System

Abstract

To provide a system.SOLUTION: The system includes: means for allowing a user to input travel needs or preferences; means for transmitting the input data from a terminal to a server; means for a server to input a prompt sentence into a generative AI model; means for the generative AI model to generate a travel plan; means for the server to transmit the generated travel plan to the terminal; a means for the user to review the travel plan and to input a modification or an additional request as needed; means for the terminal to transmit the modification data to the server; means for the server to re-input a prompt sentence into the generative AI model and generate an updated travel plan; and means for the server to transmit the updated travel plan to the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Gathering information and managing itineraries while traveling requires a lot of time and effort. It is also difficult to obtain optimal itineraries, sightseeing spots, and restaurant recommendations tailored to individual travelers' needs and preferences. Furthermore, there is a lack of systems that provide real-time flight, weather, and local traffic information. [Means for solving the problem]

[0005] We have developed a system that provides an AI-powered personal travel assistant. The system inputs customers' travel needs and preferences and then suggests optimal itineraries, tourist attractions, and restaurants based on those inputs. It also provides real-time flight, weather, and local traffic information, allowing travelers to efficiently plan their trips and stay informed during their trip. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13]FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One example of the present invention is a system that provides an AI-powered personal travel assistant. This system accepts input of travel needs and preferences from customers through a dedicated application or chatbot. For example, a customer can input a preference such as "I want to relax at a resort near the beach."

[0029] "Example 2"

[0030] Based on the customer's needs and preferences, the system will suggest the best itineraries, tourist attractions, and restaurants. These suggestions are made using AI machine learning to learn the customer's preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[0031] "Example 3"

[0032] In addition, the system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently. For example, if a flight is delayed, the system can provide that information to customers in real time and suggest alternative flights or transportation options.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Customers input their travel needs and preferences through a dedicated application or chatbot, for example, "I want to relax at a resort near the beach."

[0036] Step 2: The system analyzes your needs and preferences and makes recommendations based on them, including the best itinerary, attractions, and restaurants.

[0037] Step 3: The proposal is communicated to the customer, who reviews the proposal and adjusts the itinerary as necessary.

[0038] "Example 2"

[0039] Step 1: The system uses AI machine learning to learn customer preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[0040] Step 2: The learned results are stored in a database and used the next time a recommendation is made. Step 3: When the customer enters new travel needs or preferences, the system combines them with the learned results to make more accurate recommendations.

[0041] "Example 3"

[0042] Step 1: The system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently.

[0043] Step 2: For example, if a flight is delayed, the system provides that information to the customer in real time.

[0044] Step 3: Additionally, the system can suggest alternative flights and transportation options, allowing customers to quickly adjust their travel plans.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] Conventional travel planning systems have difficulty providing optimal travel plans that meet the individual needs and preferences of users. They also cannot respond to requests for real-time revisions or additions, which makes it difficult to increase user satisfaction. Furthermore, since proposals do not take into account past travel history or rating information, they are unable to provide plans that perfectly match the user's preferences.

[0048] 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.

[0049] In this invention, the server includes: a means for a user to input travel needs and preferences; a means for the terminal to send the input data to the server; a means for the server to input a prompt sentence to the generative AI model; a means for the generative AI model to generate a travel plan; a means for the server to send the generated travel plan to the terminal; a means for the user to check the travel plan and input corrections or additions as necessary; a means for the terminal to send the correction data to the server; a means for the server to again input a prompt sentence to the generative AI model to generate an updated plan; and a means for the server to send the updated travel plan to the terminal. This makes it possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction.

[0050] "User" means an individual or entity that inputs travel needs and preferences and reviews and modifies proposed travel plans.

[0051] "Terminal" refers to an electronic device that allows a user to input their travel needs and preferences and communicate with the server. Examples of such devices include smartphones, tablets, and PCs.

[0052] The "server" is a central processing unit that receives data sent by the user, inputs prompt sentences into the generative AI model, and sends the generated travel plan to the user's terminal.

[0053] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates optimal travel plans. Specifically, it includes models that use machine learning algorithms.

[0054] A "prompt" is text data that is input into the generative AI model and contains information such as the user's needs, preferences, budget, and travel duration.

[0055] A "trip plan" is a travel suggestion, including accommodation, attractions, and dining options, generated by a generative AI model based on the user's needs and preferences.

[0056] "Correction data" refers to data that includes additional requests or changes that a user inputs to a proposed travel plan.

[0057] "Real-time" refers to the generative AI model's ability to respond immediately to user input and modifications, quickly generating new travel plans.

[0058] This invention provides an AI-powered personal travel assistant system that allows users to input their travel needs and preferences and generate an optimal travel plan based on the input. Specific embodiments of this system are described below.

[0059] First, a user uses a dedicated application or chatbot to input their travel needs and preferences, such as a specific request such as "I want to relax at a resort near the beach." They also input a budget of "under 100,000 yen," a trip duration of "three days," and a departure point of "Tokyo."

[0060] Next, the device sends the data entered by the user to the server in real time. The server inputs a prompt into the generative AI model based on the received data. The prompt specifically describes the user's requests and conditions. For example, the following prompts can be generated:

[0061] User Request: Relax at a resort near the beach

[0062] Budget: Under 100,000 yen

[0063] Trip duration: 3 days

[0064] Departure point: Tokyo

[0065] The generative AI model analyzes the input prompt and generates a travel plan that best suits the user's requests. Specifically, this includes models that use machine learning algorithms. For example, a generative AI model such as OpenAI's (registered trademark) GPT-4 (registered trademark) is used. This model takes into account the user's preferences, budget, weather information at the travel destination, and other factors to create a plan that includes accommodation, tourist attractions, and dining options.

[0066] The generated travel plan is sent from the server to the user's device. The user can review the plan through the device and input any corrections or additions they need. For example, they could input a request such as "I'd like to increase my budget a little and stay at a more luxurious accommodation."

[0067] The device sends the revised data entered by the user to the server, which then inputs prompts into the generative AI model based on the revised data to generate an updated travel plan. For example, the budget could be increased to 150,000 yen, and a plan including more luxurious accommodations would be generated.

[0068] The updated travel plan is then sent from the server to the user's terminal, where the user can review the plan and make a final decision.

[0069] In this way, it is possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction. The system can also take into account the user's past travel history and rating information when making suggestions, thereby providing more personalized travel plans.

[0070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0071] Step 1:

[0072] Users input their travel needs and preferences.

[0073] Specifically, a user uses a dedicated application or chatbot to input a request such as "I want to relax at a resort near the beach," and also inputs a budget of "under 100,000 yen," a travel period of "3 days," and a departure point of "Tokyo."

[0074] Input: User's travel needs and preferences, budget, travel duration, departure point

[0075] Output: Input data

[0076] Step 2:

[0077] The terminal sends the input data to the server.

[0078] Specifically, the terminal transmits the data input by the user to the server in real time.

[0079] Input: Data entered by the user

[0080] Output: Data sent to the server

[0081] Step 3:

[0082] The server inputs a prompt sentence into the generative AI model.

[0083] Specifically, the server generates a prompt based on the received data:

[0084] User Request: Relax at a resort near the beach

[0085] Budget: Under 100,000 yen

[0086] Trip duration: 3 days

[0087] Departure point: Tokyo

[0088] This prompt sentence is input into the generative AI model.

[0089] Input: Data sent to the server

[0090] Output: The prompt sentence that is input to the generative AI model

[0091] Step 4:

[0092] A generative AI model generates a travel plan.

[0093] Specifically, the generative AI model analyzes the prompt and generates a travel plan that best suits the user's needs, including accommodation, tourist attractions, and dining options.

[0094] Input: prompt statement

[0095] Output: Generated itinerary

[0096] Step 5:

[0097] The server transmits the generated travel plan to the terminal.

[0098] Specifically, the server sends the generated travel plan to the user's terminal, where the user can check the plan.

[0099] Input: Generated itinerary

[0100] Output: Trip plan sent to the device

[0101] Step 6:

[0102] The user checks the travel plan and enters any corrections or additions as necessary.

[0103] Specifically, the user checks the proposed plan and inputs any modifications or additional requests, such as "I'd like to increase my budget a little and get a more luxurious accommodation."

[0104] Input: Travel plan sent to the device

[0105] Output: Corrections and additions requested

[0106] Step 7:

[0107] The terminal transmits the correction data to the server.

[0108] As a specific operation, the terminal transmits the correction data input by the user to the server.

[0109] Input: Corrections and additions

[0110] Output: Corrected data sent to the server

[0111] Step 8:

[0112] The server again inputs the prompt sentence into the generative AI model and generates an updated plan.

[0113] Specifically, the server inputs the prompt sentences into the generative AI model again based on the revised data, and generates an updated travel plan, for example, increasing the budget to 150,000 yen and including more luxurious accommodations.

[0114] Input: Correction data

[0115] Output: Updated itinerary

[0116] Step 9:

[0117] The server sends the updated travel plan to the terminal.

[0118] Specifically, the server sends the updated travel plan to the terminal, where the user can review the plan again and make a final decision.

[0119] Input: Updated travel plans

[0120] Output: Updated itinerary sent to device

[0121] (Application example 1)

[0122] Next, a description will be given of Application Example 1 of Embodiment 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."

[0123] Conventional travel planning systems have difficulty providing optimal travel plans based on customer needs and preferences. They also lack real-time information and suggestions that take into account customers' past travel history, making it difficult to increase customer satisfaction. Furthermore, they lacked the ability to automatically generate and customize travel plans using generative AI models, making it difficult to provide a user-friendly system for customers.

[0124] 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.

[0125] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on the customer's input using a generative AI model, and means for displaying the generated travel plan to the customer so that the customer can select and customize it. This makes it possible to provide optimal travel plans that meet the customer's needs, thereby increasing customer satisfaction.

[0126] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to provide optimal travel plans for individual customers.

[0127] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[0128] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel schedules, tourist spots, and restaurants based on customer input.

[0129] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that provides current flight status, weather, and local traffic conditions in real time.

[0130] A "generative AI model" is an artificial intelligence model that automatically generates travel plans based on customer input.

[0131] "Means for generating travel plans" refers to a function that uses a generative AI model to create travel plans based on customer needs.

[0132] "Means for displaying travel plans to customers and allowing them to select and customize" refers to the function of presenting generated travel plans to customers and allowing them to select or change those plans.

[0133] The following system configuration is adopted as an embodiment of this invention: The server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on customer input using a generative AI model, and means for displaying the generated travel plan to the customer, allowing them to select and customize it.

[0134] Hardware and software used

[0135] Hardware: Smartphone

[0136] Software: Python, OpenAI API

[0137] Data processing and calculation

[0138] 1. User Input: Users input their travel needs and preferences through a smartphone application, such as a specific preference such as "I want to relax at a resort near the beach."

[0139] 2. Use of generative AI model: The server sends the user's input as a prompt to the generative AI model (OpenAI's GPT-3 (registered trademark)). An example of a prompt sentence is as follows:

[0140] User travel needs and preferences: Relax at a resort near the beach

[0141] Please suggest a recommended travel plan.

[0142] 3. Itinerary Generation: The generative AI model generates an itinerary based on the prompts, including accommodations, attractions, restaurants, activities, etc.

[0143] 4. Display and customize itinerary: The server displays the generated itinerary to the user and allows the user to select and customize the itinerary. For example, the user can select "Beachfront Resorts in Maui" and add "Sunset Cruise."

[0144] Specific examples

[0145] If a user inputs, "I want to relax at a resort near the beach," the generative AI model will suggest the following itinerary:

[0146] Recommended travel plans:

[0147] 1. Accommodation: Maui Beachfront Resorts

[0148] 2. Sightseeing spot: Snorkeling at Hanauma Bay

[0149] 3. Restaurant: Beachside seafood restaurant

[0150] 4. Activity: Sunset Cruise

[0151] In this way, it becomes possible to provide the user with an optimal travel plan that meets their needs, thereby increasing customer satisfaction.

[0152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0153] Step 1:

[0154] The user launches the smartphone application and inputs their travel needs and preferences.

