on the terminal. The terminal also displays detailed information about each hotel (such as the hotel name, address, price, and details of available rooms), as well as specific suggestions for staying in multiple rooms or at multiple accommodations.

[0664] 7. User plan selection

[0665] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0666] 8. Reservation Procedure

[0667] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0668] 9. Confirmation and follow-up

[0669] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[0670] Specific examples

[0671] For example, if a family of five is looking for accommodation in Tokyo:

[0672] 1. Data Collection

[0673] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0674] 2. Data Analysis

[0675] The server analyzes this data using AI and calculates a rating score for each hotel.

[0676] 3. User request input

[0677] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[0678] 4. Manipulating the Emotion Engine

[0679] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0680] 5. Conditional Filtering

[0681] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[0682] 6. Providing the optimal plan

[0683] For example, the server may present a plan that combines a triple room at Hotel A with a double room at Hotel B, or a plan that splits the stay into two rooms at Hotel C.

[0684] 7. User plan selection

[0685] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[0686] 8. Reservation Procedure

[0687] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0688] 9. Confirmation and follow-up

[0689] The server sends reminders as the date of your stay approaches and collects feedback after your stay to suggest better plans for your next stay.

[0690] In this way, users can more easily find the accommodation that best suits their emotional state, and accommodation providers can also make effective use of available rooms.

[0691] The processing flow will be explained below.

[0692] Step 1: Data collection

[0693] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server uses web scraping technology to extract information from official websites and public databases. The collected data includes hotel names, addresses, rates, availability, and services offered.

[0694] Step 2: Data analysis

[0695] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is calculated based on price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent conditional filtering.

[0696] Step 3: User Request Input

[0697] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0698] Step 4: Manipulating the Emotion Engine

[0699] The user inputs facial expressions and voice through the device, which are analyzed by the emotion engine, which evaluates the user's emotional state in real time and sends this data to the server.

[0700] Step 5: Conditional filtering

[0701] The server filters accommodation information in the database based on the user's input criteria and emotional data. The AI ​​calculates combination plans for multiple rooms and different accommodations, taking into account price, availability, and the user's emotional state. For example, it suggests combinations of triple and double rooms, or rooms available on the same day at different hotels.

[0702] Step 6: Present the optimal plan

[0703] The server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and sends it to the terminal.The terminal displays detailed information about each hotel (hotel name, address, price, details of available rooms) and accommodation plans divided into multiple rooms and accommodation facilities.

[0704] Step 7: User Plan Selection

[0705] The user browses the accommodation plans presented and selects the most suitable one. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0706] Step 8: Booking process

[0707] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from the accommodation facility and stores this reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0708] Step 9: Confirmation and follow-up

[0709] The server sends reminders when the date of stay approaches and assists with check-in and check-out procedures. Feedback is collected after the stay and used to calculate a rating score for the next stay, allowing the server to suggest plans that will increase satisfaction.

[0710] Example 2

[0711] 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."

[0712] Conventional accommodation reservation systems have the problem of low user satisfaction because they simply propose optimal plans mechanically without considering the user's emotional state. Furthermore, conventional systems have difficulty proposing combinations of multiple accommodations, and are limited in the plans they can propose that meet specific conditions.

[0713] 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.

[0714] In this invention, the server includes means for collecting reservation status and availability information of accommodations, means for calculating evaluation scores of accommodations using an AI algorithm, means for receiving user input conditions and filtering related accommodations to propose an optimal combination, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting proposals based on the data, and means for reserving the accommodation plan selected by the user. This makes it possible to dynamically propose optimal accommodation plans according to the user's emotional state.

[0715] "Accommodation facilities" refers to facilities provided for users to stay overnight, including hotels, inns, private lodgings, guesthouses, etc.

[0716] "Reservation status" is information that indicates whether or not an accommodation facility has been booked on a specific date and time, and the availability of reservations.

[0717] "Availability information" refers to information on whether an accommodation facility has available rooms within a specified period, including the number and type of available rooms.

[0718] An "AI algorithm" refers to a computational method that uses artificial intelligence, a process that analyzes data and makes decisions to solve specific problems.

[0719] The "rating score" is a comprehensive rating calculated based on multiple evaluation criteria for the accommodation facility.

[0720] "User" refers to a person who uses this system to make reservations for accommodation.

[0721] "Input conditions" are requirements such as the date of stay, number of guests, budget, desired area, and specific services that a user specifies when reserving accommodation facilities.

[0722] "Filtering" is the process of sorting data based on input conditions and extracting only information that meets specific conditions.

[0723] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing the user's facial expressions and voice input.

[0724] "Dynamic adjustment" refers to changing the system's output and recommendations in real time based on changing conditions or new data.

[0725] An "accommodation plan" is a specific proposal for the use of an accommodation facility, and includes the date of stay, number of guests, price, room type, etc.

[0726] "Reservation" refers to the process of confirming the accommodation plan selected by the user and reserving accommodation at the accommodation facility based on that plan.

[0727] The present invention is an AI system developed to optimize accommodation use, and is characterized by taking into account the emotional state of the user. This system is specifically implemented according to the following procedure.

[0728] First, the server uses an API to collect real-time reservation status and availability information from accommodation booking sites. Furthermore, for unregistered accommodations, information is obtained using web scraping technology. This collection process uses technologies such as the Python requests library, Scrapy, and BeautifulSoup. Specific data collected includes the accommodation's name, address, price, availability, and available services.

[0729] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation using machine learning libraries such as TensorFlow and Scikit-learn. The score is calculated based on factors such as price, reviews, location, and services offered, and the analysis results are stored in an SQL database.

[0730] Users input their desired accommodation requirements using devices such as smartphones or PCs. An HTML form is used to input information, including the date of stay, number of guests, budget, desired area, and specific services. The input information is sent asynchronously to the server via JavaScript (AJAX).

[0731] Next, the user inputs their emotional state through facial expression and voice recognition technology, using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. This evaluation result is sent to the server for further processing.

[0732] The server filters the accommodation information in the database based on the user's input criteria and emotion data. The filtering is performed using the Pandas library, and the server calculates the optimal combination of price and availability. For example, it can suggest a combination of triple and double rooms, or rooms available on the same day at different hotels.

[0733] The server then generates an accommodation plan based on the filtered results to provide feedback to the user, which is displayed using HTML and CSS on the device, showing detailed information about each accommodation (name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[0734] The user checks the accommodation plans presented and selects the one they want. This is done using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[0735] The server then sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0736] Finally, the server sends reminders to the user as their check-in date approaches, using an SMS API such as Twilio, and collects feedback from the user after their stay, which is used to calculate future rating scores.

[0737] Specific examples

[0738] For example, if a family of five is looking for accommodation in Tokyo, the following process will occur:

[0739] 1. Data Collection

[0740] The server collects the latest information on accommodations in the Tokyo area using APIs and web scraping technology.

[0741] 2. Data Analysis

[0742] The server analyzes the collected data using an AI algorithm and calculates the accommodation's rating score.

[0743] 3. User request input

[0744] The user enters desired accommodation conditions, such as "2023-12-20, 5 people, under 50,000 yen, Tokyo, breakfast included," into the HTML form on the terminal and submits it.

[0745] 4. Manipulating the Emotion Engine

[0746] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0747] 5. Conditional Filtering

[0748] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[0749] 6. Providing the optimal plan

[0750] The server will display suggestions on the terminal, such as "a plan combining a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[0751] 7. User plan selection

[0752] The user selects the most suitable plan from the proposed plans and clicks "Reserve" to notify the server.

[0753] 8. Reservation Procedure

[0754] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0755] 9. Confirmation and follow-up

[0756] The server sends reminders as the date of the stay approaches and collects feedback after the stay.

[0757] Example prompt: "I'd like to stay in Tokyo with my family of five. My budget is under 50,000 yen per night, and I'd like a place with easy access to tourist spots. Could you suggest some accommodation plans I could use as a reference?"

[0758] In this way, the present invention allows users to easily find the most suitable accommodation that suits their emotional state, and also allows accommodation providers to efficiently utilize vacant rooms.

[0759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] Data collection

[0762] The server collects real-time reservation status and availability information through the API of the accommodation booking site. For unregistered accommodations, it also uses web scraping technology to obtain information. Specifically, it uses Python's requests library to obtain data from the API and BeautifulSoup to extract the necessary information from the web page. This allows it to collect information such as hotel name, address, price, availability, and available services.

[0763] Input: API endpoint URL, web page URL

[0764] Output: Detailed information about the accommodation (hotel name, address, price, availability, services)

[0765] Step 2:

[0766] Data analysis

[0767] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation. Machine learning libraries such as TensorFlow and Scikit-learn are used to calculate the rating score. Specific rating criteria include price, reviews, location, and services offered. An overall score is calculated by weighting each rating criterion, and the analysis results are stored in an SQL database.

[0768] Input: Collected accommodation details

[0769] Output: Accommodation information with rating scores

[0770] Step 3:

[0771] User request input

[0772] Users use their devices (smartphones or PCs) to enter their desired accommodation requirements. Specifically, they use an HTML form to enter the dates of stay, number of guests, budget, desired area, specific services, etc. The entered data is sent asynchronously to the server using JavaScript (AJAX).

[0773] Input: User's desired accommodation conditions (date of stay, number of people, budget, area, services)

[0774] Output: User's accommodation preferences sent to the server

[0775] Step 4:

[0776] Manipulating the Emotion Engine

[0777] Users provide emotional data through facial expressions and voice. This data is acquired using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The evaluation results are sent to the server for further processing.

[0778] Input: User's facial expression data, voice data

[0779] Output: Parsed emotional state data

[0780] Step 5:

[0781] Conditional Filtering

[0782] The server filters the accommodation information in the database based on the user's input criteria and emotion data. It uses the Pandas library to narrow down the data that matches the criteria and calculates the optimal price and available room combination. For example, it generates plans that suggest combinations of triple and double rooms, or rooms available on the same day at different hotels.

[0783] Input: User's accommodation preferences, analyzed emotional state data, accommodation information with rating scores

[0784] Output: Filtered adaptive accommodation plans

[0785] Step 6:

[0786] Providing the optimal plan

[0787] The server generates an accommodation plan based on the filtered results to provide feedback to the user. This information is displayed on the device using HTML and CSS, showing detailed information about each accommodation (accommodation name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[0788] Input: Filtered accommodation plans

[0789] Output: The best accommodation plan displayed on the device

[0790] Step 7:

[0791] User plan selection

[0792] The user checks the accommodation plans presented and selects the plan they want. This selection is made using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[0793] Input: The optimal accommodation plan displayed on the device

[0794] Output: User selections sent to the server

[0795] Step 8:

[0796] Reservation procedure

[0797] The server sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0798] Input: User selection information sent to the server

[0799] Output: Reservation request to the accommodation, confirmation of reservation to the user

[0800] Step 9:

[0801] Acknowledgments and follow-up

[0802] The server sends reminders to users as their stay approaches using an SMS API such as Twilio, and collects feedback from users after their stay, which is used to calculate future rating scores.

[0803] Input: Reservation information stored on the server

[0804] Output: Reminder notification to user, feedback from user

[0805] (Application example 2)

[0806] 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."

[0807] Conventional accommodation reservation systems were unable to present optimal accommodation plans that took into account the user's emotional state, making it difficult to meet the user's latent needs. Furthermore, because there was no dynamic feedback based on emotions, the proposed plans did not necessarily increase user satisfaction. This led to user abandonment and dissatisfaction, and accommodation operators lost opportunities to effectively utilize available rooms.

[0808] The specification process 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 collecting reservation status and availability information of accommodation facilities, means for calculating accommodation facility evaluation scores using an AI algorithm, means for receiving user input conditions and filtering related accommodation facilities to propose optimal combinations, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting the proposal content based on the emotional state, and means for reserving the accommodation plan selected by the user. This enables users to find more satisfying accommodation plans and increases opportunities for accommodation facility operators to effectively utilize available rooms.