[0155] Input: User's travel needs and preferences (e.g., "I want to relax at a resort near the beach")

[0156] Output: User input data

[0157] Specific operation: The user enters the desired travel conditions in text format into the application's input form and presses the submit button.

[0158] Step 2:

[0159] The terminal transmits the user's input data to the server.

[0160] Input: User-entered data

[0161] Output: User input data sent to the server

[0162] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[0163] Step 3:

[0164] The server receives the user's input data and sends it to the generative AI model as prompts.

[0165] Input: User input data sent to the server

[0166] Output: The prompt sent to the generative AI model

[0167] Specific operation: The server converts the user's input data into a prompt sentence and sends it to the generative AI model using the OpenAI API. An example of a prompt sentence is as follows:

[0168] User travel needs and preferences: Relax at a resort near the beach

[0169] Please suggest a recommended travel plan.

[0170] Step 4:

[0171] A generative AI model generates a travel plan based on the prompt.

[0172] Input: The prompt sent to the generative AI model

[0173] Output: Generated itinerary

[0174] How it works: The generative AI model analyzes the prompt and generates an appropriate itinerary, including accommodations, tourist attractions, restaurants, activities, etc.

[0175] Step 5:

[0176] The server receives the generated itinerary and displays it to the user.

[0177] Input: Generated itinerary

[0178] Output: The itinerary displayed to the user

[0179] Specific operation: The server sends the travel plan received from the generative AI model to the user's device and displays it on the application.

[0180] Step 6:

[0181] Users select and customize the itinerary displayed.

[0182] Input: A travel plan displayed to the user

[0183] Output: User selected and customized travel plan

[0184] What it does: The user selects the itinerary they want from the displayed options and customizes it as needed, for example, adding specific activities or changing accommodations.

[0185] Step 7:

[0186] The device sends the user's selected and customized travel plan to the server.

[0187] Input: User-selected and customized travel plans

[0188] Output: The customized itinerary sent to the server.

[0189] Specific operation: The device sends the travel plan selected and customized by the user to the server as an HTTP request.

[0190] Step 8:

[0191] The server saves the final itinerary and sends a confirmation message to the user.

[0192] Input: Customized itinerary sent to the server

[0193] Output: A confirmation message sent to the user

[0194] What happens: The server saves the customized itinerary to a database and sends the user a confirmation message that includes the final itinerary details.

[0195] Example 2

[0196] Next, a description will be given of Example 2 of Form 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."

[0197] Conventional travel planning systems have difficulty making optimal suggestions based on the user's individual needs and preferences. Furthermore, they are unable to provide real-time information on transportation options, weather, and local traffic information in an integrated manner, which makes it difficult to improve the efficiency and satisfaction of users' travel plans.

[0198] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting a user's travel needs and preferences, a means for proposing optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, a means for executing a machine learning model to generate the proposals, a means for transmitting the proposals to the user's terminal, a means for displaying the proposals on the user's terminal, and a means for providing real-time information on transportation means, weather information, and local traffic information. This makes it possible to make optimal proposals based on the user's individual needs and preferences, thereby improving the efficiency and satisfaction of travel planning.

[0199] "User" means an individual or entity that utilizes the System to input travel needs and preferences and receive suggestions.

[0200] "Travel needs and preferences" refers to the conditions and preferences a user has for travel, and specifically includes the places they want to go, the types of accommodations they like, and their food preferences.

[0201] An "itinerary" refers to a travel plan or schedule, including details of places to visit, transportation, accommodation, etc.

[0202] "Tourist attractions" refer to places and sights that are worth visiting at a travel destination, including historical buildings, natural landscapes, theme parks, etc.

[0203] "Eating establishment" refers to a place where a user visits to eat, and includes restaurants, cafes, bars, etc.

[0204] "Suggestions" refers to lists of itineraries, attractions, and dining options generated by the system based on the user's needs and preferences.

[0205] A "machine learning model" refers to an algorithm that learns patterns based on data and makes predictions and classifications.

[0206] "Terminal" refers to the device used by a user to access the system, and specifically includes a PC, smartphone, tablet, etc.

[0207] "Real-time transportation information" refers to up-to-date information about current traffic conditions and schedules.

[0208] "Weather information" refers to the latest information about the weather at your travel destination.

[0209] "Local traffic information" refers to up-to-date information about public transport and road conditions at your travel destination.

[0210] This invention is a system that suggests optimal itineraries, tourist spots, and restaurants based on a user's travel needs and preferences. The system receives user input, uses machine learning models to generate suggestions, and displays them on the user's device.

[0211] Hardware and software used

[0212] Hardware: Servers, devices (PCs, smartphones, tablets)

[0213] Software: Machine learning models (e.g., TENSORFLOW (registered trademark), PyTorch), databases (e.g., MySQL (registered trademark), PostgreSQL), web servers (e.g., Apache (registered trademark), Nginx)

[0214] Explanation of program processing

[0215] 1. Accepting user input

[0216] The user uses the device to input their travel needs and preferences, for example, "I want to go to a beach resort."

[0217] 2. Data transmission

[0218] The terminal sends the entered data to the server as an HTTP request.

[0219] 3. Data storage

[0220] The server stores the received data in a database, which also stores the user's previous input data.

[0221] 4. Running the machine learning model

[0222] The server runs a machine learning model on the stored data, which learns the user's preferences and generates optimal suggestions.

[0223] 5. Proposal Generation

[0224] The server then generates a list of itineraries, tourist spots, and restaurants that are optimal for the user based on the learning results. For example, if a user prefers beach resorts, it will suggest resorts that are close to the beach.

[0225] 6. Submit your proposal

[0226] The server sends the generated proposal to the device, and the data is sent as an HTTP response.

[0227] 7. Display of Suggestions

[0228] The terminal displays the received suggestions to the user, who can then review the suggested list and make a selection.

[0229] Specific examples

[0230] Examples:

[0231] The user enters, "I want to go to a beach resort."

[0232] The server receives this information and stores it in a database.

[0233] A machine learning model learns user preferences and generates beach resort suggestions.

[0234] The server sends suggestions such as "Waikiki Beach Resort in Hawaii" or "Nusa Dua Beach Resort in Bali" to the device.

[0235] The terminal displays these suggestions to the user.

[0236] Example prompt sentence:

[0237] User input: "I want to go to a beach resort"

[0238] Server processing: Learn user preferences and generate beach resort suggestions

[0239] Example suggestions: "Waikiki Beach Resort in Hawaii", "Nusa Dua Beach Resort in Bali"

[0240] In this way, the system can make optimal suggestions based on the user's needs and preferences.

[0241] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0242] Step 1:

[0243] A user uses a terminal to input travel needs and preferences. For example, the user inputs "I want to go to a beach resort." The input data is text data that specifically indicates the user's travel needs and preferences.

[0244] Step 2:

[0245] The device sends the entered data to the server as an HTTP POST request. The input is text data indicating the user's needs and preferences, and the output is the request data sent to the server.

[0246] Step 3:

[0247] The server stores the received data in a database, which also stores the user's previous input data. The input is the text data received as an HTTP request, and the output is the data stored in the database.

[0248] Step 4:

[0249] The server runs a machine learning model based on the stored data. The model learns the user's preferences and generates optimal suggestions. The input is the user's needs and preferences stored in the database, and the output is the suggestion data that is the result of learning. Specifically, the server uses TensorFlow to run the model and learn the user's preferences.

[0250] Step 5:

[0251] Based on the learning results, the server generates a list of itineraries, tourist spots, and restaurants that are optimal for the user. The input is the learning results of the machine learning model, and the output is a list of suggestions. Specifically, the server generates suggestions such as "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali."

[0252] Step 6:

[0253] The server sends the generated proposals to the device. The data is sent as an HTTP response. The input is the proposal list, and the output is the response data sent to the device.

[0254] Step 7:

[0255] The device displays the received suggestions to the user. The user can check the suggested list and select one. The input is the response data received from the server, and the output is the suggested list displayed to the user. Specifically, the device displays information about "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali" on the app screen.

[0256] (Application example 2)

[0257] Next, a description will be given of Application Example 2 of Form 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."

[0258] Conventional personal travel assistant systems can suggest itineraries, tourist spots, and restaurants based on a customer's travel needs and preferences, but they have the problem of not being able to suggest restaurants or dishes that take into account a customer's food preferences or past ordering history.In addition to providing real-time flight information, weather information, and local traffic information, there is a demand for more personalized services by making suggestions based on a customer's food preferences.

[0259] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0260] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, and means for learning the customer's food preferences and past order history and suggesting optimal restaurants and dishes, thereby making it possible to further personalize the customer's travel experience and suggest optimal restaurants and dishes based on the customer's food preferences.

[0261] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to suggest optimal itineraries, sightseeing spots, and restaurants based on each customer's travel needs and preferences.

[0262] "Means for inputting customer travel needs and preferences" means any interface or device through which a customer inputs their travel wishes and preferences into the system.

[0263] "Means of suggesting optimal itineraries, tourist spots, and restaurants based on input needs and preferences" refers to algorithms and software that suggest optimal travel plans, tourist spots, and places to eat based on information input by customers.

[0264] "Means of providing real-time flight information, weather information, and local traffic information" refers to systems and devices that obtain information such as current flight status, weather, and traffic conditions in real time and provide it to customers.

[0265] "Means of learning customers' food preferences and past order history and suggesting the most suitable restaurant and cuisine" refers to algorithms and software that use machine learning to analyze customers' food preferences and past order history, and then suggest the most suitable restaurant and cuisine based on that.

[0266] As an embodiment of the present invention, the following system can be constructed.

[0267] First, the server runs software to provide an AI-powered personal travel assistant. The server provides an interface for customers to input their travel needs and preferences. This interface is typically implemented as a smartphone application or a web application.

[0268] The server then runs an algorithm to suggest optimal itineraries, attractions, and restaurants based on the input needs and preferences. The algorithm uses machine learning to learn the customer's preferences and makes suggestions based on them. For example, if the server learns that the customer prefers beach resorts, it will suggest resorts near the beach.

[0269] Additionally, the server accesses databases and APIs that provide real-time flight, weather, and local traffic information, and provides this information to customers, allowing them to plan their trips based on the most up-to-date information.

[0270] The server also runs an algorithm that learns the customer's food preferences and past order history to suggest the most suitable restaurant and cuisine. This algorithm takes into account the customer's past order history and rating information to suggest restaurants and cuisines that best suit the customer's preferences. For example, if a customer inputs that they like "sushi," "Italian," and "spicy food," the server will suggest the best restaurant based on these preferences.

[0271] As a concrete example, by inputting the following prompt sentence into a generative AI model, optimal restaurant suggestions based on the customer's preferences can be obtained.

[0272] Example prompt sentence:

[0273] User Preferences: ["Sushi", "Italian", "Spicy Food"]

[0274] Restaurant Data:

[0275] {"name": "Sushi Taro", "description": "A restaurant serving fresh sushi"},

[0276] {"name": "Italian Mario", "description": "A restaurant where you can enjoy authentic Italian cuisine"},

[0277] {"name": "Spicy food specialty restaurant", "description": "A restaurant that specializes in spicy food"},

[0278] {"name": "Japanese restaurant", "description": "Restaurant serving traditional Japanese cuisine"},

[0279] {"name": "French restaurant", "description": "A restaurant where you can enjoy high-class French cuisine"}

[0280] ]

[0281] In this way, servers can better personalize a customer's travel experience and make optimal restaurant and cuisine recommendations based on the customer's food preferences.

[0282] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0283] Step 1:

[0284] Users input their travel needs and preferences through a smartphone or web application.

[0285] Input: User's travel needs and preferences (e.g., "beach resort," "sushi," "Italian")

[0286] Output: Data on input needs and preferences

[0287] Step 2:

[0288] The server receives the user's input data and inputs it into the machine learning model.

[0289] Input: Data about user needs and preferences

[0290] Output: The data input into the machine learning model

[0291] Step 3:

[0292] The server uses machine learning models to learn user preferences.

[0293] Input: Data about user needs and preferences

[0294] Output: A feature vector based on user preferences

[0295] Step 4:

[0296] The server generates optimal itineraries, sightseeing spots, and restaurant suggestions based on the user's preferences.