[0809] "Accommodation reservation status" is information indicating whether or not a specific accommodation facility currently has reservations and the number of reservations.

[0810] "Vacancy information" is information indicating rooms currently available for reservation at a specific accommodation facility and the availability of each room.

[0811] "Means" refers to methods or technical devices used to achieve a particular purpose.

[0812] The "accommodation rating score" is a comprehensive rating calculated using multiple evaluation criteria such as the accommodation's price, reviews, location, and services offered.

[0813] An "AI algorithm" refers to a computational procedure or model used to solve a specific problem using artificial intelligence.

[0814] "User input conditions" refers to information that the user inputs to the system regarding the accommodation conditions desired by the user, such as the date of stay, number of guests, budget, desired area, etc.

[0815] "Related accommodations" refers to accommodations that may be suitable based on the user's input criteria.

[0816] "Filtering" refers to the process of extracting information that meets specific conditions from a large amount of information.

[0817] The "optimal combination" refers to the result of selecting from multiple options the one that best suits the user's conditions and emotional state.

[0818] "Obtaining emotional data from a user's facial expressions and voice" refers to the process of analyzing a user's facial expressions and tone of voice to identify the user's current emotional state.

[0819] "Dynamic adjustment of suggestion content based on emotional state" refers to using acquired emotional data to make optimal suggestions in real time according to the user's current mood and emotions.

[0820] An "accommodation plan" refers to a plan including specific facility names, rooms, and available services that the system proposes for a user's accommodation.

[0821] "Reservation" refers to the process of officially reserving accommodation based on the accommodation plan selected by the user.

[0822] The present invention combines an AI system with an emotion engine to optimize accommodation use. This system can be specifically implemented as follows.

[0823] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs, and also uses web scraping technology to collect information from unregistered accommodations. The collected data includes detailed information such as the accommodation's name, address, price, availability, and available services.

[0824] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is a comprehensive score based on factors such as price, reviews, location, and services provided. The analysis results are then stored in a database.

[0825] Users input their desired accommodation requirements using a device such as a smartphone or PC. These requirements include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0826] The emotion engine acquires emotional data from the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The emotion evaluation results are used to dynamically adjust the accommodation plans proposed by the server.

[0827] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0828] Next, the server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and displays it on the terminal, along with detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[0829] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[0830] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0831] Finally, the server can optionally send reminders as the date of the stay approaches, assist with check-in and check-out procedures, and even collect feedback after the stay, which will be used to calculate future rating scores.

[0832] This system makes it easier for users to find the accommodation that best suits their emotional state, and also enables accommodation providers to make effective use of vacant rooms.

[0833] As a concrete example, let's consider the case where a family of five is looking for a place to stay in Tokyo. The server collects a large amount of up-to-date information on accommodations in the Tokyo area, analyzes this data using AI, and calculates a rating score for each hotel. The user inputs the conditions for accommodation in Tokyo for five people (e.g., budget, dates of stay) into the device and submits it. The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[0834] The server optimizes the combination of multiple hotels and room types based on the user's requirements and emotional data, and extracts available plans. For example, a plan combining a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C, are possible. The user selects the plan they like best from the presented plans and clicks "Book" to notify the server. The server then sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information. The server sends reminders as the check-in date approaches and collects feedback after the stay to suggest better plans for the next time.

[0835] Examples of prompts for a generative AI model might include:

[0836] Prompt: "A user is trying to order a meal. Determine the user's emotional state from their facial expressions and suggest restaurants and meal options that best fit that emotion. Also consider the user's past dining history, reviews, and preferred flavors and types."

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] The server collects real-time reservation status and availability information from accommodation booking sites using APIs. This process involves obtaining real-time reservation data from designated accommodations as input and storing it in an internal database. For unregistered accommodations, the server uses web scraping technology to collect information and stores it in the internal database as well.

[0840] Step 2:

[0841] The server uses the collected data to calculate an accommodation rating score using an AI algorithm. Specifically, it uses an AI algorithm (e.g., a machine learning model) to calculate the rating score based on information such as price, reviews, location, and services provided. This score is saved in a database and can be used for subsequent filtering processes.

[0842] Step 3:

[0843] Users input their desired accommodation conditions using a device such as a smartphone or PC, and send them to the server from the device. These desired accommodation conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input data is received by the server and used for subsequent processing.

[0844] Step 4:

[0845] The device acquires emotional data from the user's facial expressions and voice input. Using an emotion engine, the device analyzes the acquired facial images and voice data to evaluate the user's emotional state in real time. The evaluation results are sent from the device to a server and used to dynamically adjust the content of suggestions.

[0846] Step 5:

[0847] The server filters accommodation information in the database based on the user's input criteria and emotional data. Specifically, it narrows down the accommodations that fit the input criteria and dynamically optimizes the suggestions based on the emotional data. Using an AI algorithm, it optimally combines information such as price and availability to prepare a proposal for a combination of multiple rooms or different accommodations.

[0848] Step 6:

[0849] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results and displays it on the device. This display includes detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[0850] Step 7:

[0851] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button on the terminal, and the information is sent from the terminal to the server.

[0852] Step 8:

[0853] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives reservation confirmation responses from each accommodation facility and stores the final reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0854] Step 9:

[0855] The server sends reminders as needed when the check-in date approaches, assists with check-in and check-out procedures, and collects feedback after the stay to be used in calculating future rating scores, improving the user experience and helping service providers improve customer satisfaction.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] The present invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0873] 1. Data Collection

[0874] The server collects real-time reservation status and availability information from accommodation booking sites via API. It also automatically collects data from unregistered accommodations using web scraping technology and stores it in a database for analysis. In the process, it obtains detailed information such as the accommodation's name, address, price, availability, and available services.

[0875] 2. Data Analysis

[0876] The server uses an AI algorithm to calculate a rating score for each accommodation based on the collected data. This AI algorithm uses criteria such as price, reviews, location, and services provided. The analysis results are stored in a database and can be used according to user requests.

[0877] 3. User request input

[0878] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. The entered conditions are sent to the server via the device.

[0879] 4. Conditional Filtering

[0880] The server filters the accommodation information in its database based on the user's criteria. AI, in particular, calculates the optimal combination of rates and availability, and can propose plans combining multiple rooms or different accommodations. For example, it can suggest a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[0881] 5. Providing the optimal plan

[0882] The server creates an accommodation plan that best suits the user's requirements from the filtering results and displays it on the terminal. The terminal also displays detailed information about each hotel (e.g., hotel name, address, price, details of available rooms, etc.) and specific suggestions for staying in multiple rooms or accommodations.

[0883] 6. User plan selection

[0884] The user checks the displayed accommodation plans and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent to the server.

[0885] 7. Reservation Procedure

[0886] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (e.g., reservation confirmation number, check-in procedure, etc.) on the terminal.

[0887] Specific examples

[0888] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0889] 1. Data Collection

[0890] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0891] 2. Data Analysis

[0892] The server analyzes this data using AI and calculates a rating score for each hotel.

[0893] 3. User request input

[0894] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[0895] 4. Conditional Filtering

[0896] The server optimizes the combination of multiple hotels and room types based on the input conditions and extracts available plans.

[0897] 5. Providing the optimal plan

[0898] The server will present the terminal with a plan that combines, for example, a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C.

[0899] 6. User plan selection

[0900] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[0901] 7. Reservation Procedure

[0902] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0903] In this way, users can easily find suitable accommodations, and accommodation providers can also make effective use of vacant rooms.

[0904] The processing flow will be explained below.

[0905] Step 1: Data collection

[0906] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs. For unregistered accommodations, the server then uses web scraping technology to extract the necessary information from official websites and public databases. The collected data includes details such as hotel names, addresses, rates, availability, and available services.

[0907] Step 2: Data analysis

[0908] The server inputs the data collected in step 1 into an AI algorithm to calculate a rating score for each accommodation. This rating score takes into consideration a comprehensive range of evaluation criteria, including price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent filtering.

[0909] Step 3: Entering User Requests

[0910] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[0911] Step 4: Conditional filtering

[0912] The server filters the accommodation information in the database based on the conditions received from the user in step 3. During filtering, the AI ​​calculates the optimal combination of rates and availability, and prepares to propose combination plans for multiple rooms or different accommodations. For example, it can propose a combination of a triple room and a double room, or rooms available on the same day at different hotels.

[0913] Step 5: Presenting the optimal plan

[0914] The server creates an accommodation plan that best suits the user's requirements based on the filtering results of step 4. This plan includes detailed information about each hotel (hotel name, address, price, available room details, etc.) and specific suggestions for staying in multiple rooms or accommodations. The created plan is sent to the terminal and displayed to the user.

[0915] Step 6: User Plan Selection

[0916] The user checks the optimal plans displayed on the terminal and selects the desired plan. The selected plan is confirmed by clicking the "Reserve" button, and the information is sent from the terminal to the server.

[0917] Step 7: Booking process

[0918] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from each accommodation facility and stores this reservation information in a database. The server also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[0919] Step 8: Confirmation and follow-up

[0920] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[0921] Example 1

[0922] 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."

[0923] Conventional accommodation reservation systems have limitations in obtaining reservation status and availability information, and it is difficult to calculate accurate evaluation scores. As a result, they are not capable of proposing optimal accommodation plans for users, and it is difficult for accommodation operators to effectively utilize available rooms. Furthermore, there is no flexible way to combine multiple accommodations and room types, so they are unable to meet the diverse needs of users.

[0924] 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.

[0925] In this invention, the server includes means for collecting accommodation reservation status and availability information, means for collecting detailed information about accommodations using web scraping technology, means for calculating accommodation rating scores using an AI algorithm, means for receiving user input conditions and filtering related accommodations from a database, means for proposing optimal combinations of multiple accommodations and room types, means for reserving the accommodation plan selected by the user, and means for notifying the user of confirmed reservation information. This allows users to easily find the optimal accommodation plan and enables accommodation providers to efficiently utilize available rooms.

[0926] "Accommodation facility" means a facility that provides a building or place for guests to stay.

[0927] "Reservation status" is information that indicates the current reservation status at a specific accommodation facility.

[0928] "Availability information" is information that indicates the status of rooms or spaces currently available at a particular accommodation facility.

[0929] "Web scraping technology" is a technology that automatically collects information that is publicly available on the Internet.

[0930] The "rating score" is a number that indicates the overall rating of an accommodation facility calculated based on various evaluation criteria.

[0931] An "AI algorithm" is a set of steps or calculation methods used to solve a specific problem using artificial intelligence technology.

[0932] "User" refers to an individual or group who uses the System to search for and reserve accommodation plans.

[0933] A "database" is a collection of data that stores collected information in an organized manner and allows for efficient search and analysis.

[0934] "Filtering" is the process of sorting data based on specific conditions and extracting the necessary information.

[0935] "Accommodation plan" refers to a specific proposal that combines accommodation facilities and their terms of use.

[0936] A "reservation request" is information sent by a user to convey the intention to make a reservation to an accommodation facility.

[0937] "Reservation confirmation information" is information for notifying the user that the reservation has been confirmed by the accommodation facility.

[0938] This invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[0939] The server uses the APIs of multiple accommodation booking sites to collect information on accommodation reservation status and availability. For example, the server uses Python's Requests library to send GET requests to the API endpoints of each booking site and retrieve data in JSON format. For unregistered accommodations, the server also uses Python's BeautifulSoup and Requests library to automatically collect data using web scraping technology. During this process, detailed information such as the accommodation's name, address, price, availability, and available services is obtained and stored in a database.

[0940] The server uses an AI algorithm to calculate an accommodation rating score based on the collected data. Specifically, it uses Python's Scikit-learn to calculate the rating score based on evaluation criteria such as price, reviews, location, and services provided. This allows accommodations with high ratings to be suggested to users with higher priority.

[0941] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. These conditions are collected by a front-end user interface using React and sent to the server via an HTTP POST request.