[0297] Input: A feature vector based on user preferences

[0298] Output: A list of recommended itineraries, attractions, and restaurants

[0299] Step 5:

[0300] The server retrieves real-time flight information, weather information, and local traffic information and provides it to the user.

[0301] Input: Real-time information obtained from an external API

[0302] Output: Latest flight, weather and traffic information provided to the user

[0303] Step 6:

[0304] The server learns the user's food preferences and past ordering history and suggests the most suitable restaurant and dish.

[0305] Input: User's food preferences, past order history

[0306] Output: A list of recommendations for the best restaurants and dishes

[0307] Step 7:

[0308] The server inputs prompts into the generative AI model to get optimal restaurant suggestions based on the user's preferences.

[0309] Input: Prompt text (e.g., "User preferences: ["Sushi", "Italian", "Spicy food"]")

[0310] Output: Restaurant suggestions from a generative AI model

[0311] Step 8:

[0312] The server provides the final proposal to the user, allowing the user to make a selection.

[0313] Input: A list of the best itineraries, attractions, and restaurants

[0314] Output: The final list of suggestions provided to the user

[0315] Example 3

[0316] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0317] Traditional travel assistance systems lacked the ability to provide real-time information, making it difficult to quickly respond to problems customers faced during their trip. They also lacked the ability to provide appropriate alternatives for unexpected situations such as flight delays, weather changes, and traffic congestion. This meant customers had to spend a great deal of time and effort changing their travel plans.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0319] In this invention, the server includes a means for providing real-time flight information, weather information, and local traffic information, a means for integrating and analyzing the flight information, weather information, and traffic information, and a means for transmitting the analysis results to the customer's terminal, thereby enabling the customer to receive the latest information in real time and take appropriate action promptly.

[0320] An "AI-powered personal travel assistant" is a system that uses artificial intelligence technology to assist customers in planning their trips.

[0321] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[0322] "A means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, places to visit, and places to eat based on the customer's input needs and preferences.

[0323] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[0324] The "means for integrating and analyzing the flight information, weather information, and traffic information" is a function that brings together and analyzes the various types of information acquired to generate information that is useful to customers.

[0325] "Means for transmitting analysis results to the customer's terminal" is a function for transmitting analyzed information to the device used by the customer.

[0326] "Means for displaying information on the customer's terminal" refers to a function that visually displays the transmitted information on the customer's device.

[0327] "Means for customers to check information and take necessary actions" refers to an interface that allows customers to select and carry out necessary actions based on the displayed information.

[0328] This invention provides an AI-powered personal travel assistant system, which includes a means for inputting a customer's travel needs and preferences, a means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, and a means for providing real-time flight information, weather information, and local traffic information.

[0329] The server uses multiple APIs to collect real-time flight information, weather information, and local traffic information. Specifically, it uses the "Flight Information API" to obtain flight information, the "Weather Data API" to obtain weather information, and the "Traffic Information API" to obtain traffic information. By integrating and analyzing the data obtained from these APIs, it generates information that is useful to customers.

[0330] The server then sends the analysis results to the customer's device, including flight delay information, local weather information, traffic information, etc. This allows the customer to receive the latest information in real time.

[0331] The terminal displays the information received from the server to the user, such as flight delays, local weather, traffic congestion, etc. The terminal provides the information in a visually easy-to-understand format so that the user can easily check the information.

[0332] The user checks the information displayed on the terminal and takes the necessary action. For example, if a flight is delayed, the user can book an alternative flight or consider other means of transportation. The system also has the function of suggesting alternatives to the user.

[0333] As a concrete example, suppose a user has booked a flight from Tokyo to New York. If the flight is delayed, the server obtains the delay information from the "Flight Information API" and notifies the user's device. The server then obtains weather information for New York from the "Weather Data API" and provides it to the user. Finally, the server obtains local traffic information from the "Traffic Information API" and suggests alternative transportation options to the user.

[0334] Examples of prompts to be input to a generative AI model include:

[0335] Prompt: "If a flight from Tokyo to New York is delayed, suggest alternative flights or transportation."

[0336] By inputting this prompt sentence into the generative AI model, the system can provide the user with an appropriate alternative. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] The server collects real-time data.

[0339] Input: Data from flight information API, weather data API, and traffic information API.

[0340] Specific operation: The server calls each API every minute to obtain the latest flight, weather, and traffic information. For example, it obtains flight delay information from the flight information API, weather information for the destination from the weather data API, and local traffic conditions from the traffic information API.

[0341] Output: Real-time data obtained.

[0342] Step 2:

[0343] The server consolidates and analyzes the data.

[0344] Input: Real-time data obtained in step 1.

[0345] How it works: The server integrates the acquired data into a single database. For example, if a flight is delayed, the server compares that information with weather and traffic data to analyze the cause and impact of the delay.

[0346] Output: Consolidated and analyzed data.

[0347] Step 3:

[0348] The server sends the analysis results to the device.

[0349] Input: Data integrated and analyzed in Step 2.

[0350] Specific operation: The server sends the analysis results in JSON format to the terminal. For example, if a flight is delayed, JSON data containing information about the delay time, cause, and alternative flight information is sent to the terminal.

[0351] Output: Analysis results sent to the device.

[0352] Step 4:

[0353] The terminal displays the information to the user.

[0354] Input: Analysis results submitted in step 3.

[0355] Specific operation: The device parses the received JSON data and displays it to the user in a visually understandable format, for example, highlighting flight delay information in red and displaying information about alternative flights in a list format.

[0356] Output: Information displayed to the user.

[0357] Step 5:

[0358] The user reviews the information and takes the necessary action.

[0359] Input: The information displayed in step 4.

[0360] Specific operation: The user checks the information displayed on the terminal and books an alternative flight if necessary. For example, if the flight is delayed by two hours, the user can select the alternative flight suggested by the system and proceed with the booking process.

[0361] Output: The action taken by the user.

[0362] (Application example 3)

[0363] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0364] Conventional personal travel assistant systems provide real-time flight, weather, and local traffic information to assist customers in planning their trips, but there is a need for delivery services to utilize similar real-time information to improve delivery efficiency and avoid delays. In particular, food delivery services, which are easily affected by traffic congestion and bad weather, require optimal route suggestions based on real-time information and alternative routes in the event of delays. This will increase delivery efficiency and customer satisfaction.

[0365] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0366] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for suggesting optimal routes based on real-time traffic and weather information, and means for suggesting alternative routes or handing over to another delivery person in the event of a delivery delay. This enables efficient route suggestions using real-time information and rapid response to delays in delivery work.

[0367] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to help customers plan their trips.

[0368] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[0369] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to customers based on their input needs and preferences.

[0370] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[0371] "A means of suggesting the optimal route based on real-time traffic and weather information" is a function that suggests the optimal delivery route to delivery personnel, taking into account current traffic conditions and weather.

[0372] "Means to suggest alternative routes or handover to other delivery personnel in the event of a delivery delay" is a function that suggests alternative routes or handover to other delivery personnel in the event of a delivery delay.

[0373] The following system configuration will be described as an embodiment of the present invention.

[0374] System Configuration

[0375] The system consists of three main components: a server, a terminal, and a user. The server obtains real-time flight, weather, and local traffic information and calculates the optimal route based on this information. The terminal is a device such as a smartphone or tablet that the user uses to access the system. The user inputs their travel needs and preferences and receives suggestions from the system.

[0376] Program processing

[0377] The server processes the data in the following steps:

[0378] 1. Data Acquisition: The server acquires real-time data from the flight information API, weather information API, and traffic information API using the Python Requests library.

[0379] 2. Data analysis: Analyze the acquired data in JSON format and extract the necessary information.

[0380] 3. Route Calculation: Optimal routes are calculated based on real-time traffic and weather information, using AI models and machine learning algorithms.

[0381] 4. Proposal Generation: Proposes the calculated route and alternative routes to the user. In case of a delay, proposes an alternative route or a handover to another delivery person.

[0382] Hardware and software used

[0383] Hardware: smartphones, tablets, servers

[0384] Software: Python, Requests library, AI model (machine learning algorithm)

[0385] Specific examples

[0386] For example, if a user delivers food from Tokyo to Osaka, the server calculates the optimal route based on real-time traffic and weather information and suggests it to the device. If traffic congestion or bad weather is predicted, the server will suggest an alternative route or hand over to another delivery person.

[0387] Prompt Sentence Examples

[0388] "Enter your current location and destination, and we'll suggest the best route for you based on real-time traffic and weather information."

[0389] In this way, the system provides users with efficient delivery routes and allows for quick response in the event of delays.

[0390] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0391] Step 1:

[0392] The server retrieves real-time data from flight information API, weather information API, and traffic information API.

[0393] Input: API endpoint and request parameters

[0394] Data processing: Sending API requests and receiving responses

[0395] Output: JSON data of flight information, weather information, and traffic information

[0396] Step 2:

[0397] The server analyzes the acquired JSON data and extracts the necessary information.

[0398] Input: JSON data of flight information, weather information, and traffic information

[0399] Data processing: Parse JSON data and extract necessary fields

[0400] Output: Specific information about flight status, weather conditions, and traffic conditions

[0401] Step 3:

[0402] The server calculates the optimal route based on real-time traffic and weather information.

[0403] Input: Specific information about flight status, weather conditions, and traffic conditions

[0404] Data computation: Using AI models to calculate optimal routes that take traffic congestion and weather conditions into account

[0405] Output: Optimal route information

[0406] Step 4:

[0407] The server transmits the calculated optimal route information to the terminal.

[0408] Input: Optimal route information

[0409] Data processing: Converting route information into a format for sending to the device

[0410] Output: Route information sent to the device

[0411] Step 5:

[0412] The terminal displays the route information received from the server to the user.

[0413] Input: Route information received from the server

[0414] Data processing: Display route information in a user-friendly format

[0415] Output: Route information displayed to the user

[0416] Step 6:

[0417] The user inputs the current location information and destination through the terminal.

[0418] Input: current location, destination

[0419] Data processing: Converting location information and destination into a format for sending to the server

[0420] Output: Location and destination sent to the server

[0421] Step 7:

[0422] The server reacquires real-time information based on the location information and destination received from the user and recalculates the optimal route.

[0423] Input: location information, destination

[0424] Data calculation: Send another API request to get the latest flight, weather, and traffic information and recalculate the optimal route

[0425] Output: Updated optimal route information

[0426] Step 8:

[0427] The server sends the updated optimal route information to the terminal, which displays it to the user.

[0428] Input: Updated optimal route information

[0429] Data processing: Converts route information into a format suitable for sending to the device and sends it to the device.

[0430] Output: Updated route information displayed to the user

[0431] In this way, the system utilizes real-time information to provide efficient delivery routes to users.

[0432] 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.

[0433] "Example 1"

[0434] One embodiment of the present invention combines an AI-powered personal travel assistant with an emotion engine. This system recognizes the user's emotions and tailors travel suggestions based on those emotions. Specifically, if the system recognizes that the user is feeling happy or excited, it will suggest active tourist spots and entertainment facilities. On the other hand, if the system recognizes that the user is feeling tired or stressed, it will suggest relaxation facilities and quiet tourist spots.

[0435] "Example 2"

[0436] The emotion engine also recognizes the user's emotions in real time and provides services based on those emotions. For example, if it recognizes that the user is feeling angry or frustrated, it will provide support to resolve the problem. Specifically, it will respond quickly and appropriately to problems that may arise during travel, such as flight delays or hotel troubles.

[0437] "Example 3"

[0438] Furthermore, the emotion engine learns changes in the user's emotions and reflects them in the next travel recommendation. For example, if it recognizes that the user felt joy after visiting a particular tourist spot, it will suggest tourist spots with similar characteristics. This improves user satisfaction and makes the travel experience more personalized.

[0439] The processing flow of each embodiment will be described below.

[0440] "Example 1"

[0441] Step 1: The user launches the application and enters their travel needs and preferences.

[0442] Step 2: The system recognizes the user's emotions using the emotion engine.

[0443] Step 3: Based on the emotions recognized by the system, optimal travel recommendations are made.

[0444] "Example 2"

[0445] Step 1: A user encounters a problem during their trip (e.g., flight delay, hotel trouble).

[0446] Step 2: The system recognizes the user's emotions in real time using an emotion engine. Step 3: Based on the emotions recognized by the system (e.g., anger, frustration), the system provides support for resolving the problem.

[0447] "Example 3"

[0448] Step 1: A user visits a specific tourist attraction.

[0449] Step 2: The system recognizes the user's emotions using an emotion engine and learns changes in those emotions.

[0450] Step 3: The system reflects the learned changes in emotions in its next travel suggestions.

[0451] Example 1

[0452] Next, a description will be given of Example 1 of Form 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."

[0453] Conventional personal travel assistant systems make suggestions based on the user's travel needs and preferences, but they are unable to take the user's emotions into account when making suggestions, which means they are unable to fully increase user satisfaction. Furthermore, they lack real-time information provision and advanced suggestion generation using generative AI models, making it difficult to provide optimal travel plans for users.

[0454] 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.

[0455] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for recognizing a user's emotions and adjusting the travel suggestions based on the emotions, means for generating travel suggestions using a generative AI model, and means for providing the generated travel suggestions to a user terminal, thereby enabling the provision of optimal travel plans that take the user's emotions into consideration.

[0456] An "AI-powered personal travel assistant" is a system that uses artificial intelligence technology to assist users in planning their trips.

[0457] "Means for inputting customer travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[0458] "Means for suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants based on information input by the user.

[0459] "Means for providing real-time flight information, weather information, and local traffic information" is a function that provides users with the latest information on current flight status, weather, traffic conditions, etc.

[0460] The "means for recognizing the user's emotions and adjusting travel suggestions based on those emotions" is a function that analyzes the user's emotional state and adjusts the travel plan based on the results.

[0461] "Means for generating travel suggestions using a generative AI model" refers to a function that uses a generative AI model to automatically create travel suggestions based on a user's needs and emotions.

[0462] The "means for providing the generated travel proposal to the user terminal" is a function for transmitting the generated travel plan to the user's device and displaying it.

[0463] MODE FOR CARRYING OUT THE INVENTION

[0464] This invention relates to an AI-powered personal travel assistant system that provides optimal travel plans that take into account the user's travel needs, preferences, and emotions. The system receives input from the user through a dedicated application or chatbot, generates travel suggestions using an emotion recognition engine and generative AI model, and provides them to the user's device.

[0465] Hardware and software used

[0466] Hardware: Servers, user devices (smartphones, tablets, PCs)

[0467] Software: dedicated applications, chatbots, emotion recognition engines, generative AI models (e.g., GPT-4)

[0468] Data processing and calculation

[0469] 1. Getting user input:

[0470] Users launch a dedicated application or chatbot and input their travel needs and preferences, for example, "I'd like to relax at a resort near the beach."

[0471] The terminal receives the user's input and sends it to the server.

[0472] 2. Performing emotion recognition:

[0473] The server passes the received user input data to the emotion recognition engine, which analyzes emotions from the user's input, voice, facial expressions, etc.

[0474] If the user inputs "tired," the emotion recognition engine determines that the user is tired and returns the analysis result to the server.

[0475] 3. Generate travel suggestions:

[0476] The server generates travel suggestions using a generative AI model (e.g., GPT-4) based on the analysis results from the emotion recognition engine and the user's needs and preferences.

[0477] If the generative AI model recognizes that the user is tired, it will suggest relaxation facilities and quiet tourist spots. For example, it will generate suggestions such as, "The resort we recommend for you is X resort with a quiet beach."

[0478] 4. Providing Proposals:

[0479] The server transmits the generated travel proposal to the user terminal.

[0480] The device displays the received travel suggestions to the user, for example, "The resort we recommend for you is XX Resort, which has a quiet beach."

[0481] Specific examples

[0482] User input: The user opens the application and types, "I've been stressed lately and would like to go somewhere to relax."

[0483] Emotion recognition result: The emotion recognition engine analyzes the user's input and determines that the user is feeling stressed.

[0484] Travel suggestion generation: The generative AI model generates suggestions such as, "The recommended travel destination for you is the hot spring resort of X hot springs. It has a quiet and relaxing environment."

[0485] Providing suggestions: The server sends the generated suggestions to the user's device, which then displays the message, "The recommended travel destination for you is the hot spring resort of X hot springs. It has a quiet and relaxing environment."

[0486] Prompt Sentence Examples

[0487] "If a user is feeling stressed, suggest a relaxing travel destination."

[0488] "If the user is experiencing joy, suggest active tourist destinations."

[0489] The above is an embodiment of the present invention.

[0490] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0491] Step 1:

[0492] Input of user needs and preferences

[0493] User: Launches a dedicated application or chatbot and inputs their travel needs and preferences, for example, "I'd like to relax at a resort near the beach."

[0494] Input: Text data about the user's travel wishes and preferences.

[0495] Output: User input data is sent to the terminal.

[0496] What happens: The user enters "I want to relax at a resort near the beach" into the application's input field and presses the submit button.

[0497] Step 2:

[0498] Sending User Input

[0499] Terminal: Sends user input data to the server.

[0500] Input: User input data.

[0501] Output: User input data sent to the server.

[0502] Specific operation: The device sends the user's input data to the server via the Internet.

[0503] Step 3:

[0504] Performing emotion recognition

[0505] Server: Passes the received user input data to the emotion recognition engine, which analyzes emotions from the user's input, voice, facial expressions, etc.

[0506] Input: User input data.

[0507] Output: Analysis result by the emotion recognition engine (e.g., user is tired).

[0508] Specific operation: The server passes the user's input data to the emotion recognition engine, which analyzes the emotion "tired" and returns the result.

[0509] Step 4:

[0510] Generate travel suggestions

[0511] Server: Generates travel recommendations using a generative AI model (e.g., GPT-4) based on the analysis results from the emotion recognition engine and the user's needs and preferences.

[0512] Input: Emotion recognition engine analysis results and user needs and preferences.

[0513] Output: Generated travel suggestions (e.g. "The resort we recommend for you is X resort with a quiet beach").

[0514] Specific operation: The server inputs a prompt to the generated AI model, such as "If the user is tired, please suggest a relaxing travel destination," and the model generates a suggestion such as "The resort recommended for you is XX resort with a quiet beach."

[0515] Step 5:

[0516] Providing suggestions

[0517] Server: Sends the generated travel proposals to the user terminal.

[0518] Input: Generated travel suggestions.

[0519] Output: Trip suggestions sent to the user device.

[0520] Specific operations: The server transmits the generated travel proposal to the user terminal via the Internet.

[0521] Step 6:

[0522] View Suggestions

[0523] Terminal: Displays received travel suggestions to the user.

[0524] Input: Travel proposals sent by the server.

[0525] Output: The trip suggestions displayed to the user.

[0526] Specific operation: The device will display suggestions on the screen, such as "The resort we recommend for you is XX Resort, which has a quiet beach."

[0527] The above is the specific processing flow of the program of this system.

[0528] (Application example 1)

[0529] Next, a description will be given of Application Example 1 of Embodiment 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."

[0530] Conventional personal travel assistant systems make suggestions based on the user's travel needs and preferences, but they are unable to take the user's emotions into account when making suggestions, which prevents them from fully increasing user satisfaction. Furthermore, they are unable to make suggestions to improve the shopping experience in physical stores, making it difficult to meet the diverse needs of users. Furthermore, since they do not utilize generative AI models to generate suggestions or prompts, the quality and accuracy of the suggestions is insufficient.

[0531] 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.

[0532] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist attractions, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for recognizing a user's emotions and adjusting suggestions based on the emotions, means for making suggestions to improve the shopping experience in physical stores, means for generating suggestions using a generative AI model, and means for generating prompt sentences and making suggestions. This enables optimal suggestions based on the user's emotions, thereby improving the shopping experience in physical stores. Furthermore, utilizing the generative AI model can improve the quality and accuracy of suggestions.

[0533] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to suggest optimal itineraries, tourist spots, and restaurants based on the user's travel needs and preferences.

[0534] "Means for inputting customer travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[0535] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to users based on their input needs and preferences.

[0536] "Means for providing real-time flight information, weather information, and local traffic information" is a function that provides users with current flight status, weather, and traffic information in real time.

[0537] The "means for recognizing the user's emotions and adjusting the suggestions based on those emotions" is a function for analyzing the user's emotions and changing the suggestions based on those emotions.

[0538] The "means for making suggestions to improve the shopping experience in physical stores" is a function that suggests products and services based on emotions and preferences so that users can enjoy shopping in physical stores more.

[0539] The "means for generating proposals using a generative AI model" is a function that utilizes a generative AI model to automatically generate proposals for users.

[0540] The "means for generating prompt sentences and making suggestions" is a function that generates appropriate prompt sentences based on the user's feelings and preferences and makes suggestions based on them.

[0541] The following system configuration will be described as an embodiment of the present invention.

[0542] System Configuration

[0543] This system includes a server, a user terminal, and various sensors and cameras. The server runs a program to provide an AI-powered personal travel assistant, and the user terminal is a device such as a smartphone or tablet. The sensors and cameras are used to recognize the user's emotions.

[0544] Program processing

[0545] The server performs the process using the following means.

[0546] 1. User needs and preferences input:

[0547] Through the user terminal, the user inputs their travel needs and preferences, for example, a specific preference such as "I want to relax at a resort near the beach."

[0548] 2. How to make the best offer:

[0549] Based on your needs and preferences, the server will suggest the best itineraries, attractions, and restaurants, taking into account real-time flight, weather, and local traffic information.

[0550] 3. Emotion recognition means:

[0551] Recognizes user emotions using sensors and cameras. For example, EmotionRecognizer analyzes images captured by a camera to determine whether the user is feeling happy or tired.

[0552] 4. Emotion-based suggestion adjustment measures:

[0553] The system tailors its recommendations based on the perceived emotion, for example suggesting relaxation spots if the user is feeling tired, or suggesting active tourist spots if the user is feeling happy.

[0554] 5. Ways to improve your shopping experience:

[0555] To improve the shopping experience in brick-and-mortar stores, we suggest products and services based on the user's emotions and preferences. For example, if the user prefers casual clothing, we suggest casual clothes.

[0556] 6. How to generate suggestions using generative AI models:

[0557] Use generative AI models to automatically generate suggestions for users, for example, by using the OpenAI API to generate prompts based on the user's emotions and preferences.

[0558] 7. Prompt generation method:

[0559] Based on the user's feelings and preferences, appropriate prompts are generated and suggestions are made. As a concrete example, the following prompts are generated:

[0560] Example prompt sentence:

[0561] Suggest product or service recommendations when the user is feeling joyful. The user prefers casual clothing.

[0562] Hardware and software used

[0563] Hardware: Camera (to capture the user's image)

[0564] Software: OpenAI API, EmotionRecognizer (emotion recognition library)

[0565] This will enable optimal suggestions based on user emotions, improving the shopping experience in physical stores. Furthermore, by utilizing generative AI models, it will be possible to improve the quality and accuracy of suggestions.

[0566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0567] Step 1:

[0568] The user uses the terminal to input their travel needs and preferences.

[0569] Input: User's travel wishes and preferences (e.g., "I want to relax at a resort near the beach")

[0570] Output: Data about user needs and preferences

[0571] Specific operation: Users enter their travel wishes and preferences in text format through an application on their smartphone or tablet.

[0572] Step 2:

[0573] The server will suggest the best itineraries, attractions, and restaurants based on your needs and preferences.

[0574] Input: Data about user needs and preferences

[0575] Output: A list of suggested itineraries, attractions, and restaurants

[0576] How it works: The server uses AI algorithms to analyze the input data and generate an optimal travel plan.

[0577] Step 3:

[0578] A user uses the device to capture an image of themselves with the camera.

[0579] Input: User image

[0580] Output: Captured image data

[0581] Specific actions: The user uses the camera function of their smartphone or tablet to take a picture of their face.

[0582] Step 4:

[0583] The server uses the captured image to recognize the user's emotions.

[0584] Input: Captured image data

[0585] Output: Recognized emotion data (e.g., happy, tired)

[0586] Specific operation: The server uses the EmotionRecognizer library to analyze image data and recognize the user's emotions.