[0942] The server filters the accommodation information in the database based on the conditions received from the user. It uses SQL queries to filter and extract the accommodations that best fit the user's conditions. The AI ​​model then calculates the optimal combination of rates and availability, and creates plans combining multiple accommodations and room types. For example, Python's Pandas library can be used to efficiently find accommodations that meet the conditions.

[0943] The server then generates an accommodation plan that best suits the user's requirements based on the filtering results and displays it on the device. Using React and MUI, the device dynamically displays hotel names, addresses, rates, and detailed information about available rooms. Plan examples include "combining a triple room at Hotel A with a double room at Hotel B" and "staying in two separate rooms at Hotel C."

[0944] The user selects the desired plan from the displayed accommodation plans. The selection is made by clicking the "Book" button in React, and the selected plan information is sent to the server via an Ajax request.

[0945] Finally, the server sends a reservation request to each relevant accommodation based on the details of the accommodation plan selected by the user. Specifically, it uses the Python Requests library to send a POST request to each accommodation's API and receives a confirmation response. This information is stored in a database and the user is notified that the reservation has been confirmed. The front-end displays the reservation confirmation number and check-in details.

[0946] For example, if a group of 5 people are looking for accommodation in Tokyo:

[0947] 1. The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[0948] 2. The server analyzes this data using Scikit-learn and calculates a rating score for each hotel.

[0949] 3. The user enters conditions such as "budget is within 10,000 yen per person per night, and the date of stay is next weekend" into the terminal and submits the request.

[0950] 4. The server uses SQL queries and the Pandas library to optimize the hotel combinations and room types based on the input criteria and extract available deals.

[0951] 5. The server presents the terminal with options such as "a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[0952] 6. The user selects the plan they like best from the plans presented and clicks "Book."

[0953] 7. The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[0954] This system allows users to easily find the best accommodation plan and enables accommodation providers to make effective use of available rooms.

[0955] Example prompt sentence:

[0956] "Please suggest the best accommodation plan for a group of five people looking to stay in Tokyo. The budget is 10,000 yen per person per night, and the stay is next weekend."

[0957] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0958] Step 1:

[0959] The server collects accommodation reservation status and availability information. To do this, it uses the accommodation booking site's API to send a GET request and retrieve data in JSON format. It also uses Python's BeautifulSoup and Requests libraries to web scrape information on unregistered accommodations. The collected data includes the accommodation's name, address, price, availability, and available services, and is stored in a database.

[0960] Input: API request parameters and website URL

[0961] Output: JSON data containing details of accommodations and scraping results

[0962] Step 2:

[0963] The server analyzes the collected data using an AI algorithm and calculates an evaluation score for each accommodation. Specifically, it uses Python's Scikit-learn to calculate the evaluation score based on evaluation criteria such as price, reviews, location, and services provided. The lower the price and the higher the reviews, the higher the score. The analysis results are stored in a database.

[0964] Input: Accommodation details stored in the database

[0965] Output: Rating score for each accommodation

[0966] Step 3:

[0967] The user uses a terminal to input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.). The user enters the information into a front-end form using React, which is then sent to the server via an HTTP POST request.

[0968] Input: desired accommodation conditions (date of stay, number of guests, budget, desired area, specific services, etc.)

[0969] Output: User's accommodation preferences sent to the server

[0970] Step 4:

[0971] The server filters the accommodation information in the database based on the user's desired accommodations, using an SQL query to extract accommodations that match the user's criteria, and then uses Python's Pandas library to manipulate the results, including price and availability, to calculate the optimal accommodation combination.

[0972] Input: User's desired accommodation conditions, accommodation information in the database

[0973] Output: The best accommodation candidates that meet the user's criteria

[0974] Step 5:

[0975] The server creates the optimal accommodation plan based on the filtering results and sends it to the front end. The accommodation plan is dynamically displayed on the terminal using React and MUI. Detailed information includes the hotel name, address, price, and available room details, and multiple combinations of accommodations and room types are presented.

[0976] Input: Best accommodation options

[0977] Output: The best accommodation plan displayed on the device

[0978] Step 6:

[0979] The user uses the React interface to select the desired accommodation plan from the displayed options. When the user clicks the "Book" button, the selected plan information is sent to the server via an Ajax request.

[0980] Input: The accommodation plan selected by the user

[0981] Output: Selected plan information sent to the server

[0982] Step 7:

[0983] The server sends a reservation request to each relevant accommodation facility based on the details of the accommodation plan selected by the user. Using the Python Requests library, it sends a POST request to each accommodation facility's API and receives a confirmed reservation response. The final reservation information is saved in a database and a confirmation notification is sent to the user. The reservation confirmation number and check-in procedure details are displayed on the terminal.

[0984] Input: Accommodation plan information selected by the user

[0985] Output: Confirmation response from the accommodation and notification to the user

[0986] (Application example 1)

[0987] 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."

[0988] Optimizing accommodation reservations and food delivery is important for increasing user convenience while also efficiently utilizing provider resources. However, previous systems did not adequately collect real-time reservation status and availability information, or propose optimal accommodation and delivery plans. Furthermore, it was difficult to collect information on unregistered accommodations and restaurants, making it difficult to provide users with a large number of options. Furthermore, systems lacked the functionality to optimize the combination of multiple accommodations and restaurants, making it impossible to meet the diverse needs of users.

[0989] 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.

[0990] In this invention, the server includes: means for collecting accommodation reservation status and availability information; means for calculating accommodation rating scores using an AI algorithm; means for receiving user input conditions and filtering related accommodations to suggest optimal combinations; means for reserving the user's selected accommodation plan; means for collecting order conditions and analyzing restaurant menu information and delivery status; means for presenting optimal restaurants and delivery plans based on the user's input order conditions; and means for notifying delivery personnel of the user's selected order. This allows users to find optimal accommodation plans and delivery plans in real time, while also allowing providers to efficiently utilize their resources and effectively collect information on unregistered accommodations and restaurants. Furthermore, it is possible to optimize combinations of multiple accommodations and restaurants to meet diverse user needs.

[0991] "Accommodation facilities" are facilities that provide accommodation services to guests, including hotels, inns, guest houses, etc.

[0992] "Reservation status" is information that indicates the degree to which available resources such as accommodations and restaurants are occupied during a specific period or date and time.

[0993] "Availability information" is information that indicates whether accommodation facilities, restaurants, etc. are available for use during a specific period or date and time.

[0994] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and solve a specific problem.

[0995] "User" means an individual or corporation that uses the system to make accommodation reservations or use food delivery services.

[0996] "Input conditions" are requests and wishes that the user provides to the system, and include reservation date, budget, type of meal, and the like.

[0997] "Filtering" is the process of selecting information that matches specific conditions from a large amount of information.

[0998] An "accommodation plan" is a plan for using accommodation facilities proposed in response to a user's request, and includes the price, room type, service content, and the like.

[0999] "Order conditions" refer to the wishes and requests that a user provides to the food delivery system, including the type of food, budget, delivery time, etc.

[1000] "Eating establishments" are establishments that serve meals, and include restaurants, cafes, fast food restaurants, etc.

[1001] "Delivery status" is information that indicates the current order processing status and delivery area when a restaurant provides food delivery services.

[1002] A "delivery plan" is a delivery plan from a restaurant proposed based on the user's order conditions, and includes the price, delivery time, delivery route, etc.

[1003] "Web scraping" is a technique for automatically collecting information from web pages on the Internet.

[1004] An "unregistered accommodation" is an accommodation that has not been pre-registered in the system.

[1005] An "unregistered restaurant" is a restaurant that has not been pre-registered in the system.

[1006] The present invention relates to an AI system that optimizes accommodation reservations and food delivery, and is composed of three entities: a server, a terminal, and a user. Specific embodiments are described below.

[1007] System configuration

[1008] 1. Data Collection

[1009] The server collects information on accommodation reservations and availability, as well as restaurant menus and delivery status in real time. Specifically, it collects information through APIs, and for unregistered accommodations and restaurants, it uses web scraping technology (e.g., BeautifulSoup, Selenium).

[1010] 2. Data Analysis

[1011] The server uses AI algorithms (e.g., Scikit-learn, TensorFlow) to analyze the collected data, including calculating the accommodation's rating score and the optimal delivery route.

[1012] 3. User request input

[1013] Users input their hotel reservation and delivery order requirements using devices such as smartphones or PCs, using an interface developed using React Native and Flutter.

[1014] 4. Conditional Filtering

[1015] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price, time, etc.

[1016] 5. Providing the optimal plan

[1017] The server creates accommodation and delivery plans that best suit the user's needs from the filtered results and displays them on the user's device.

[1018] 6. User Choice

[1019] The user selects one of the plans presented and presses the confirm button.

[1020] 7. Booking and Ordering Procedures

[1021] The server sends a reservation request and order to the accommodation facility or restaurant based on the selected plan, and stores the confirmed information in a database.

[1022] Hardware and software used

[1023] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[1024] Data Collection: RESTful API, BeautifulSoup, Selenium

[1025] Data analysis: Scikit-learn, TensorFlow

[1026] User Interface: React Native, Flutter

[1027] Specific examples

[1028] For example, if a group of five people are looking for accommodation in Tokyo and want to order dinner delivery for that night, here's how it works:

[1029] 1. The server collects information on accommodations and delivery restaurants in the Tokyo area.

[1030] 2. Analyze the collected data using AI to optimize accommodation rating scores and delivery plans.

[1031] 3. Users enter their accommodation and delivery requirements into the app.

[1032] 4. The server then filters the most suitable accommodation and delivery plans based on this information.

[1033] 5. The filtered results are presented to the user, who selects and confirms the desired plan.

[1034] 6. The server sends reservation and order requests to accommodations and restaurants based on the selected plan.

[1035] Prompt Sentence Examples

[1036] "Design an AI system that collects information on delivery restaurants in the Tokyo area and provides the optimal delivery plan based on the user's order criteria. The system should have the ability to suggest the optimal restaurant and menu combination based on the user's criteria, such as type of food, budget, and delivery time."

[1037] As described above, the present invention provides a system that optimizes accommodation reservations and food delivery, thereby significantly improving user convenience.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1: Data collection

[1040] The server collects accommodation reservation status and availability information, restaurant menu information, and delivery status in real time via API. It also uses web scraping technology to collect information on unregistered accommodations and restaurants. Specifically, it sends API requests and stores the received data in a database in JSON format. Web scraping uses BeautifulSoup and Selenium to analyze the necessary information from web pages and save it in a database.

[1041] Input: API request, web page URL

[1042] Output: Latest information on accommodations and restaurants (JSON format)

[1043] Step 2: Data analysis

[1044] The server analyzes the collected data using AI algorithms (e.g., Scikit-learn, TensorFlow). Specifically, it calculates an evaluation score based on the accommodation's price, reviews, location, and services provided. It also calculates the optimal delivery route and time.

[1045] Input: Collected accommodation and restaurant data

[1046] Output: Evaluation score, optimal delivery route

[1047] Step 3: User Request Input

[1048] Users use their smartphones or PCs to input the conditions for booking accommodation or ordering delivery (e.g., accommodation dates, budget, type of food, delivery time), and the input data is sent from the device to the server.

[1049] Input: Accommodation and order conditions entered by the user

[1050] Output: Request data sent to the server

[1051] Step 4: Conditional filtering

[1052] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price and time, narrowing down the options from a large number of options to the best plan.

[1053] Input: User request data, accommodation and restaurant data in the database

[1054] Output: Optimal accommodation and delivery plan candidates

[1055] Step 5: Presenting the optimal plan

[1056] The server then creates the best accommodation and delivery plan for the user based on the filtered results and displays it on the user's device. The displayed plan includes detailed information about each facility (e.g., name, address, price, reviews), the best delivery time, and menu details.

[1057] Input: Best accommodation and delivery plan candidates

[1058] Output: Accommodation plan and delivery plan presented

[1059] Step 6: Selecting Users

[1060] The user selects the desired plan from the presented options and presses the confirm button. The selected data is sent from the terminal to the server.

[1061] Input: User's choice

[1062] Output: Confirmed booking and order data

[1063] Step 7: Booking and ordering process

[1064] The server sends reservation requests and orders to accommodations and restaurants based on the user's selection. It receives reservation and order confirmation information via API and stores it in a database. At the same time, it sends a confirmation notification of the reservation and order to the user's device.

[1065] Input: Confirmed reservation and order data

[1066] Output: Reservation and order confirmation information, notification to user

[1067] 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.

[1068] The present invention combines an emotion engine with an AI system for optimizing accommodation use, and can be specifically implemented as follows.

[1069] 1. Data Collection

[1070] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server also uses web scraping technology to collect information. The data collected during the collection process includes detailed information such as hotel name, address, rates, availability, and available services.

[1071] 2. Data Analysis

[1072] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation facility. This rating score is a comprehensive score based on criteria such as price, reviews, location, and services provided, and the analysis results are stored in a database.

[1073] 3. User request input

[1074] Users use devices such as smartphones or PCs to input their desired accommodation conditions, including the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1075] 4. Manipulating the Emotion Engine

[1076] Emotion data is acquired through the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. Based on this evaluation, the server dynamically adjusts the accommodation plan it proposes.

[1077] 5. Conditional Filtering

[1078] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[1079] 6. Providing the optimal plan

[1080] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results, and displays it on the terminal. The terminal also displays detailed information about each hotel (such as the hotel name, address, price, and details of available rooms), as well as specific suggestions for staying in multiple rooms or at multiple accommodations.

[1081] 7. User plan selection

[1082] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1083] 8. Reservation Procedure

[1084] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1085] 9. Confirmation and follow-up

[1086] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[1087] Specific examples

[1088] For example, if a family of five is looking for accommodation in Tokyo:

[1089] 1. Data Collection

[1090] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[1091] 2. Data Analysis

[1092] The server analyzes this data using AI and calculates a rating score for each hotel.

[1093] 3. User request input

[1094] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[1095] 4. Manipulating the Emotion Engine

[1096] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1097] 5. Conditional Filtering

[1098] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[1099] 6. Providing the optimal plan

[1100] For example, the server may present a plan that combines a triple room at Hotel A with a double room at Hotel B, or a plan that splits the stay into two rooms at Hotel C.

[1101] 7. User plan selection

[1102] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[1103] 8. Reservation Procedure

[1104] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1105] 9. Confirmation and follow-up

[1106] The server sends reminders as the date of your stay approaches and collects feedback after your stay to suggest better plans for your next stay.

[1107] In this way, users can more easily find the accommodation that best suits their emotional state, and accommodation providers can also make effective use of available rooms.

[1108] The processing flow will be explained below.

[1109] Step 1: Data collection

[1110] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server uses web scraping technology to extract information from official websites and public databases. The collected data includes hotel names, addresses, rates, availability, and services offered.

[1111] Step 2: Data analysis

[1112] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is calculated based on price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent conditional filtering.

[1113] Step 3: User Request Input

[1114] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1115] Step 4: Manipulating the Emotion Engine

[1116] The user inputs facial expressions and voice through the device, which are analyzed by the emotion engine, which evaluates the user's emotional state in real time and sends this data to the server.

[1117] Step 5: Conditional filtering

[1118] The server filters accommodation information in the database based on the user's input criteria and emotional data. The AI ​​calculates combination plans for multiple rooms and different accommodations, taking into account price, availability, and the user's emotional state. For example, it suggests combinations of triple and double rooms, or rooms available on the same day at different hotels.

[1119] Step 6: Present the optimal plan

[1120] The server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and sends it to the terminal.The terminal displays detailed information about each hotel (hotel name, address, price, details of available rooms) and accommodation plans divided into multiple rooms and accommodation facilities.

[1121] Step 7: User Plan Selection

[1122] The user browses the accommodation plans presented and selects the most suitable one. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1123] Step 8: Booking process

[1124] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from the accommodation facility and stores this reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1125] Step 9: Confirmation and follow-up

[1126] The server sends reminders when the date of stay approaches and assists with check-in and check-out procedures. Feedback is collected after the stay and used to calculate a rating score for the next stay, allowing the server to suggest plans that will increase satisfaction.

[1127] Example 2

[1128] 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."

[1129] Conventional accommodation reservation systems have the problem of low user satisfaction because they simply propose optimal plans mechanically without considering the user's emotional state. Furthermore, conventional systems have difficulty proposing combinations of multiple accommodations, and are limited in the plans they can propose that meet specific conditions.

[1130] 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.

[1131] In this invention, the server includes means for collecting reservation status and availability information of accommodations, means for calculating evaluation scores of accommodations using an AI algorithm, means for receiving user input conditions and filtering related accommodations to propose an optimal combination, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting proposals based on the data, and means for reserving the accommodation plan selected by the user. This makes it possible to dynamically propose optimal accommodation plans according to the user's emotional state.

[1132] "Accommodation facilities" refers to facilities provided for users to stay overnight, including hotels, inns, private lodgings, guesthouses, etc.

[1133] "Reservation status" is information that indicates whether or not an accommodation facility has been booked on a specific date and time, and the availability of reservations.

[1134] "Availability information" refers to information on whether an accommodation facility has available rooms within a specified period, including the number and type of available rooms.

[1135] An "AI algorithm" refers to a computational method that uses artificial intelligence, a process that analyzes data and makes decisions to solve specific problems.

[1136] The "rating score" is a comprehensive rating calculated based on multiple evaluation criteria for the accommodation facility.

[1137] "User" refers to a person who uses this system to make reservations for accommodation.

[1138] "Input conditions" are requirements such as the date of stay, number of guests, budget, desired area, and specific services that a user specifies when reserving accommodation facilities.

[1139] "Filtering" is the process of sorting data based on input conditions and extracting only information that meets specific conditions.

[1140] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing the user's facial expressions and voice input.

[1141] "Dynamic adjustment" refers to changing the system's output and recommendations in real time based on changing conditions or new data.

[1142] An "accommodation plan" is a specific proposal for the use of an accommodation facility, and includes the date of stay, number of guests, price, room type, etc.

[1143] "Reservation" refers to the process of confirming the accommodation plan selected by the user and reserving accommodation at the accommodation facility based on that plan.

[1144] The present invention is an AI system developed to optimize accommodation use, and is characterized by taking into account the emotional state of the user. This system is specifically implemented according to the following procedure.

[1145] First, the server uses an API to collect real-time reservation status and availability information from accommodation booking sites. Furthermore, for unregistered accommodations, information is obtained using web scraping technology. This collection process uses technologies such as the Python requests library, Scrapy, and BeautifulSoup. Specific data collected includes the accommodation's name, address, price, availability, and available services.

[1146] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation using machine learning libraries such as TensorFlow and Scikit-learn. The score is calculated based on factors such as price, reviews, location, and services offered, and the analysis results are stored in an SQL database.

[1147] Users input their desired accommodation requirements using devices such as smartphones or PCs. An HTML form is used to input information, including the date of stay, number of guests, budget, desired area, and specific services. The input information is sent asynchronously to the server via JavaScript (AJAX).

[1148] Next, the user inputs their emotional state through facial expression and voice recognition technology, using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. This evaluation result is sent to the server for further processing.

[1149] The server filters the accommodation information in the database based on the user's input criteria and emotion data. The filtering is performed using the Pandas library, and the server calculates the optimal combination of price and availability. For example, it can suggest a combination of triple and double rooms, or rooms available on the same day at different hotels.

[1150] The server then generates an accommodation plan based on the filtered results to provide feedback to the user, which is displayed using HTML and CSS on the device, showing detailed information about each accommodation (name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[1151] The user checks the accommodation plans presented and selects the one they want. This is done using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[1152] The server then sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1153] Finally, the server sends reminders to the user as their check-in date approaches, using an SMS API such as Twilio, and collects feedback from the user after their stay, which is used to calculate future rating scores.

[1154] Specific examples

[1155] For example, if a family of five is looking for accommodation in Tokyo, the following process will occur:

[1156] 1. Data Collection

[1157] The server collects the latest information on accommodations in the Tokyo area using APIs and web scraping technology.

[1158] 2. Data Analysis

[1159] The server analyzes the collected data using an AI algorithm and calculates the accommodation's rating score.

[1160] 3. User request input

[1161] The user enters desired accommodation conditions, such as "2023-12-20, 5 people, under 50,000 yen, Tokyo, breakfast included," into the HTML form on the terminal and submits it.

[1162] 4. Manipulating the Emotion Engine

[1163] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1164] 5. Conditional Filtering

[1165] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[1166] 6. Providing the optimal plan

[1167] The server will display suggestions on the terminal, such as "a plan combining a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[1168] 7. User plan selection

[1169] The user selects the most suitable plan from the proposed plans and clicks "Reserve" to notify the server.

[1170] 8. Reservation Procedure

[1171] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1172] 9. Confirmation and follow-up

[1173] The server sends reminders as the date of the stay approaches and collects feedback after the stay.

[1174] Example prompt: "I'd like to stay in Tokyo with my family of five. My budget is under 50,000 yen per night, and I'd like a place with easy access to tourist spots. Could you suggest some accommodation plans I could use as a reference?"

[1175] In this way, the present invention allows users to easily find the most suitable accommodation that suits their emotional state, and also allows accommodation providers to efficiently utilize vacant rooms.

[1176] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1177] Step 1:

[1178] Data collection

[1179] The server collects real-time reservation status and availability information through the API of the accommodation booking site. For unregistered accommodations, it also uses web scraping technology to obtain information. Specifically, it uses Python's requests library to obtain data from the API and BeautifulSoup to extract the necessary information from the web page. This allows it to collect information such as hotel name, address, price, availability, and available services.

[1180] Input: API endpoint URL, web page URL

[1181] Output: Detailed information about the accommodation (hotel name, address, price, availability, services)

[1182] Step 2:

[1183] Data analysis

[1184] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation. Machine learning libraries such as TensorFlow and Scikit-learn are used to calculate the rating score. Specific rating criteria include price, reviews, location, and services offered. An overall score is calculated by weighting each rating criterion, and the analysis results are stored in an SQL database.

[1185] Input: Collected accommodation details

[1186] Output: Accommodation information with rating scores

[1187] Step 3:

[1188] User request input

[1189] Users use their devices (smartphones or PCs) to enter their desired accommodation requirements. Specifically, they use an HTML form to enter the dates of stay, number of guests, budget, desired area, specific services, etc. The entered data is sent asynchronously to the server using JavaScript (AJAX).

[1190] Input: User's desired accommodation conditions (date of stay, number of people, budget, area, services)

[1191] Output: User's accommodation preferences sent to the server

[1192] Step 4:

[1193] Manipulating the Emotion Engine

[1194] Users provide emotional data through facial expressions and voice. This data is acquired using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The evaluation results are sent to the server for further processing.

[1195] Input: User's facial expression data, voice data

[1196] Output: Parsed emotional state data

[1197] Step 5:

[1198] Conditional Filtering

[1199] The server filters the accommodation information in the database based on the user's input criteria and emotion data. It uses the Pandas library to narrow down the data that matches the criteria and calculates the optimal price and available room combination. For example, it generates plans that suggest combinations of triple and double rooms, or rooms available on the same day at different hotels.

[1200] Input: User's accommodation preferences, analyzed emotional state data, accommodation information with rating scores

[1201] Output: Filtered adaptive accommodation plans

[1202] Step 6:

[1203] Providing the optimal plan

[1204] The server generates an accommodation plan based on the filtered results to provide feedback to the user. This information is displayed on the device using HTML and CSS, showing detailed information about each accommodation (accommodation name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[1205] Input: Filtered accommodation plans

[1206] Output: The best accommodation plan displayed on the device

[1207] Step 7:

[1208] User plan selection

[1209] The user checks the accommodation plans presented and selects the plan they want. This selection is made using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[1210] Input: The optimal accommodation plan displayed on the device

[1211] Output: User selections sent to the server

[1212] Step 8:

[1213] Reservation procedure

[1214] The server sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1215] Input: User selection information sent to the server

[1216] Output: Reservation request to the accommodation, confirmation of reservation to the user

[1217] Step 9:

[1218] Acknowledgments and follow-up

[1219] The server sends reminders to users as their stay approaches using an SMS API such as Twilio, and collects feedback from users after their stay, which is used to calculate future rating scores.