[0587] Step 5:

[0588] The server adjusts the suggestions based on the perceived sentiment.

[0589] Input: Recognized emotion data, suggested itineraries, tourist attractions, and restaurant lists

[0590] Output: Adjusted proposal

[0591] Specific operation: The server changes the suggestions based on the recognized emotion data. For example, if the user feels tired, it adds relaxation facilities.

[0592] Step 6:

[0593] The server makes suggestions to improve the shopping experience in physical stores.

[0594] Input: Recognized emotion data, user preference data

[0595] Output: Shopping suggestions

[0596] Specific operation: Based on the user's emotions and preferences, the server suggests products and services suitable for shopping in a physical store.

[0597] Step 7:

[0598] The server uses the generative AI model to generate the proposals.

[0599] Input: Recognized emotion data, user preference data

[0600] Output: Generated proposals

[0601] How it works: The server uses the OpenAI API to generate prompts based on the user's emotions and preferences, and then makes suggestions based on those prompts.

[0602] Step 8:

[0603] The server generates a prompt and makes suggestions.

[0604] Input: Recognized emotion data, user preference data

[0605] Output: Prompt and suggestions

[0606] How it works: The server uses a generative AI model to generate prompts like the one below and makes suggestions based on them.

[0607] Example prompt sentence:

[0608] Suggest product or service recommendations when the user is feeling joyful. The user prefers casual clothing.

[0609] Example 2

[0610] Next, a description will be given of Example 2 of Form 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."

[0611] Conventional travel assistant systems often fail to provide recommendations based on user needs and preferences, and struggle to recognize emotions in real time and provide services accordingly. This results in low user satisfaction and often delays in resolving travel issues. Furthermore, they are unable to effectively utilize users' past choices and feedback, making it difficult to provide personalized recommendations.

[0612] 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.

[0613] In this invention, the server includes means for inputting a user's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time transportation information, weather information, and local traffic information, means for recognizing a user's emotions in real time and providing services according to the emotions, means for learning the user's preferences using a machine learning algorithm, and means for generating suggestions using a generative AI model. This enables personalized suggestions based on the user's needs and preferences, and realizes real-time emotion recognition and prompt provision of services accordingly.

[0614] "Means for inputting user's travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[0615] "Means of suggesting optimal itineraries, tourist spots, and restaurants based on input needs and preferences" refers to algorithms and systems that suggest optimal travel plans, tourist spots, and restaurants based on information input by the user.

[0616] "Means for providing real-time transportation information, weather information, and local traffic information" refers to a system that provides users with the latest information on current traffic conditions, weather, and local transportation.

[0617] "Means for recognizing a user's emotions in real time and providing services in accordance with those emotions" refers to a system that analyzes a user's emotional state in real time and provides appropriate services based on the results.

[0618] "Means for learning user preferences using machine learning algorithms" refers to algorithms for learning user preferences based on the user's past behavior and choices.

[0619] The "means for generating proposals using a generative AI model" is a system for automatically generating optimal proposals for users using AI technology.

[0620] MODE FOR CARRYING OUT THE INVENTION

[0621] This invention is a system that suggests optimal itineraries, tourist spots, and restaurants based on a user's travel needs and preferences. The system receives user input, generates suggestions using machine learning algorithms and generative AI models, and provides services by recognizing user emotions in real time.

[0622] Hardware and software used

[0623] The server uses machine learning libraries such as Python's Scikit-learn and TensorFlow to learn user preferences. It also uses Google® Cloud Natural Language API and IBM Watson® sentiment analysis tools to recognize user emotions in real time. The terminal provides a user interface such as a smartphone or PC and receives user input.

[0624] Data processing and calculation

[0625] The server receives data input from the user and uses machine learning algorithms to learn the user's preferences. For example, if a user inputs "I want to go to a beach resort on my next vacation," the server generates a list of beach resorts based on past data and suggests them to the user. Suggestions are automatically generated using a generative AI model.

[0626] Specific examples

[0627] A user opens an app on their smartphone and types, "I want to go to a beach resort on my next vacation." The device sends this input data to the server. The server analyzes the received input data and compares it with past data. For example, if the user previously input that they like "beach resorts" and "relaxing," the server uses this as learning data.

[0628] The server uses a generative AI model based on the training data to generate a list of beach resorts, such as "Waikiki Beach in Hawaii" and "Kuta Beach in Bali."

[0629] If a user types "my flight is delayed," the device analyzes this text in real time and uses an emotion engine to recognize the user's dissatisfaction. After recognizing the user's dissatisfaction, the server supports the user in suggesting alternative flights or extending their hotel stay. For example, it might suggest to the user, "The next flight departs in two hours. Would you like to extend your hotel stay?"

[0630] Prompt Sentence Examples

[0631] "I'd like to go to a beach resort on my next vacation. Can you recommend any places?"

[0632] "My flight is delayed. What should I do?"

[0633] In this way, the server and the terminal work together to provide the most suitable service according to the user's needs and emotions.

[0634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0635] Step 1:

[0636] The user uses the terminal to input their travel needs and preferences. For example, they may input "I want to go to a beach resort on my next vacation." The input data includes the user's travel wishes and preferences. The terminal then transmits the input data to the server.

[0637] Step 2:

[0638] The server analyzes the input data it receives. Specifically, it uses machine learning libraries such as Python's Scikit-learn and TensorFlow to learn the user's preferences. Based on the input data, it takes into account past choices and feedback and models the user's preferences. The output is the learned results of the user's preferences.

[0639] Step 3:

[0640] The server uses a generative AI model based on the learning results to generate optimal suggestions. For example, if the server learns that a user prefers beach resorts, it generates a list of resorts that are close to the beach. Specifically, it suggests resorts such as "Waikiki Beach in Hawaii" and "Kuta Beach in Bali." The output is a list of suggested resorts.

[0641] Step 4:

[0642] The terminal displays the suggestions received from the server to the user, who can then review the list of suggested resorts and make a selection. The input is the suggestion data from the server, and the output is the display to the user.

[0643] Step 5:

[0644] When a user reports a problem during a trip, for example, by typing "my flight is delayed," the device analyzes this text in real time and sends it to the server. The input is the user's problem report.

[0645] Step 6:

[0646] The server analyzes the received problem report and recognizes the user's emotion using an emotion engine. Specifically, it uses Google Cloud Natural Language API and IBM Watson's emotion analysis tool. The input is the user's problem report, and the output is the emotion recognition result.

[0647] Step 7:

[0648] The server provides appropriate services based on the results of emotion recognition. For example, if the user is dissatisfied, it can suggest alternative flights or extend the stay at the hotel. Specifically, it suggests to the user, "The next flight departs in two hours. Would you like to extend your stay at the hotel?" The input is the result of emotion recognition, and the output is a service suggestion.

[0649] Step 8:

[0650] The terminal displays the service proposals from the server to the user, who can then review and select the proposed services. The input is the service proposal from the server, and the output is the display to the user.

[0651] In this way, the server and the terminal work together to provide the most suitable service according to the user's needs and emotions.

[0652] (Application example 2)

[0653] Next, a description will be given of Application Example 2 of Form 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."

[0654] Conventional food delivery systems do not adequately provide optimal suggestions based on user preferences and needs. Furthermore, they lack a mechanism for quickly responding to user complaints or anger, resulting in a poor user experience.

[0655] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0656] In this invention, the server includes a means for providing an AI-equipped personal assistant, a means for inputting customer needs and preferences, a means for making optimal suggestions based on the input needs and preferences, a means for providing real-time information, a means for recognizing user emotions in real time, and a means for providing support in accordance with the user emotions. This makes it possible to make optimal suggestions based on the user's preferences and needs, and also to respond quickly when the user feels dissatisfied or anger.

[0657] An "AI-powered personal assistant" is a system that uses artificial intelligence to provide optimal suggestions and support based on the user's needs and preferences.

[0658] "Means for inputting customer needs and preferences" refers to the interface or method by which a user inputs their wishes and preferences into the system.

[0659] "Means for making optimal suggestions based on input needs and preferences" refers to algorithms or processes that present optimal options based on information input by the user.

[0660] A "real-time information provider" is a system or method for providing users with up-to-date information based on current circumstances and conditions.

[0661] "Means for recognizing a user's emotions in real time" refers to technology or devices that can instantly determine a user's emotions from their facial expressions, voice, etc.

[0662] The "means for providing support according to the user's emotions" refers to a system or method for providing appropriate responses or support based on the recognized user's emotions.

[0663] A system for carrying out this invention has the following configuration: A server includes means for providing an AI-equipped personal assistant, means for inputting customer needs and preferences, means for making optimal suggestions based on the input needs and preferences, means for providing real-time information, means for recognizing user emotions in real time, and means for providing support in accordance with the user emotions.

[0664] Hardware and software used

[0665] Hardware: Smartphone (camera, microphone)

[0666] Software: TensorFlow (machine learning model), OpenCV (image processing), Google Cloud Speech-to-Text API (voice recognition)

[0667] Data processing and calculation

[0668] 1. Learning user preferences:

[0669] The server collects past order data and uses TensorFlow to build a model that learns user preferences.

[0670] Example: If a user has frequently ordered "sushi" in the past, the next time suggestions are given, priority will be given to "sushi" restaurants.

[0671] 2. Best Suggestion:

[0672] The server uses the learned model to suggest restaurants and menus that best suit the user's current needs.

[0673] Example: Suggesting the best restaurant based on user preferences and current location.

[0674] 3. Emotion recognition:

[0675] The server uses OpenCV to process the image data acquired from the camera and analyze the user's facial expression.

[0676] The server uses the Google Cloud Speech-to-Text API to convert the audio data obtained from the microphone into text and analyze emotions.

[0677] Example: Recognizing emotions from a user's facial expressions using camera images as input.

[0678] 4. Problem-solving support:

[0679] Based on the emotion recognition results, the server automatically connects the user to customer support if they are feeling dissatisfied or angry.

[0680] Example: If the user's emotion is recognized as "anger," send a notification to customer support.

[0681] Prompt Sentence Examples

[0682] Learn user preferences: "Learn user preferences based on past order data."

[0683] Emotion Recognition: "Recognize user emotions in real time based on camera images and audio data."

[0684] In this way, the embodiment of the invention provides a system that can make optimal suggestions based on the user's preferences and needs, and can also respond quickly when the user feels dissatisfied or angered.

[0685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0686] Step 1:

[0687] Users input their needs and preferences using a terminal. The input data is sent to the server. The input data includes past order history and evaluation information.

[0688] Step 2:

[0689] The server uses TensorFlow to learn the user's preferences based on the received data. Specifically, it uses past order history and rating information as input to generate a model that characterizes the user's tastes. The output is a feature vector that reflects the user's preferences.

[0690] Step 3:

[0691] The server uses the generated feature vector to suggest restaurants and menus that best suit the user's current needs. Specifically, it runs an algorithm that selects the best restaurant using the user's current location information and feature vector as input. The output is a list of suggested restaurants and menus.

[0692] Step 4:

[0693] The user uses the device to review suggested restaurants and menus, and once the user makes a selection, the information is sent to the server.

[0694] Step 5:

[0695] The server processes the order based on the user's selection, sending the order to the selected restaurant and receiving order confirmation information. The output is order confirmation information.

[0696] Step 6:

[0697] The server provides real-time information, specifically notifying the user of the order progress and delivery time. The input is progress information from the restaurant, and the output is notification information to the user.

[0698] Step 7:

[0699] The user uses the device to express emotions through the camera and microphone. The server processes the camera images using OpenCV and converts the voice data into text using the Google Cloud Speech-to-Text API. The input is the camera images and voice data, and the output is the user's emotional information.

[0700] Step 8:

[0701] If the server determines that the user is feeling dissatisfied or angry based on the emotion recognition results, it automatically connects the user to customer support. Specifically, it takes emotion information as input and sends a notification to customer support. The output is notification information for customer support.

[0702] In this way, the system provides a system that can make optimal suggestions based on the user's preferences and needs through the specific actions performed at each step, and can also quickly respond when the user feels dissatisfied or angered.