[1220] Input: Reservation information stored on the server

[1221] Output: Reminder notification to user, feedback from user

[1222] (Application example 2)

[1223] 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."

[1224] Conventional accommodation reservation systems were unable to present optimal accommodation plans that took into account the user's emotional state, making it difficult to meet the user's latent needs. Furthermore, because there was no dynamic feedback based on emotions, the proposed plans did not necessarily increase user satisfaction. This led to user abandonment and dissatisfaction, and accommodation operators lost opportunities to effectively utilize available rooms.

[1225] The specification process 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 collecting reservation status and availability information of accommodation facilities, means for calculating accommodation facility evaluation scores using an AI algorithm, means for receiving user input conditions and filtering related accommodation facilities to propose optimal combinations, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting the proposal content based on the emotional state, and means for reserving the accommodation plan selected by the user. This enables users to find more satisfying accommodation plans and increases opportunities for accommodation facility operators to effectively utilize available rooms.

[1226] "Accommodation reservation status" is information indicating whether or not a specific accommodation facility currently has reservations and the number of reservations.

[1227] "Vacancy information" is information indicating rooms currently available for reservation at a specific accommodation facility and the availability of each room.

[1228] "Means" refers to methods or technical devices used to achieve a particular purpose.

[1229] The "accommodation rating score" is a comprehensive rating calculated using multiple evaluation criteria such as the accommodation's price, reviews, location, and services offered.

[1230] An "AI algorithm" refers to a computational procedure or model used to solve a specific problem using artificial intelligence.

[1231] "User input conditions" refers to information that the user inputs to the system regarding the accommodation conditions desired by the user, such as the date of stay, number of guests, budget, desired area, etc.

[1232] "Related accommodations" refers to accommodations that may be suitable based on the user's input criteria.

[1233] "Filtering" refers to the process of extracting information that meets specific conditions from a large amount of information.

[1234] The "optimal combination" refers to the result of selecting from multiple options the one that best suits the user's conditions and emotional state.

[1235] "Obtaining emotional data from a user's facial expressions and voice" refers to the process of analyzing a user's facial expressions and tone of voice to identify the user's current emotional state.

[1236] "Dynamic adjustment of suggestion content based on emotional state" refers to using acquired emotional data to make optimal suggestions in real time according to the user's current mood and emotions.

[1237] An "accommodation plan" refers to a plan including specific facility names, rooms, and available services that the system proposes for a user's accommodation.

[1238] "Reservation" refers to the process of officially reserving accommodation based on the accommodation plan selected by the user.

[1239] The present invention combines an AI system with an emotion engine to optimize accommodation use. This system can be specifically implemented as follows.

[1240] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs, and also uses web scraping technology to collect information from unregistered accommodations. The collected data includes detailed information such as the accommodation's name, address, price, availability, and available services.

[1241] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is a comprehensive score based on factors such as price, reviews, location, and services provided. The analysis results are then stored in a database.

[1242] Users input their desired accommodation requirements using a device such as a smartphone or PC. These requirements include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1243] The emotion engine acquires emotional data from the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The emotion evaluation results are used to dynamically adjust the accommodation plans proposed by the server.

[1244] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[1245] Next, the server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and displays it on the terminal, along with detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[1246] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1247] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1248] Finally, the server can optionally send reminders as the date of the stay approaches, assist with check-in and check-out procedures, and even collect feedback after the stay, which will be used to calculate future rating scores.

[1249] This system makes it easier for users to find the accommodation that best suits their emotional state, and also enables accommodation providers to make effective use of vacant rooms.

[1250] As a concrete example, let's consider the case where a family of five is looking for a place to stay in Tokyo. The server collects a large amount of up-to-date information on accommodations in the Tokyo area, analyzes this data using AI, and calculates a rating score for each hotel. The user inputs the conditions for accommodation in Tokyo for five people (e.g., budget, dates of stay) into the device and submits it. The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1251] The server optimizes the combination of multiple hotels and room types based on the user's requirements and emotional data, and extracts available plans. For example, a plan combining a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C, are possible. The user selects the plan they like best from the presented plans and clicks "Book" to notify the server. The server then sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information. The server sends reminders as the check-in date approaches and collects feedback after the stay to suggest better plans for the next time.

[1252] Examples of prompts for a generative AI model might include:

[1253] Prompt: "A user is trying to order a meal. Determine the user's emotional state from their facial expressions and suggest restaurants and meal options that best fit that emotion. Also consider the user's past dining history, reviews, and preferred flavors and types."

[1254] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1255] Step 1:

[1256] The server collects real-time reservation status and availability information from accommodation booking sites using APIs. This process involves obtaining real-time reservation data from designated accommodations as input and storing it in an internal database. For unregistered accommodations, the server uses web scraping technology to collect information and stores it in the internal database as well.

[1257] Step 2:

[1258] The server uses the collected data to calculate an accommodation rating score using an AI algorithm. Specifically, it uses an AI algorithm (e.g., a machine learning model) to calculate the rating score based on information such as price, reviews, location, and services provided. This score is saved in a database and can be used for subsequent filtering processes.

[1259] Step 3:

[1260] Users input their desired accommodation conditions using a device such as a smartphone or PC, and send them to the server from the device. These desired accommodation conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input data is received by the server and used for subsequent processing.

[1261] Step 4:

[1262] The device acquires emotional data from the user's facial expressions and voice input. Using an emotion engine, the device analyzes the acquired facial images and voice data to evaluate the user's emotional state in real time. The evaluation results are sent from the device to a server and used to dynamically adjust the content of suggestions.

[1263] Step 5:

[1264] The server filters accommodation information in the database based on the user's input criteria and emotional data. Specifically, it narrows down the accommodations that fit the input criteria and dynamically optimizes the suggestions based on the emotional data. Using an AI algorithm, it optimally combines information such as price and availability to prepare a proposal for a combination of multiple rooms or different accommodations.

[1265] Step 6:

[1266] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results and displays it on the device. This display includes detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[1267] Step 7:

[1268] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button on the terminal, and the information is sent from the terminal to the server.

[1269] Step 8:

[1270] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives reservation confirmation responses from each accommodation facility and stores the final reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1271] Step 9:

[1272] The server sends reminders as needed when the check-in date approaches, assists with check-in and check-out procedures, and collects feedback after the stay to be used in calculating future rating scores, improving the user experience and helping service providers improve customer satisfaction.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] [Fourth embodiment]

[1277] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1278] 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.

[1279] 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).

[1280] 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.

[1281] 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.

[1282] 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).

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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."

[1290] The present invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[1291] 1. Data Collection

[1292] The server collects real-time reservation status and availability information from accommodation booking sites via API. It also automatically collects data from unregistered accommodations using web scraping technology and stores it in a database for analysis. In the process, it obtains detailed information such as the accommodation's name, address, price, availability, and available services.

[1293] 2. Data Analysis

[1294] The server uses an AI algorithm to calculate a rating score for each accommodation based on the collected data. This AI algorithm uses criteria such as price, reviews, location, and services provided. The analysis results are stored in a database and can be used according to user requests.

[1295] 3. User request input

[1296] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. The entered conditions are sent to the server via the device.

[1297] 4. Conditional Filtering

[1298] The server filters the accommodation information in its database based on the user's criteria. AI, in particular, calculates the optimal combination of rates and availability, and can propose plans combining multiple rooms or different accommodations. For example, it can suggest a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[1299] 5. Providing the optimal plan

[1300] The server creates an accommodation plan that best suits the user's requirements from the filtering results and displays it on the terminal. The terminal also displays detailed information about each hotel (e.g., hotel name, address, price, details of available rooms, etc.) and specific suggestions for staying in multiple rooms or accommodations.

[1301] 6. User plan selection

[1302] The user checks the displayed accommodation plans and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent to the server.

[1303] 7. Reservation Procedure

[1304] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (e.g., reservation confirmation number, check-in procedure, etc.) on the terminal.

[1305] Specific examples

[1306] For example, if a group of 5 people are looking for accommodation in Tokyo:

[1307] 1. Data Collection

[1308] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[1309] 2. Data Analysis

[1310] The server analyzes this data using AI and calculates a rating score for each hotel.

[1311] 3. User request input

[1312] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[1313] 4. Conditional Filtering

[1314] The server optimizes the combination of multiple hotels and room types based on the input conditions and extracts available plans.

[1315] 5. Providing the optimal plan

[1316] The server will present the terminal with a plan that combines, for example, a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C.

[1317] 6. User plan selection

[1318] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[1319] 7. Reservation Procedure

[1320] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1321] In this way, users can easily find suitable accommodations, and accommodation providers can also make effective use of vacant rooms.

[1322] The processing flow will be explained below.

[1323] Step 1: Data collection

[1324] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs. For unregistered accommodations, the server then uses web scraping technology to extract the necessary information from official websites and public databases. The collected data includes details such as hotel names, addresses, rates, availability, and available services.

[1325] Step 2: Data analysis

[1326] The server inputs the data collected in step 1 into an AI algorithm to calculate a rating score for each accommodation. This rating score takes into consideration a comprehensive range of evaluation criteria, including price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent filtering.

[1327] Step 3: Entering User Requests

[1328] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1329] Step 4: Conditional filtering

[1330] The server filters the accommodation information in the database based on the conditions received from the user in step 3. During filtering, the AI ​​calculates the optimal combination of rates and availability, and prepares to propose combination plans for multiple rooms or different accommodations. For example, it can propose a combination of a triple room and a double room, or rooms available on the same day at different hotels.

[1331] Step 5: Presenting the optimal plan

[1332] The server creates an accommodation plan that best suits the user's requirements based on the filtering results of step 4. This plan includes detailed information about each hotel (hotel name, address, price, available room details, etc.) and specific suggestions for staying in multiple rooms or accommodations. The created plan is sent to the terminal and displayed to the user.

[1333] Step 6: User Plan Selection

[1334] The user checks the optimal plans displayed on the terminal and selects the desired plan. The selected plan is confirmed by clicking the "Reserve" button, and the information is sent from the terminal to the server.

[1335] Step 7: Booking process

[1336] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from each accommodation facility and stores this reservation information in a database. The server also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1337] Step 8: Confirmation and follow-up

[1338] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[1339] Example 1

[1340] 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."

[1341] Conventional accommodation reservation systems have limitations in obtaining reservation status and availability information, and it is difficult to calculate accurate evaluation scores. As a result, they are not capable of proposing optimal accommodation plans for users, and it is difficult for accommodation operators to effectively utilize available rooms. Furthermore, there is no flexible way to combine multiple accommodations and room types, so they are unable to meet the diverse needs of users.

[1342] 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.

[1343] In this invention, the server includes means for collecting accommodation reservation status and availability information, means for collecting detailed information about accommodations using web scraping technology, means for calculating accommodation rating scores using an AI algorithm, means for receiving user input conditions and filtering related accommodations from a database, means for proposing optimal combinations of multiple accommodations and room types, means for reserving the accommodation plan selected by the user, and means for notifying the user of confirmed reservation information. This allows users to easily find the optimal accommodation plan and enables accommodation providers to efficiently utilize available rooms.

[1344] "Accommodation facility" means a facility that provides a building or place for guests to stay.

[1345] "Reservation status" is information that indicates the current reservation status at a specific accommodation facility.

[1346] "Availability information" is information that indicates the status of rooms or spaces currently available at a particular accommodation facility.

[1347] "Web scraping technology" is a technology that automatically collects information that is publicly available on the Internet.

[1348] The "rating score" is a number that indicates the overall rating of an accommodation facility calculated based on various evaluation criteria.

[1349] An "AI algorithm" is a set of steps or calculation methods used to solve a specific problem using artificial intelligence technology.

[1350] "User" refers to an individual or group who uses the System to search for and reserve accommodation plans.

[1351] A "database" is a collection of data that stores collected information in an organized manner and allows for efficient search and analysis.

[1352] "Filtering" is the process of sorting data based on specific conditions and extracting the necessary information.

[1353] "Accommodation plan" refers to a specific proposal that combines accommodation facilities and their terms of use.

[1354] A "reservation request" is information sent by a user to convey the intention to make a reservation to an accommodation facility.

[1355] "Reservation confirmation information" is information for notifying the user that the reservation has been confirmed by the accommodation facility.

[1356] This invention relates to an AI system for optimizing the use of accommodation facilities, and can be specifically implemented as follows.

[1357] The server uses the APIs of multiple accommodation booking sites to collect information on accommodation reservation status and availability. For example, the server uses Python's Requests library to send GET requests to the API endpoints of each booking site and retrieve data in JSON format. For unregistered accommodations, the server also uses Python's BeautifulSoup and Requests library to automatically collect data using web scraping technology. During this process, detailed information such as the accommodation's name, address, price, availability, and available services is obtained and stored in a database.

[1358] The server uses an AI algorithm to calculate an accommodation rating score based on the collected data. Specifically, it uses Python's Scikit-learn to calculate the rating score based on evaluation criteria such as price, reviews, location, and services provided. This allows accommodations with high ratings to be suggested to users with higher priority.

[1359] Users input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.) on a device such as a smartphone or PC. These conditions are collected by a front-end user interface using React and sent to the server via an HTTP POST request.

[1360] The server filters the accommodation information in the database based on the conditions received from the user. It uses SQL queries to filter and extract the accommodations that best fit the user's conditions. The AI ​​model then calculates the optimal combination of rates and availability, and creates plans combining multiple accommodations and room types. For example, Python's Pandas library can be used to efficiently find accommodations that meet the conditions.

[1361] The server then generates an accommodation plan that best suits the user's requirements based on the filtering results and displays it on the device. Using React and MUI, the device dynamically displays hotel names, addresses, rates, and detailed information about available rooms. Plan examples include "combining a triple room at Hotel A with a double room at Hotel B" and "staying in two separate rooms at Hotel C."

[1362] The user selects the desired plan from the displayed accommodation plans. The selection is made by clicking the "Book" button in React, and the selected plan information is sent to the server via an Ajax request.

[1363] Finally, the server sends a reservation request to each relevant accommodation based on the details of the accommodation plan selected by the user. Specifically, it uses the Python Requests library to send a POST request to each accommodation's API and receives a confirmation response. This information is stored in a database and the user is notified that the reservation has been confirmed. The front-end displays the reservation confirmation number and check-in details.

[1364] For example, if a group of 5 people are looking for accommodation in Tokyo:

[1365] 1. The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[1366] 2. The server analyzes this data using Scikit-learn and calculates a rating score for each hotel.

[1367] 3. The user enters conditions such as "budget is within 10,000 yen per person per night, and the date of stay is next weekend" into the terminal and submits the request.

[1368] 4. The server uses SQL queries and the Pandas library to optimize the hotel combinations and room types based on the input criteria and extract available deals.

[1369] 5. The server presents the terminal with options such as "a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[1370] 6. The user selects the plan they like best from the plans presented and clicks "Book."

[1371] 7. The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1372] This system allows users to easily find the best accommodation plan and enables accommodation providers to make effective use of available rooms.

[1373] Example prompt sentence:

[1374] "Please suggest the best accommodation plan for a group of five people looking to stay in Tokyo. The budget is 10,000 yen per person per night, and the stay is next weekend."

[1375] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1376] Step 1:

[1377] The server collects accommodation reservation status and availability information. To do this, it uses the accommodation booking site's API to send a GET request and retrieve data in JSON format. It also uses Python's BeautifulSoup and Requests libraries to web scrape information on unregistered accommodations. The collected data includes the accommodation's name, address, price, availability, and available services, and is stored in a database.

[1378] Input: API request parameters and website URL

[1379] Output: JSON data containing details of accommodations and scraping results

[1380] Step 2:

[1381] The server analyzes the collected data using an AI algorithm and calculates an evaluation score for each accommodation. Specifically, it uses Python's Scikit-learn to calculate the evaluation score based on evaluation criteria such as price, reviews, location, and services provided. The lower the price and the higher the reviews, the higher the score. The analysis results are stored in a database.

[1382] Input: Accommodation details stored in the database

[1383] Output: Rating score for each accommodation

[1384] Step 3:

[1385] The user uses a terminal to input their desired accommodation conditions (e.g., dates of stay, number of guests, budget, desired area, specific services, etc.). The user enters the information into a front-end form using React, which is then sent to the server via an HTTP POST request.

[1386] Input: desired accommodation conditions (date of stay, number of guests, budget, desired area, specific services, etc.)

[1387] Output: User's accommodation preferences sent to the server

[1388] Step 4:

[1389] The server filters the accommodation information in the database based on the user's desired accommodations, using an SQL query to extract accommodations that match the user's criteria, and then uses Python's Pandas library to manipulate the results, including price and availability, to calculate the optimal accommodation combination.

[1390] Input: User's desired accommodation conditions, accommodation information in the database

[1391] Output: The best accommodation candidates that meet the user's criteria

[1392] Step 5:

[1393] The server creates the optimal accommodation plan based on the filtering results and sends it to the front end. The accommodation plan is dynamically displayed on the terminal using React and MUI. Detailed information includes the hotel name, address, price, and available room details, and multiple combinations of accommodations and room types are presented.

[1394] Input: Best accommodation options

[1395] Output: The best accommodation plan displayed on the device

[1396] Step 6:

[1397] The user uses the React interface to select the desired accommodation plan from the displayed options. When the user clicks the "Book" button, the selected plan information is sent to the server via an Ajax request.

[1398] Input: The accommodation plan selected by the user

[1399] Output: Selected plan information sent to the server

[1400] Step 7:

[1401] The server sends a reservation request to each relevant accommodation facility based on the details of the accommodation plan selected by the user. Using the Python Requests library, it sends a POST request to each accommodation facility's API and receives a confirmed reservation response. The final reservation information is saved in a database and a confirmation notification is sent to the user. The reservation confirmation number and check-in procedure details are displayed on the terminal.

[1402] Input: Accommodation plan information selected by the user

[1403] Output: Confirmation response from the accommodation and notification to the user

[1404] (Application example 1)

[1405] 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."

[1406] Optimizing accommodation reservations and food delivery is important for increasing user convenience while also efficiently utilizing provider resources. However, previous systems did not adequately collect real-time reservation status and availability information, or propose optimal accommodation and delivery plans. Furthermore, it was difficult to collect information on unregistered accommodations and restaurants, making it difficult to provide users with a large number of options. Furthermore, systems lacked the functionality to optimize the combination of multiple accommodations and restaurants, making it impossible to meet the diverse needs of users.

[1407] 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.

[1408] In this invention, the server includes: means for collecting accommodation reservation status and availability information; means for calculating accommodation rating scores using an AI algorithm; means for receiving user input conditions and filtering related accommodations to suggest optimal combinations; means for reserving the user's selected accommodation plan; means for collecting order conditions and analyzing restaurant menu information and delivery status; means for presenting optimal restaurants and delivery plans based on the user's input order conditions; and means for notifying delivery personnel of the user's selected order. This allows users to find optimal accommodation plans and delivery plans in real time, while also allowing providers to efficiently utilize their resources and effectively collect information on unregistered accommodations and restaurants. Furthermore, it is possible to optimize combinations of multiple accommodations and restaurants to meet diverse user needs.

[1409] "Accommodation facilities" are facilities that provide accommodation services to guests, including hotels, inns, guest houses, etc.

[1410] "Reservation status" is information that indicates the degree to which available resources such as accommodations and restaurants are occupied during a specific period or date and time.

[1411] "Availability information" is information that indicates whether accommodation facilities, restaurants, etc. are available for use during a specific period or date and time.

[1412] An "AI algorithm" is a computational procedure that uses artificial intelligence technology to analyze data and solve a specific problem.

[1413] "User" means an individual or corporation that uses the system to make accommodation reservations or use food delivery services.

[1414] "Input conditions" are requests and wishes that the user provides to the system, and include reservation date, budget, type of meal, and the like.

[1415] "Filtering" is the process of selecting information that matches specific conditions from a large amount of information.

[1416] An "accommodation plan" is a plan for using accommodation facilities proposed in response to a user's request, and includes the price, room type, service content, and the like.

[1417] "Order conditions" refer to the wishes and requests that a user provides to the food delivery system, including the type of food, budget, delivery time, etc.

[1418] "Eating establishments" are establishments that serve meals, and include restaurants, cafes, fast food restaurants, etc.

[1419] "Delivery status" is information that indicates the current order processing status and delivery area when a restaurant provides food delivery services.

[1420] A "delivery plan" is a delivery plan from a restaurant proposed based on the user's order conditions, and includes the price, delivery time, delivery route, etc.

[1421] "Web scraping" is a technique for automatically collecting information from web pages on the Internet.

[1422] An "unregistered accommodation" is an accommodation that has not been pre-registered in the system.

[1423] An "unregistered restaurant" is a restaurant that has not been pre-registered in the system.

[1424] The present invention relates to an AI system that optimizes accommodation reservations and food delivery, and is composed of three entities: a server, a terminal, and a user. Specific embodiments are described below.

[1425] System configuration

[1426] 1. Data Collection

[1427] The server collects information on accommodation reservations and availability, as well as restaurant menus and delivery status in real time. Specifically, it collects information through APIs, and for unregistered accommodations and restaurants, it uses web scraping technology (e.g., BeautifulSoup, Selenium).

[1428] 2. Data Analysis

[1429] The server uses AI algorithms (e.g., Scikit-learn, TensorFlow) to analyze the collected data, including calculating the accommodation's rating score and the optimal delivery route.

[1430] 3. User request input

[1431] Users input their hotel reservation and delivery order requirements using devices such as smartphones or PCs, using an interface developed using React Native and Flutter.

[1432] 4. Conditional Filtering

[1433] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price, time, etc.

[1434] 5. Providing the optimal plan

[1435] The server creates accommodation and delivery plans that best suit the user's needs from the filtered results and displays them on the user's device.

[1436] 6. User Choice

[1437] The user selects one of the plans presented and presses the confirm button.

[1438] 7. Booking and Ordering Procedures

[1439] The server sends a reservation request and order to the accommodation facility or restaurant based on the selected plan, and stores the confirmed information in a database.

[1440] Hardware and software used

[1441] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[1442] Data Collection: RESTful API, BeautifulSoup, Selenium

[1443] Data analysis: Scikit-learn, TensorFlow

[1444] User Interface: React Native, Flutter

[1445] Specific examples

[1446] For example, if a group of five people are looking for accommodation in Tokyo and want to order dinner delivery for that night, here's how it works:

[1447] 1. The server collects information on accommodations and delivery restaurants in the Tokyo area.

[1448] 2. Analyze the collected data using AI to optimize accommodation rating scores and delivery plans.

[1449] 3. Users enter their accommodation and delivery requirements into the app.

[1450] 4. The server then filters the most suitable accommodation and delivery plans based on this information.

[1451] 5. The filtered results are presented to the user, who selects and confirms the desired plan.

[1452] 6. The server sends reservation and order requests to accommodations and restaurants based on the selected plan.

[1453] Prompt Sentence Examples

[1454] "Design an AI system that collects information on delivery restaurants in the Tokyo area and provides the optimal delivery plan based on the user's order criteria. The system should have the ability to suggest the optimal restaurant and menu combination based on the user's criteria, such as type of food, budget, and delivery time."

[1455] As described above, the present invention provides a system that optimizes accommodation reservations and food delivery, thereby significantly improving user convenience.