[0703] Example 3

[0704] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0705] Conventional travel assistant systems do not adequately provide real-time information or personalized suggestions based on the user's emotions. As a result, users have difficulty obtaining prompt and appropriate information when changing their travel plans or selecting a new travel destination. Furthermore, they are unable to effectively utilize the user's past travel history and emotional data, which makes it difficult to improve user satisfaction.

[0706] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0707] In this invention, the server includes means for collecting real-time flight information, weather information, and local traffic information and storing them in a database, means for retrieving and displaying the latest information from the server when accessed by a customer, means for the customer to input travel feedback and transmit the data to the server, means for learning customer emotion data using a machine learning model and reflecting the data in next travel proposals, and means for generating and displaying personalized travel proposals based on the customer's past emotion data, thereby enabling users to quickly obtain real-time information and receive personalized proposals based on past emotion data.

[0708] "Real-time flight information" refers to information that provides the operational status of aircraft in real time.

[0709] "Weather information" is information that indicates the weather conditions in a specific area or location.

[0710] "Local traffic information" refers to information about traffic conditions and modes of transportation in a specific area.

[0711] A "database" is a system for systematically organizing and storing information.

[0712] A "server" is a computer system that provides data and services over a network.

[0713] A "terminal" is a device that is directly operated by a user and that communicates with a server to obtain information.

[0714] A "customer" is a user who uses the system to plan and obtain travel information.

[0715] "Feedback" refers to evaluations and opinions provided by users to the system.

[0716] A "machine learning model" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[0717] "Emotion data" is information relating to the user's emotions and thoughts.

[0718] "Personalized travel suggestions" are travel suggestions that are customized based on a user's individual preferences and historical data.

[0719] This invention is a system that provides real-time flight information, weather information, and local traffic information, and makes personalized travel suggestions based on the user's emotional data. Specific embodiments of this system are described below.

[0720] Real-time information system

[0721] The server uses external APIs (e.g., flight information API, weather information API, traffic information API) to collect real-time flight information, weather information, and local traffic information and stores it in the database, ensuring that the latest information is always kept in the database.

[0722] When a user accesses the app, the device retrieves the latest information from the server and displays it to the user. For example, if a user opens an app to plan a trip, the device sends a request to the server to retrieve the latest flight and weather information.

[0723] Users can check real-time information through the app and plan their trips. For example, if a flight is delayed, users can receive real-time information through the app and be offered alternative flights or transportation options.

[0724] For example, if a user opens the app and types "What are the next flight details?", the device will send a request to the server to get the latest flight information and display it to the user, like this:

[0725] "Flight AA123 is delayed by one hour. The alternative flight is BB456."

[0726] Travel recommendation system using emotion engine

[0727] The server collects user emotion data and uses machine learning models (e.g., TensorFlow, PyTorch) to learn emotion changes. This data is obtained from user feedback and behavioral history.

[0728] When a user inputs feedback about their trip, the device sends the data to the server. For example, if a user inputs feedback such as "I had fun" in the app after visiting a particular tourist spot, the data is sent to the server.

[0729] The next time a user requests travel suggestions, they will receive personalized suggestions based on their past emotional data. For example, if the system recognizes that a user felt joy after visiting a particular tourist spot, it will suggest tourist spots with similar characteristics.

[0730] For example, if a user inputs feedback after a trip such as "I really enjoyed this tourist spot," the device will send that data to the server. Next time, if the user inputs "Please suggest my next travel destination," the server will use the emotion engine to generate the following suggestions, which the device will display to the user:

[0731] "I recommend the following tourist spots that are similar to the ones I enjoyed on my last trip: Tourist Spot A, Tourist Spot B."

[0732] In this way, the system can provide real-time information and personalized travel suggestions utilizing the emotion engine. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0733] Real-time information system

[0734] Step 1: Gather information

[0735] The server uses external APIs (e.g., flight information API, weather information API, traffic information API) to collect real-time flight information, weather information, and local traffic information, which is periodically updated and stored in a database.

[0736] Input: Data request from external API

[0737] Output: The latest information stored in the database

[0738] What happens: The server sends an API request, for example:

[0739] GET https: / / api.flightinfo.com / flights

[0740] GET https: / / api.weatherinfo.com / weather

[0741] GET https: / / api.trafficinfo.com / traffic

[0742] The acquired data is stored in a database.

[0743] Step 2: Request Information

[0744] If a user opens the app and wants to check the next flight information, they type "What is the next flight information?" in the app.

[0745] Input: User prompt text

[0746] Output: Request from terminal to server

[0747] What happens: The user enters a prompt in the app.

[0748] Step 3: Information Acquisition

[0749] The terminal sends the user's request to the server, which retrieves the latest flight information from the database and returns it to the terminal.

[0750] Input: Request from terminal

[0751] Output: Latest information from the server

[0752] Specific operation: The device sends the following request to the server:

[0753] GET / flights?flightNumber=AA123

[0754] The server retrieves the information from the database and returns it to the terminal.

[0755] Step 4: Display information

[0756] The terminal displays the information received from the server to the user. For example, if the flight is delayed, the terminal displays the following information:

[0757] Input: Latest information from the server

[0758] Output: Displaying information to the user

[0759] What happens: The device displays the following information to the user:

[0760] "Flight AA123 is delayed by one hour. The alternative flight is BB456."

[0761] Travel recommendation system using emotion engine

[0762] Step 1: Collecting Emotional Data

[0763] After a trip, the user enters feedback in the app, for example, "I really enjoyed this tourist spot."

[0764] Input: User feedback

[0765] Output: Sending data from the device to the server

[0766] What happens: A user enters feedback in the app.

[0767] Step 2: Send data

[0768] The device sends the user's feedback to the server, which then sends this data to the emotion engine.

[0769] Input: User feedback

[0770] Output: Send data to the server

[0771] Specific operation: The device sends the following data to the server:

[0772] POST / feedback

[0773] {

[0774] "userId": "12345",

[0775] "feedback": "I really enjoyed this tourist spot"

[0776] }

[0777] Step 3: Emotional Learning

[0778] The server uses machine learning models (e.g., TensorFlow, PyTorch) to learn the user's emotional data, which allows it to understand changes in the user's emotions and reflect them in its next travel suggestions.

[0779] Input: User feedback data

[0780] Output: Learned emotion model

[0781] What happens: The server uses a machine learning model to learn the data.

[0782] Step 4: Travel proposal

[0783] When a user wants suggestions for their next trip, they type "Suggest my next trip" into the app.

[0784] Input: User prompt text

[0785] Output: Request from terminal to server

[0786] What happens: The user enters a prompt in the app.

[0787] Step 5: Proposal Generation

[0788] The server uses an emotion engine to generate personalized travel suggestions based on the user's past emotion data, for example, suggesting places similar to tourist spots enjoyed on a previous trip.

[0789] Input: User's past emotional data

[0790] Output: Personalized travel suggestions

[0791] What it does: The server uses the emotion engine to generate suggestions such as:

[0792] {

[0793] "recommendations": ["Tourist Spot A", "Tourist Spot B"]

[0794] }

[0795] Step 6: Display suggestions

[0796] The device displays the travel suggestions received from the server to the user, for example, suggestions such as:

[0797] Input: Travel suggestions from the server

[0798] Output: Proposal displayed to the user

[0799] What it does: The device displays the following suggestions to the user:

[0800] "I recommend the following tourist spots that are similar to the ones I enjoyed on my last trip: Tourist Spot A, Tourist Spot B."

[0801] In this way, the system can provide real-time information and personalized travel recommendations using an emotion engine.

[0802] (Application example 3)

[0803] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0804] Conventional food delivery systems were unable to predict delivery times that took into account real-time traffic and weather information, which led to lower customer satisfaction. Furthermore, they lacked the ability to learn customer sentiment and suggest preferred dishes and restaurants for the next order, making it difficult to provide personalized service. This made it difficult to improve customer repeat rates and satisfaction.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0806] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for predicting delivery times based on real-time traffic and weather information, and means for learning customer emotions and suggesting preferred dishes and restaurants for the next order. This enables accurate delivery time predictions based on real-time information and personalized suggestions based on customer emotions.

[0807] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to help customers plan their trips.

[0808] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel needs and preferences into the system.

[0809] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to customers based on their input needs and preferences.

[0810] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[0811] "Means for predicting delivery times based on real-time traffic and weather information" refers to a function that predicts food delivery times based on current traffic conditions and weather information.

[0812] "Means of learning customer emotions and suggesting preferred dishes and restaurants the next time they order" refers to a function that analyzes a customer's past order history and ratings, learns their emotions, and suggests dishes and restaurants that suit the customer's preferences the next time they order.

[0813] A system for carrying out this invention is configured as follows: The server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for predicting delivery times based on real-time traffic and weather information, and means for learning customer sentiment and suggesting preferred dishes and restaurants for the next order.

[0814] Hardware and software used

[0815] Hardware: Smartphone (iOS or ANDROID (registered trademark))

[0816] software:

[0817] Real-time data acquisition API (e.g., Google Maps API, OpenWeatherMap API)

[0818] Sentiment analysis engine (e.g., IBM Watson, Microsoft® Azure® Emotion API)

[0819] Databases (e.g., Firebase, MongoDB)

[0820] Data processing and calculation

[0821] Real-time data acquisition

[0822] The server uses the Google Maps API to get current traffic information and the OpenWeatherMap API to get current weather information, and then uses this information to predict delivery times.

[0823] sentiment analysis

[0824] The server inputs the user's past order history and reviews into a sentiment analysis engine to analyze the user's sentiment. If the user has positive feelings about a particular food or restaurant, the information is stored in a database.

[0825] Next proposal

[0826] The next time the user uses the app, the server will suggest their favorite dishes and restaurants based on their past emotional data.

[0827] Specific examples

[0828] If a user has previously ordered pizza and left a review saying it was delicious, the next time they open the app, a pizza suggestion will be displayed. The app also displays a delivery time forecast based on current traffic and weather information.

[0829] Prompt Sentence Examples

[0830] Below are some examples of prompt sentences:

[0831] python

[0832] Get traffic information using Google Maps API

[0833] import requests

[0834] def get_traffic_info(api_key, origin, destination):

[0835] url = f"https: / / maps.googleapis.com / maps / api / directions / json?origin={origin}&destination={destination}&key={api_key}"

[0836] response = requests.get(url)

[0837] traffic_data = response.json()

[0838] return traffic_data

[0839] Get weather information using the OpenWeatherMap API

[0840] def get_weather_info(api_key, location):

[0841] url = f"http: / / api.openweathermap.org / data / 2.5 / weather?q={location}&appid={api_key}"

[0842] response = requests.get(url)

[0843] weather_data = response.json()

[0844] return weather_data

[0845] Analyze user sentiment using a sentiment analysis engine

[0846] def analyze_emotion(text):

[0847] Here we use a hypothetical sentiment analysis function

[0848] if "delicious" in text:

[0849] return "positive"

[0850] else:

[0851] return "neutral"

[0852] Store emotion data in a database

[0853] def save_emotion_data(user_id, emotion_data):

[0854] Here we use a hypothetical database save function

[0855] pass

[0856] Generate next proposal

[0857] def generate_recommendation(user_id):

[0858] Here we use a tentative proposal generation function

[0859] return ["Pizza", "Sushi"]

[0860] Main Processing

[0861] def main():

[0862] api_key_maps = "YOUR_GOOGLE_MAPS_API_KEY"

[0863] api_key_weather = "YOUR_OPENWEATHERMAP_API_KEY"

[0864] origin = "user's current location"

[0865] destination = "restaurant location"

[0866] location = "user's current location"

[0867] traffic_info = get_traffic_info(api_key_maps, origin, destination)

[0868] weather_info = get_weather_info(api_key_weather, location)

[0869] user_review = "This pizza was delicious"

[0870] emotion = analyze_emotion(user_review)

[0871] save_emotion_data("user123", emotion)

[0872] recommendation = generate_recommendation("user123")

[0873] print(f"Next recommendation: {recommendation}")

[0874] print(f"Traffic information: {traffic_info}")

[0875] print(f"Weather information: {weather_info}")

[0876] if __name__ == "__main__":

[0877] main()

[0878] The program obtains real-time traffic and weather information, analyzes user sentiment, and generates next-generation recommendations. The specific API key and database implementation need to be configured according to the actual environment.