[1456] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1457] Step 1: Data collection

[1458] The server collects accommodation reservation status and availability information, restaurant menu information, and delivery status in real time via API. It also uses web scraping technology to collect information on unregistered accommodations and restaurants. Specifically, it sends API requests and stores the received data in a database in JSON format. Web scraping uses BeautifulSoup and Selenium to analyze the necessary information from web pages and save it in a database.

[1459] Input: API request, web page URL

[1460] Output: Latest information on accommodations and restaurants (JSON format)

[1461] Step 2: Data analysis

[1462] The server analyzes the collected data using AI algorithms (e.g., Scikit-learn, TensorFlow). Specifically, it calculates an evaluation score based on the accommodation's price, reviews, location, and services provided. It also calculates the optimal delivery route and time.

[1463] Input: Collected accommodation and restaurant data

[1464] Output: Evaluation score, optimal delivery route

[1465] Step 3: User Request Input

[1466] Users use their smartphones or PCs to input the conditions for booking accommodation or ordering delivery (e.g., accommodation dates, budget, type of food, delivery time), and the input data is sent from the device to the server.

[1467] Input: Accommodation and order conditions entered by the user

[1468] Output: Request data sent to the server

[1469] Step 4: Conditional filtering

[1470] The server filters accommodation and restaurant information in its database based on the user's criteria, and an AI algorithm calculates the optimal combination of price and time, narrowing down the options from a large number of options to the best plan.

[1471] Input: User request data, accommodation and restaurant data in the database

[1472] Output: Optimal accommodation and delivery plan candidates

[1473] Step 5: Presenting the optimal plan

[1474] The server then creates the best accommodation and delivery plan for the user based on the filtered results and displays it on the user's device. The displayed plan includes detailed information about each facility (e.g., name, address, price, reviews), the best delivery time, and menu details.

[1475] Input: Best accommodation and delivery plan candidates

[1476] Output: Accommodation plan and delivery plan presented

[1477] Step 6: Selecting Users

[1478] The user selects the desired plan from the presented options and presses the confirm button. The selected data is sent from the terminal to the server.

[1479] Input: User's choice

[1480] Output: Confirmed booking and order data

[1481] Step 7: Booking and ordering process

[1482] The server sends reservation requests and orders to accommodations and restaurants based on the user's selection. It receives reservation and order confirmation information via API and stores it in a database. At the same time, it sends a confirmation notification of the reservation and order to the user's device.

[1483] Input: Confirmed reservation and order data

[1484] Output: Reservation and order confirmation information, notification to user

[1485] 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.

[1486] The present invention combines an emotion engine with an AI system for optimizing accommodation use, and can be specifically implemented as follows.

[1487] 1. Data Collection

[1488] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server also uses web scraping technology to collect information. The data collected during the collection process includes detailed information such as hotel name, address, rates, availability, and available services.

[1489] 2. Data Analysis

[1490] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation facility. This rating score is a comprehensive score based on criteria such as price, reviews, location, and services provided, and the analysis results are stored in a database.

[1491] 3. User request input

[1492] Users use devices such as smartphones or PCs to input their desired accommodation conditions, including the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1493] 4. Manipulating the Emotion Engine

[1494] Emotion data is acquired through the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. Based on this evaluation, the server dynamically adjusts the accommodation plan it proposes.

[1495] 5. Conditional Filtering

[1496] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[1497] 6. Providing the optimal plan

[1498] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results, and displays it on the terminal. The terminal also displays detailed information about each hotel (such as the hotel name, address, price, and details of available rooms), as well as specific suggestions for staying in multiple rooms or at multiple accommodations.

[1499] 7. User plan selection

[1500] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1501] 8. Reservation Procedure

[1502] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1503] 9. Confirmation and follow-up

[1504] The server will send reminders as needed, assist with check-in and check-out procedures, and also provide the ability to collect feedback after a stay, which will be used to calculate future rating scores.

[1505] Specific examples

[1506] For example, if a family of five is looking for accommodation in Tokyo:

[1507] 1. Data Collection

[1508] The server collects a large amount of up-to-date information on accommodations in the Tokyo area.

[1509] 2. Data Analysis

[1510] The server analyzes this data using AI and calculates a rating score for each hotel.

[1511] 3. User request input

[1512] The user inputs the conditions (e.g., budget, dates of stay) for accommodation for five people in Tokyo into the terminal and transmits it.

[1513] 4. Manipulating the Emotion Engine

[1514] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1515] 5. Conditional Filtering

[1516] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[1517] 6. Providing the optimal plan

[1518] For example, the server may present a plan that combines a triple room at Hotel A with a double room at Hotel B, or a plan that splits the stay into two rooms at Hotel C.

[1519] 7. User plan selection

[1520] The user selects the plan they like best from the plans presented and clicks "Reserve" to notify the server.

[1521] 8. Reservation Procedure

[1522] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1523] 9. Confirmation and follow-up

[1524] The server sends reminders as the date of your stay approaches and collects feedback after your stay to suggest better plans for your next stay.

[1525] In this way, users can more easily find the accommodation that best suits their emotional state, and accommodation providers can also make effective use of available rooms.

[1526] The processing flow will be explained below.

[1527] Step 1: Data collection

[1528] The server uses APIs to collect real-time reservation status and availability information from accommodation booking sites. For unregistered accommodations, the server uses web scraping technology to extract information from official websites and public databases. The collected data includes hotel names, addresses, rates, availability, and services offered.

[1529] Step 2: Data analysis

[1530] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is calculated based on price, reviews, location, and services provided. The analysis results are stored in a database and used for subsequent conditional filtering.

[1531] Step 3: User Request Input

[1532] Users input their desired accommodation conditions using their own devices such as smartphones or PCs. These conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1533] Step 4: Manipulating the Emotion Engine

[1534] The user inputs facial expressions and voice through the device, which are analyzed by the emotion engine, which evaluates the user's emotional state in real time and sends this data to the server.

[1535] Step 5: Conditional filtering

[1536] The server filters accommodation information in the database based on the user's input criteria and emotional data. The AI ​​calculates combination plans for multiple rooms and different accommodations, taking into account price, availability, and the user's emotional state. For example, it suggests combinations of triple and double rooms, or rooms available on the same day at different hotels.

[1537] Step 6: Present the optimal plan

[1538] The server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and sends it to the terminal.The terminal displays detailed information about each hotel (hotel name, address, price, details of available rooms) and accommodation plans divided into multiple rooms and accommodation facilities.

[1539] Step 7: User Plan Selection

[1540] The user browses the accommodation plans presented and selects the most suitable one. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1541] Step 8: Booking process

[1542] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. It receives a reservation confirmation response from the accommodation facility and stores this reservation information in a database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1543] Step 9: Confirmation and follow-up

[1544] The server sends reminders when the date of stay approaches and assists with check-in and check-out procedures. Feedback is collected after the stay and used to calculate a rating score for the next stay, allowing the server to suggest plans that will increase satisfaction.

[1545] Example 2

[1546] 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."

[1547] Conventional accommodation reservation systems have the problem of low user satisfaction because they simply propose optimal plans mechanically without considering the user's emotional state. Furthermore, conventional systems have difficulty proposing combinations of multiple accommodations, and are limited in the plans they can propose that meet specific conditions.

[1548] 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.

[1549] In this invention, the server includes means for collecting reservation status and availability information of accommodations, means for calculating evaluation scores of accommodations using an AI algorithm, means for receiving user input conditions and filtering related accommodations to propose an optimal combination, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting proposals based on the data, and means for reserving the accommodation plan selected by the user. This makes it possible to dynamically propose optimal accommodation plans according to the user's emotional state.

[1550] "Accommodation facilities" refers to facilities provided for users to stay overnight, including hotels, inns, private lodgings, guesthouses, etc.

[1551] "Reservation status" is information that indicates whether or not an accommodation facility has been booked on a specific date and time, and the availability of reservations.

[1552] "Availability information" refers to information on whether an accommodation facility has available rooms within a specified period, including the number and type of available rooms.

[1553] An "AI algorithm" refers to a computational method that uses artificial intelligence, a process that analyzes data and makes decisions to solve specific problems.

[1554] The "rating score" is a comprehensive rating calculated based on multiple evaluation criteria for the accommodation facility.

[1555] "User" refers to a person who uses this system to make reservations for accommodation.

[1556] "Input conditions" are requirements such as the date of stay, number of guests, budget, desired area, and specific services that a user specifies when reserving accommodation facilities.

[1557] "Filtering" is the process of sorting data based on input conditions and extracting only information that meets specific conditions.

[1558] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing the user's facial expressions and voice input.

[1559] "Dynamic adjustment" refers to changing the system's output and recommendations in real time based on changing conditions or new data.

[1560] An "accommodation plan" is a specific proposal for the use of an accommodation facility, and includes the date of stay, number of guests, price, room type, etc.

[1561] "Reservation" refers to the process of confirming the accommodation plan selected by the user and reserving accommodation at the accommodation facility based on that plan.

[1562] The present invention is an AI system developed to optimize accommodation use, and is characterized by taking into account the emotional state of the user. This system is specifically implemented according to the following procedure.

[1563] First, the server uses an API to collect real-time reservation status and availability information from accommodation booking sites. Furthermore, for unregistered accommodations, information is obtained using web scraping technology. This collection process uses technologies such as the Python requests library, Scrapy, and BeautifulSoup. Specific data collected includes the accommodation's name, address, price, availability, and available services.

[1564] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation using machine learning libraries such as TensorFlow and Scikit-learn. The score is calculated based on factors such as price, reviews, location, and services offered, and the analysis results are stored in an SQL database.

[1565] Users input their desired accommodation requirements using devices such as smartphones or PCs. An HTML form is used to input information, including the date of stay, number of guests, budget, desired area, and specific services. The input information is sent asynchronously to the server via JavaScript (AJAX).

[1566] Next, the user inputs their emotional state through facial expression and voice recognition technology, using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. This evaluation result is sent to the server for further processing.

[1567] The server filters the accommodation information in the database based on the user's input criteria and emotion data. The filtering is performed using the Pandas library, and the server calculates the optimal combination of price and availability. For example, it can suggest a combination of triple and double rooms, or rooms available on the same day at different hotels.

[1568] The server then generates an accommodation plan based on the filtered results to provide feedback to the user, which is displayed using HTML and CSS on the device, showing detailed information about each accommodation (name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[1569] The user checks the accommodation plans presented and selects the one they want. This is done using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[1570] The server then sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1571] Finally, the server sends reminders to the user as their check-in date approaches, using an SMS API such as Twilio, and collects feedback from the user after their stay, which is used to calculate future rating scores.

[1572] Specific examples

[1573] For example, if a family of five is looking for accommodation in Tokyo, the following process will occur:

[1574] 1. Data Collection

[1575] The server collects the latest information on accommodations in the Tokyo area using APIs and web scraping technology.

[1576] 2. Data Analysis

[1577] The server analyzes the collected data using an AI algorithm and calculates the accommodation's rating score.

[1578] 3. User request input

[1579] The user enters desired accommodation conditions, such as "2023-12-20, 5 people, under 50,000 yen, Tokyo, breakfast included," into the HTML form on the terminal and submits it.

[1580] 4. Manipulating the Emotion Engine

[1581] The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1582] 5. Conditional Filtering

[1583] The server optimizes the combination of multiple hotels and room types based on the user's conditions and emotional data, and extracts available plans.

[1584] 6. Providing the optimal plan

[1585] The server will display suggestions on the terminal, such as "a plan combining a triple room at Hotel A and a double room at Hotel B" or "a plan to stay in two separate rooms at Hotel C."

[1586] 7. User plan selection

[1587] The user selects the most suitable plan from the proposed plans and clicks "Reserve" to notify the server.

[1588] 8. Reservation Procedure

[1589] The server sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information.

[1590] 9. Confirmation and follow-up

[1591] The server sends reminders as the date of the stay approaches and collects feedback after the stay.

[1592] Example prompt: "I'd like to stay in Tokyo with my family of five. My budget is under 50,000 yen per night, and I'd like a place with easy access to tourist spots. Could you suggest some accommodation plans I could use as a reference?"

[1593] In this way, the present invention allows users to easily find the most suitable accommodation that suits their emotional state, and also allows accommodation providers to efficiently utilize vacant rooms.

[1594] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1595] Step 1:

[1596] Data collection

[1597] The server collects real-time reservation status and availability information through the API of the accommodation booking site. For unregistered accommodations, it also uses web scraping technology to obtain information. Specifically, it uses Python's requests library to obtain data from the API and BeautifulSoup to extract the necessary information from the web page. This allows it to collect information such as hotel name, address, price, availability, and available services.

[1598] Input: API endpoint URL, web page URL

[1599] Output: Detailed information about the accommodation (hotel name, address, price, availability, services)

[1600] Step 2:

[1601] Data analysis

[1602] The server inputs the collected data into an AI algorithm to calculate a rating score for each accommodation. Machine learning libraries such as TensorFlow and Scikit-learn are used to calculate the rating score. Specific rating criteria include price, reviews, location, and services offered. An overall score is calculated by weighting each rating criterion, and the analysis results are stored in an SQL database.

[1603] Input: Collected accommodation details

[1604] Output: Accommodation information with rating scores

[1605] Step 3:

[1606] User request input

[1607] Users use their devices (smartphones or PCs) to enter their desired accommodation requirements. Specifically, they use an HTML form to enter the dates of stay, number of guests, budget, desired area, specific services, etc. The entered data is sent asynchronously to the server using JavaScript (AJAX).

[1608] Input: User's desired accommodation conditions (date of stay, number of people, budget, area, services)

[1609] Output: User's accommodation preferences sent to the server

[1610] Step 4:

[1611] Manipulating the Emotion Engine

[1612] Users provide emotional data through facial expressions and voice. This data is acquired using the OpenCV library and Google Cloud's Speech-to-Text API. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The evaluation results are sent to the server for further processing.

[1613] Input: User's facial expression data, voice data

[1614] Output: Parsed emotional state data

[1615] Step 5:

[1616] Conditional Filtering

[1617] The server filters the accommodation information in the database based on the user's input criteria and emotion data. It uses the Pandas library to narrow down the data that matches the criteria and calculates the optimal price and available room combination. For example, it generates plans that suggest combinations of triple and double rooms, or rooms available on the same day at different hotels.

[1618] Input: User's accommodation preferences, analyzed emotional state data, accommodation information with rating scores

[1619] Output: Filtered adaptive accommodation plans

[1620] Step 6:

[1621] Providing the optimal plan

[1622] The server generates an accommodation plan based on the filtered results to provide feedback to the user. This information is displayed on the device using HTML and CSS, showing detailed information about each accommodation (accommodation name, address, price, details of available rooms) and specific suggestions for staying in multiple rooms or across different accommodations.

[1623] Input: Filtered accommodation plans

[1624] Output: The best accommodation plan displayed on the device

[1625] Step 7:

[1626] User plan selection

[1627] The user checks the accommodation plans presented and selects the plan they want. This selection is made using a JavaScript-driven "Book" button, and the information about the selected plan is sent to the server.

[1628] Input: The optimal accommodation plan displayed on the device

[1629] Output: User selections sent to the server

[1630] Step 8:

[1631] Reservation procedure

[1632] The server sends a reservation request to the relevant accommodation facility via API based on the details of the selected accommodation plan. After receiving the response from the API, the server saves the final reservation information in a database. It also sends a reservation confirmation email to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1633] Input: User selection information sent to the server

[1634] Output: Reservation request to the accommodation, confirmation of reservation to the user

[1635] Step 9:

[1636] Acknowledgments and follow-up

[1637] The server sends reminders to users as their stay approaches using an SMS API such as Twilio, and collects feedback from users after their stay, which is used to calculate future rating scores.

[1638] Input: Reservation information stored on the server

[1639] Output: Reminder notification to user, feedback from user

[1640] (Application example 2)

[1641] 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."

[1642] Conventional accommodation reservation systems were unable to present optimal accommodation plans that took into account the user's emotional state, making it difficult to meet the user's latent needs. Furthermore, because there was no dynamic feedback based on emotions, the proposed plans did not necessarily increase user satisfaction. This led to user abandonment and dissatisfaction, and accommodation operators lost opportunities to effectively utilize available rooms.

[1643] The specification process 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 collecting reservation status and availability information of accommodation facilities, means for calculating accommodation facility evaluation scores using an AI algorithm, means for receiving user input conditions and filtering related accommodation facilities to propose optimal combinations, means for acquiring emotional data from the user's facial expressions and voice and dynamically adjusting the proposal content based on the emotional state, and means for reserving the accommodation plan selected by the user. This enables users to find more satisfying accommodation plans and increases opportunities for accommodation facility operators to effectively utilize available rooms.

[1644] "Accommodation reservation status" is information indicating whether or not a specific accommodation facility currently has reservations and the number of reservations.

[1645] "Vacancy information" is information indicating rooms currently available for reservation at a specific accommodation facility and the availability of each room.

[1646] "Means" refers to methods or technical devices used to achieve a particular purpose.

[1647] The "accommodation rating score" is a comprehensive rating calculated using multiple evaluation criteria such as the accommodation's price, reviews, location, and services offered.

[1648] An "AI algorithm" refers to a computational procedure or model used to solve a specific problem using artificial intelligence.

[1649] "User input conditions" refers to information that the user inputs to the system regarding the accommodation conditions desired by the user, such as the date of stay, number of guests, budget, desired area, etc.

[1650] "Related accommodations" refers to accommodations that may be suitable based on the user's input criteria.

[1651] "Filtering" refers to the process of extracting information that meets specific conditions from a large amount of information.

[1652] The "optimal combination" refers to the result of selecting from multiple options the one that best suits the user's conditions and emotional state.

[1653] "Obtaining emotional data from a user's facial expressions and voice" refers to the process of analyzing a user's facial expressions and tone of voice to identify the user's current emotional state.

[1654] "Dynamic adjustment of suggestion content based on emotional state" refers to using acquired emotional data to make optimal suggestions in real time according to the user's current mood and emotions.

[1655] An "accommodation plan" refers to a plan including specific facility names, rooms, and available services that the system proposes for a user's accommodation.

[1656] "Reservation" refers to the process of officially reserving accommodation based on the accommodation plan selected by the user.

[1657] The present invention combines an AI system with an emotion engine to optimize accommodation use. This system can be specifically implemented as follows.

[1658] The server first collects real-time reservation status and availability information from accommodation booking sites using APIs, and also uses web scraping technology to collect information from unregistered accommodations. The collected data includes detailed information such as the accommodation's name, address, price, availability, and available services.

[1659] The server then inputs the collected data into an AI algorithm to calculate a rating score for each accommodation, which is a comprehensive score based on factors such as price, reviews, location, and services provided. The analysis results are then stored in a database.

[1660] Users input their desired accommodation requirements using a device such as a smartphone or PC. These requirements include the date of stay, number of guests, budget, desired area, specific services, etc. The input information is sent from the device to the server.

[1661] The emotion engine acquires emotional data from the user's facial expressions and voice input. The emotion engine analyzes this data and evaluates the user's emotional state in real time. The emotion evaluation results are used to dynamically adjust the accommodation plans proposed by the server.

[1662] The server filters accommodation information in the database based on the user's input criteria and emotional data. In particular, AI calculates the optimal combination of rates and availability, and prepares to propose plans combining multiple rooms or different accommodations. For example, it suggests a combination of a triple room and a double room, or rooms available at different hotels on the same day.

[1663] Next, the server creates an accommodation plan that best suits the user's conditions and emotional state based on the filtering results and displays it on the terminal, along with detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions for staying in multiple rooms or accommodations.

[1664] The user checks the accommodation plans presented and selects the one they want. The selected plan is confirmed by clicking the "Book" button, and the information is sent from the terminal to the server.

[1665] The server sends a reservation request to each relevant accommodation facility via API based on the details of the accommodation plan selected by the user. After receiving a reservation confirmation response from the accommodation facility, the server saves the final reservation information in the database. It also sends a reservation confirmation notification to the user and displays confirmation information (reservation confirmation number, check-in procedure, etc.) on the terminal.

[1666] Finally, the server can optionally send reminders as the date of the stay approaches, assist with check-in and check-out procedures, and even collect feedback after the stay, which will be used to calculate future rating scores.

[1667] This system makes it easier for users to find the accommodation that best suits their emotional state, and also enables accommodation providers to make effective use of vacant rooms.

[1668] As a concrete example, let's consider the case where a family of five is looking for a place to stay in Tokyo. The server collects a large amount of up-to-date information on accommodations in the Tokyo area, analyzes this data using AI, and calculates a rating score for each hotel. The user inputs the conditions for accommodation in Tokyo for five people (e.g., budget, dates of stay) into the device and submits it. The emotion engine obtains emotional data from the user's facial expressions and voice, and sends the analysis results to the server.

[1669] The server optimizes the combination of multiple hotels and room types based on the user's requirements and emotional data, and extracts available plans. For example, a plan combining a triple room at Hotel A with a double room at Hotel B, or a plan to stay in two separate rooms at Hotel C, are possible. The user selects the plan they like best from the presented plans and clicks "Book" to notify the server. The server then sends a reservation request to the accommodation facility based on the selected plan and notifies the user of the confirmed reservation information. The server sends reminders as the check-in date approaches and collects feedback after the stay to suggest better plans for the next time.

[1670] Examples of prompts for a generative AI model might include:

[1671] Prompt: "A user is trying to order a meal. Determine the user's emotional state from their facial expressions and suggest restaurants and meal options that best fit that emotion. Also consider the user's past dining history, reviews, and preferred flavors and types."

[1672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1673] Step 1:

[1674] The server collects real-time reservation status and availability information from accommodation booking sites using APIs. This process involves obtaining real-time reservation data from designated accommodations as input and storing it in an internal database. For unregistered accommodations, the server uses web scraping technology to collect information and stores it in the internal database as well.

[1675] Step 2:

[1676] The server uses the collected data to calculate an accommodation rating score using an AI algorithm. Specifically, it uses an AI algorithm (e.g., a machine learning model) to calculate the rating score based on information such as price, reviews, location, and services provided. This score is saved in a database and can be used for subsequent filtering processes.

[1677] Step 3:

[1678] Users input their desired accommodation conditions using a device such as a smartphone or PC, and send them to the server from the device. These desired accommodation conditions include the date of stay, number of guests, budget, desired area, specific services, etc. The input data is received by the server and used for subsequent processing.

[1679] Step 4:

[1680] The device acquires emotional data from the user's facial expressions and voice input. Using an emotion engine, the device analyzes the acquired facial images and voice data to evaluate the user's emotional state in real time. The evaluation results are sent from the device to a server and used to dynamically adjust the content of suggestions.

[1681] Step 5:

[1682] The server filters accommodation information in the database based on the user's input criteria and emotional data. Specifically, it narrows down the accommodations that fit the input criteria and dynamically optimizes the suggestions based on the emotional data. Using an AI algorithm, it optimally combines information such as price and availability to prepare a proposal for a combination of multiple rooms or different accommodations.

[1683] Step 6:

[1684] The server then creates an accommodation plan that best suits the user's requirements and emotional state based on the filtering results and displays it on the device. This display includes detailed information about each hotel (such as the hotel name, address, price, and details of available rooms) and specific suggestions ...

Claims

1. A means of collecting information on accommodation reservations and availability; A method to calculate the accommodation rating score using an AI algorithm, A means for receiving input conditions from a user and filtering relevant accommodations to suggest an optimal combination; A means for booking the accommodation plan selected by the user; A system including:

2. The system according to claim 1, wherein information on unregistered accommodations is collected using web scraping technology.

3. The system according to claim 1, further comprising means for proposing a combination of a plurality of accommodation facilities.

Images