[0879] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0880] Step 1:

[0881] The server gets traffic information using the Google Maps API. Specifically, it sends a request to the Google Maps API with the user's current location (origin) and the restaurant's location (destination) as input. It receives the traffic information (traffic_data) returned by the API and stores it for use in the next processing step.

[0882] Step 2:

[0883] The server retrieves weather information using the OpenWeatherMap API. Specifically, it sends a request to the OpenWeatherMap API with the user's current location as input. It receives the weather information (weather_data) returned by the API and stores it for use in the next processing step.

[0884] Step 3:

[0885] The server inputs the user's past order history and reviews into a sentiment analysis engine to analyze the user's emotions. Specifically, it sends the user's review (user_review) as input to the sentiment analysis engine. It receives the emotion data (emotion) returned by the sentiment analysis engine and stores it in a database.

[0886] Step 4:

[0887] The server generates the next recommendation based on the emotion data stored in the database. Specifically, it inputs the user ID (user_id), retrieves the emotion data from the database, and generates a list (recommendation) of dishes and restaurants that match the user's preferences.

[0888] Step 5:

[0889] The server displays the next recommendation to the user. Specifically, it sends the generated recommendation list (recommendation) and the acquired traffic information (traffic_info) and weather information (weather_info) to the user's device, and displays this information when the user opens the app.

[0890] Step 6:

[0891] The user places their next order based on the suggestion list, traffic information, and weather information provided by the server. Specifically, the user selects the suggested dishes and restaurants and confirms the order. The server receives the user's selection and stores it in a database as the next order history.

[0892] 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.

[0893] 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> ) 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.

[0894] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0895] 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.

[0896] [Second embodiment]

[0897] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0898] 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.

[0899] 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).

[0900] 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.

[0901] 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.

[0902] 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).

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0909] "Example 1"

[0910] One example of the present invention is a system that provides an AI-powered personal travel assistant. This system accepts input of travel needs and preferences from customers through a dedicated application or chatbot. For example, a customer can input a preference such as "I want to relax at a resort near the beach."

[0911] "Example 2"

[0912] Based on the customer's needs and preferences, the system will suggest the best itineraries, tourist attractions, and restaurants. These suggestions are made using AI machine learning to learn the customer's preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[0913] "Example 3"

[0914] In addition, the system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently. For example, if a flight is delayed, the system can provide that information to customers in real time and suggest alternative flights or transportation options.

[0915] The processing flow of each embodiment will be described below.

[0916] "Example 1"

[0917] Step 1: Customers input their travel needs and preferences through a dedicated application or chatbot, for example, "I want to relax at a resort near the beach."

[0918] Step 2: The system analyzes your needs and preferences and makes recommendations based on them, including the best itinerary, attractions, and restaurants.

[0919] Step 3: The proposal is communicated to the customer, who reviews the proposal and adjusts the itinerary as necessary.

[0920] "Example 2"

[0921] Step 1: The system uses AI machine learning to learn customer preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[0922] Step 2: The learned results are stored in a database and used the next time a recommendation is made. Step 3: When the customer enters new travel needs or preferences, the system combines them with the learned results to make more accurate recommendations.

[0923] "Example 3"

[0924] Step 1: The system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently.

[0925] Step 2: For example, if a flight is delayed, the system provides that information to the customer in real time.

[0926] Step 3: Additionally, the system can suggest alternative flights and transportation options, allowing customers to quickly adjust their travel plans.

[0927] Example 1

[0928] Next, a description will be given of Example 1 of Form 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."

[0929] Conventional travel planning systems have difficulty providing optimal travel plans that meet the individual needs and preferences of users. They also cannot respond to requests for real-time revisions or additions, which makes it difficult to increase user satisfaction. Furthermore, since proposals do not take into account past travel history or rating information, they are unable to provide plans that perfectly match the user's preferences.

[0930] 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.

[0931] In this invention, the server includes: a means for a user to input travel needs and preferences; a means for the terminal to send the input data to the server; a means for the server to input a prompt sentence to the generative AI model; a means for the generative AI model to generate a travel plan; a means for the server to send the generated travel plan to the terminal; a means for the user to check the travel plan and input corrections or additions as necessary; a means for the terminal to send the correction data to the server; a means for the server to again input a prompt sentence to the generative AI model to generate an updated plan; and a means for the server to send the updated travel plan to the terminal. This makes it possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction.

[0932] "User" means an individual or entity that inputs travel needs and preferences and reviews and modifies proposed travel plans.

[0933] "Terminal" refers to an electronic device that allows a user to input their travel needs and preferences and communicate with the server. Examples of such devices include smartphones, tablets, and PCs.

[0934] The "server" is a central processing unit that receives data sent by the user, inputs prompt sentences into the generative AI model, and sends the generated travel plan to the user's terminal.

[0935] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates optimal travel plans. Specifically, it includes models that use machine learning algorithms.

[0936] A "prompt" is text data that is input into the generative AI model and contains information such as the user's needs, preferences, budget, and travel duration.

[0937] A "trip plan" is a travel suggestion, including accommodation, attractions, and dining options, generated by a generative AI model based on the user's needs and preferences.

[0938] "Correction data" refers to data that includes additional requests or changes that a user inputs to a proposed travel plan.

[0939] "Real-time" refers to the generative AI model's ability to respond immediately to user input and modifications, quickly generating new travel plans.

[0940] This invention provides an AI-powered personal travel assistant system that allows users to input their travel needs and preferences and generate an optimal travel plan based on the input. Specific embodiments of this system are described below.

[0941] First, a user uses a dedicated application or chatbot to input their travel needs and preferences, such as a specific request such as "I want to relax at a resort near the beach." They also input a budget of "under 100,000 yen," a trip duration of "three days," and a departure point of "Tokyo."

[0942] Next, the device sends the data entered by the user to the server in real time. The server inputs a prompt into the generative AI model based on the received data. The prompt specifically describes the user's requests and conditions. For example, the following prompts can be generated:

[0943] User Request: Relax at a resort near the beach

[0944] Budget: Under 100,000 yen

[0945] Trip duration: 3 days

[0946] Departure point: Tokyo

[0947] The generative AI model analyzes the input prompt and generates a travel plan that best suits the user's requests. Specifically, this includes models that use machine learning algorithms. For example, a generative AI model such as OpenAI's GPT-4 is used. This model takes into account the user's preferences, budget, weather information at the travel destination, and other factors to create a plan that includes accommodation, tourist attractions, and dining options.

[0948] The generated travel plan is sent from the server to the user's device. The user can review the plan through the device and input any corrections or additions they need. For example, they could input a request such as "I'd like to increase my budget a little and stay at a more luxurious accommodation."

[0949] The device sends the revised data entered by the user to the server, which then inputs prompts into the generative AI model based on the revised data to generate an updated travel plan. For example, the budget could be increased to 150,000 yen, and a plan including more luxurious accommodations would be generated.

[0950] The updated travel plan is then sent from the server to the user's terminal, where the user can review the plan and make a final decision.

[0951] In this way, it is possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction. The system can also take into account the user's past travel history and rating information when making suggestions, thereby providing more personalized travel plans.

[0952] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] Users input their travel needs and preferences.

[0955] Specifically, a user uses a dedicated application or chatbot to input a request such as "I want to relax at a resort near the beach," and also inputs a budget of "under 100,000 yen," a travel period of "3 days," and a departure point of "Tokyo."

[0956] Input: User's travel needs and preferences, budget, travel duration, departure point

[0957] Output: Input data

[0958] Step 2:

[0959] The terminal sends the input data to the server.

[0960] Specifically, the terminal transmits the data input by the user to the server in real time.

[0961] Input: Data entered by the user

[0962] Output: Data sent to the server

[0963] Step 3:

[0964] The server inputs a prompt sentence into the generative AI model.

[0965] Specifically, the server generates a prompt based on the received data:

[0966] User Request: Relax at a resort near the beach

[0967] Budget: Under 100,000 yen

[0968] Trip duration: 3 days

[0969] Departure point: Tokyo

[0970] This prompt sentence is input into the generative AI model.

[0971] Input: Data sent to the server

[0972] Output: The prompt sentence that is input to the generative AI model

[0973] Step 4:

[0974] A generative AI model generates a travel plan.

[0975] Specifically, the generative AI model analyzes the prompt and generates a travel plan that best suits the user's needs, including accommodation, tourist attractions, and dining options.

[0976] Input: prompt statement

[0977] Output: Generated itinerary

[0978] Step 5:

[0979] The server transmits the generated travel plan to the terminal.

[0980] Specifically, the server sends the generated travel plan to the user's terminal, where the user can check the plan.

[0981] Input: Generated itinerary

[0982] Output: Trip plan sent to the device

[0983] Step 6:

[0984] The user checks the travel plan and enters any corrections or additions as necessary.

[0985] Specifically, the user checks the proposed plan and inputs any modifications or additional requests, such as "I'd like to increase my budget a little and get a more luxurious accommodation."

[0986] Input: Travel plan sent to the device

[0987] Output: Corrections and additions requested

[0988] Step 7:

[0989] The terminal transmits the correction data to the server.

[0990] As a specific operation, the terminal transmits the correction data input by the user to the server.

[0991] Input: Corrections and additions

[0992] Output: Corrected data sent to the server

[0993] Step 8:

[0994] The server again inputs the prompt sentence into the generative AI model and generates an updated plan.

[0995] Specifically, the server inputs the prompt sentences into the generative AI model again based on the revised data, and generates an updated travel plan, for example, increasing the budget to 150,000 yen and including more luxurious accommodations.

[0996] Input: Correction data

[0997] Output: Updated itinerary

[0998] Step 9:

[0999] The server sends the updated travel plan to the terminal.

[1000] Specifically, the server sends the updated travel plan to the terminal, where the user can review the plan again and make a final decision.

[1001] Input: Updated travel plans

[1002] Output: Updated itinerary sent to device

[1003] (Application example 1)

[1004] Next, a description will be given of Application Example 1 of Form 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."

[1005] Conventional travel planning systems have difficulty providing optimal travel plans based on customer needs and preferences. They also lack real-time information and suggestions that take into account customers' past travel history, making it difficult to increase customer satisfaction. Furthermore, they lacked the ability to automatically generate and customize travel plans using generative AI models, making it difficult to provide a user-friendly system for customers.

[1006] 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.

[1007] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on the customer's input using a generative AI model, and means for displaying the generated travel plan to the customer so that the customer can select and customize it. This makes it possible to provide optimal travel plans that meet the customer's needs, thereby increasing customer satisfaction.

[1008] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to provide optimal travel plans for individual customers.

[1009] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[1010] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel schedules, tourist spots, and restaurants based on customer input.

[1011] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that provides current flight status, weather, and local traffic conditions in real time.

[1012] A "generative AI model" is an artificial intelligence model that automatically generates travel plans based on customer input.

[1013] "Means for generating travel plans" refers to a function that uses a generative AI model to create travel plans based on customer needs.

[1014] "Means for displaying travel plans to customers and allowing them to select and customize" refers to the function of presenting generated travel plans to customers and allowing them to select or change those plans.

[1015] The following system configuration is adopted as an embodiment of this invention: The server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on customer input using a generative AI model, and means for displaying the generated travel plan to the customer, allowing them to select and customize it.

[1016] Hardware and software used

[1017] Hardware: Smartphone

[1018] Software: Python, OpenAI API

[1019] Data processing and calculation

[1020] 1. User Input: Users input their travel needs and preferences through a smartphone application, such as a specific preference such as "I want to relax at a resort near the beach."

[1021] 2. Use of generative AI model: The server sends the user's input to the generative AI model (OpenAI's GPT-3) as a prompt. An example of a prompt sentence is as follows:

[1022] User travel needs and preferences: Relax at a resort near the beach

[1023] Please suggest a recommended travel plan.

[1024] 3. Itinerary Generation: The generative AI model generates an itinerary based on the prompts, including accommodations, attractions, restaurants, activities, etc.

[1025] 4. Display and customize itinerary: The server displays the generated itinerary to the user and allows the user to select and customize the itinerary. For example, the user can select "Beachfront Resorts in Maui" and add "Sunset Cruise."

[1026] Specific examples

[1027] If a user inputs, "I want to relax at a resort near the beach," the generative AI model will suggest the following itinerary:

[1028] Recommended travel plans:

[1029] 1. Accommodation: Maui Beachfront Resorts

[1030] 2. Sightseeing spot: Snorkeling at Hanauma Bay

[1031] 3. Restaurant: Beachside seafood restaurant

[1032] 4. Activity: Sunset Cruise

[1033] In this way, it becomes possible to provide the user with an optimal travel plan that meets their needs, thereby increasing customer satisfaction.

[1034] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1035] Step 1:

[1036] The user launches the smartphone application and inputs their travel needs and preferences.

[1037] Input: User's travel needs and preferences (e.g., "I want to relax at a resort near the beach")

[1038] Output: User input data

[1039] Specific operation: The user enters the desired travel conditions in text format into the application's input form and presses the submit button.

[1040] Step 2:

[1041] The terminal transmits the user's input data to the server.

[1042] Input: User-entered data

[1043] Output: User input data sent to the server

[1044] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[1045] Step 3:

[1046] The server receives the user's input data and sends it to the generative AI model as prompts.

[1047] Input: User input data sent to the server

[1048] Output: The prompt sent to the generative AI model

[1049] Specific operation: The server converts the user's input data into a prompt sentence and sends it to the generative AI model using the OpenAI API. An example of a prompt sentence is as follows:

[1050] User travel needs and preferences: Relax at a resort near the beach

[1051] Please suggest a recommended travel plan.

[1052] Step 4:

[1053] A generative AI model generates a travel plan based on the prompt.

[1054] Input: The prompt sent to the generative AI model

[1055] Output: Generated itinerary

[1056] How it works: The generative AI model analyzes the prompt and generates an appropriate itinerary, including accommodations, tourist attractions, restaurants, activities, etc.

[1057] Step 5:

[1058] The server receives the generated itinerary and displays it to the user.

[1059] Input: Generated itinerary

[1060] Output: The itinerary displayed to the user

[1061] Specific operation: The server sends the travel plan received from the generative AI model to the user's device and displays it on the application.

[1062] Step 6:

[1063] Users select and customize the itinerary displayed.

[1064] Input: A travel plan displayed to the user

[1065] Output: User selected and customized travel plan

[1066] What it does: The user selects the itinerary they want from the displayed options and customizes it as needed, for example, adding specific activities or changing accommodations.

[1067] Step 7:

[1068] The device sends the user's selected and customized travel plan to the server.

[1069] Input: User-selected and customized travel plans

[1070] Output: The customized itinerary sent to the server.

[1071] Specific operation: The device sends the travel plan selected and customized by the user to the server as an HTTP request.

[1072] Step 8:

[1073] The server saves the final itinerary and sends a confirmation message to the user.

[1074] Input: Customized itinerary sent to the server

[1075] Output: A confirmation message sent to the user

[1076] What happens: The server saves the customized itinerary to a database and sends the user a confirmation message that includes the final itinerary details.

[1077] Example 2

[1078] Next, a description will be given of Example 2 of Form 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."

[1079] Conventional travel planning systems have difficulty making optimal suggestions based on the user's individual needs and preferences. Furthermore, they are unable to provide real-time information on transportation options, weather, and local traffic information in an integrated manner, which makes it difficult to improve the efficiency and satisfaction of users' travel plans.

[1080] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting a user's travel needs and preferences, a means for proposing optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, a means for executing a machine learning model to generate the proposals, a means for transmitting the proposals to the user's terminal, a means for displaying the proposals on the user's terminal, and a means for providing real-time information on transportation means, weather information, and local traffic information. This makes it possible to make optimal proposals based on the user's individual needs and preferences, thereby improving the efficiency and satisfaction of travel planning.

[1081] "User" means an individual or entity that utilizes the System to input travel needs and preferences and receive suggestions.

[1082] "Travel needs and preferences" refers to the conditions and preferences a user has for travel, and specifically includes the places they want to go, the types of accommodations they like, and their food preferences.

[1083] An "itinerary" refers to a travel plan or schedule, including details of places to visit, transportation, accommodation, etc.

[1084] "Tourist attractions" refer to places and sights that are worth visiting at a travel destination, including historical buildings, natural landscapes, theme parks, etc.

[1085] "Eating establishment" refers to a place where a user visits to eat, and includes restaurants, cafes, bars, etc.

[1086] "Suggestions" refers to lists of itineraries, attractions, and dining options generated by the system based on the user's needs and preferences.

[1087] A "machine learning model" refers to an algorithm that learns patterns based on data and makes predictions and classifications.

[1088] "Terminal" refers to the device used by a user to access the system, and specifically includes a PC, smartphone, tablet, etc.

[1089] "Real-time transportation information" refers to up-to-date information about current traffic conditions and schedules.

[1090] "Weather information" refers to the latest information about the weather at your travel destination.

[1091] "Local traffic information" refers to up-to-date information about public transport and road conditions at your travel destination.

[1092] This invention is a system that suggests optimal itineraries, tourist spots, and restaurants based on a user's travel needs and preferences. The system receives user input, uses machine learning models to generate suggestions, and displays them on the user's device.

[1093] Hardware and software used

[1094] Hardware: Servers, devices (PCs, smartphones, tablets)

[1095] Software: Machine learning models (e.g., TensorFlow, PyTorch), databases (e.g., MySQL, PostgreSQL), web servers (e.g., Apache, Nginx)

[1096] Explanation of program processing

[1097] 1. Accepting user input

[1098] The user uses the device to input their travel needs and preferences, for example, "I want to go to a beach resort."

[1099] 2. Data transmission

[1100] The terminal sends the entered data to the server as an HTTP request.

[1101] 3. Data storage

[1102] The server stores the received data in a database, which also stores the user's previous input data.

[1103] 4. Running the machine learning model

[1104] The server runs a machine learning model on the stored data, which learns the user's preferences and generates optimal suggestions.

[1105] 5. Proposal Generation

[1106] The server then generates a list of itineraries, tourist spots, and restaurants that are optimal for the user based on the learning results. For example, if a user prefers beach resorts, it will suggest resorts that are close to the beach.

[1107] 6. Submit your proposal

[1108] The server sends the generated proposal to the device, and the data is sent as an HTTP response.

[1109] 7. Display of Suggestions

[1110] The terminal displays the received suggestions to the user, who can then review the suggested list and make a selection.

[1111] Specific examples

[1112] Examples:

[1113] The user enters, "I want to go to a beach resort."

[1114] The server receives this information and stores it in a database.

[1115] A machine learning model learns user preferences and generates beach resort suggestions.

[1116] The server sends suggestions such as "Waikiki Beach Resort in Hawaii" or "Nusa Dua Beach Resort in Bali" to the device.

[1117] The terminal displays these suggestions to the user.

[1118] Example prompt sentence:

[1119] User input: "I want to go to a beach resort"

[1120] Server processing: Learn user preferences and generate beach resort suggestions

[1121] Example suggestions: "Waikiki Beach Resort in Hawaii", "Nusa Dua Beach Resort in Bali"

[1122] In this way, the system can make optimal suggestions based on the user's needs and preferences.

[1123] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1124] Step 1:

[1125] A user uses a terminal to input travel needs and preferences. For example, the user inputs "I want to go to a beach resort." The input data is text data that specifically indicates the user's travel needs and preferences.

[1126] Step 2:

[1127] The device sends the entered data to the server as an HTTP POST request. The input is text data indicating the user's needs and preferences, and the output is the request data sent to the server.

[1128] Step 3:

[1129] The server stores the received data in a database, which also stores the user's previous input data. The input is the text data received as an HTTP request, and the output is the data stored in the database.

[1130] Step 4:

[1131] The server runs a machine learning model based on the stored data. The model learns the user's preferences and generates optimal suggestions. The input is the user's needs and preferences stored in the database, and the output is the suggestion data that is the result of learning. Specifically, the server uses TensorFlow to run the model and learn the user's preferences.

[1132] Step 5:

[1133] Based on the learning results, the server generates a list of itineraries, tourist spots, and restaurants that are optimal for the user. The input is the learning results of the machine learning model, and the output is a list of suggestions. Specifically, the server generates suggestions such as "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali."

[1134] Step 6:

[1135] The server sends the generated proposals to the device. The data is sent as an HTTP response. The input is the proposal list, and the output is the response data sent to the device.

[1136] Step 7:

[1137] The device displays the received suggestions to the user. The user can check the suggested list and select one. The input is the response data received from the server, and the output is the suggested list displayed to the user. Specifically, the device displays information about "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali" on the app screen.

[1138] (Application example 2)

[1139] Next, a description will be given of Application Example 2 of Form 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."

[1140] Conventional personal travel assistant systems can suggest itineraries, tourist spots, and restaurants based on a customer's travel needs and preferences, but they have the problem of not being able to suggest restaurants or dishes that take into account a customer's food preferences or past ordering history.In addition to providing real-time flight information, weather information, and local traffic information, there is a demand for more personalized services by making suggestions based on a customer's food preferences.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1142] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, and means for learning the customer's food preferences and past order history and suggesting optimal restaurants and dishes, thereby making it possible to further personalize the customer's travel experience and suggest optimal restaurants and dishes based on the customer's food preferences.

[1143] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to suggest optimal itineraries, sightseeing spots, and restaurants based on each customer's travel needs and preferences.

[1144] "Means for inputting customer travel needs and preferences" means any interface or device through which a customer inputs their travel wishes and preferences into the system.

[1145] "Means of suggesting optimal itineraries, tourist spots, and restaurants based on input needs and preferences" refers to algorithms and software that suggest optimal travel plans, tourist spots, and places to eat based on information input by customers.

[1146] "Means of providing real-time flight information, weather information, and local traffic information" refers to systems and devices that obtain information such as current flight status, weather, and traffic conditions in real time and provide it to customers.

[1147] "Means of learning customers' food preferences and past order history and suggesting the most suitable restaurant and cuisine" refers to algorithms and software that use machine learning to analyze customers' food preferences and past order history, and then suggest the most suitable restaurant and cuisine based on that.

[1148] As an embodiment of the present invention, the following system can be constructed.

[1149] First, the server runs software to provide an AI-powered personal travel assistant. The server provides an interface for customers to input their travel needs and preferences. This interface is typically implemented as a smartphone application or a web application.

[1150] The server then runs an algorithm to suggest optimal itineraries, attractions, and restaurants based on the input needs and preferences. The algorithm uses machine learning to learn the customer's preferences and makes suggestions based on them. For example, if the server learns that the customer prefers beach resorts, it will suggest resorts near the beach.

[1151] Additionally, the server accesses databases and APIs that provide real-time flight, weather, and local traffic information, and provides this information to customers, allowing them to plan their trips based on the most up-to-date information.

[1152] The server also runs an algorithm that learns the customer's food preferences and past order history to suggest the most suitable restaurant and cuisine. This algorithm takes into account the customer's past order history and rating information to suggest restaurants and cuisines that best suit the customer's preferences. For example, if a customer inputs that they like "sushi," "Italian," and "spicy food," the server will suggest the best restaurant based on these preferences.

[1153] As a concrete example, by inputting the following prompt sentence into a generative AI model, optimal restaurant suggestions based on the customer's preferences can be obtained.

[1154] Example prompt sentence:

[1155] User Preferences: ["Sushi", "Italian", "Spicy Food"]

[1156] Restaurant Data:

[1157] {"name": "Sushi Taro", "description": "A restaurant serving fresh sushi"},

[1158] {"name": "Italian Mario", "description": "A restaurant where you can enjoy authentic Italian cuisine"},

[1159] {"name": "Spicy food specialty restaurant", "description": "A restaurant that specializes in spicy food"},

[1160] {"name": "Japanese restaurant", "description": "Restaurant serving traditional Japanese cuisine"},

[1161] {"name": "French restaurant", "description": "A restaurant where you can enjoy high-class French cuisine"}

[1162] ]

[1163] In this way, servers can better personalize a customer's travel experience and make optimal restaurant and cuisine recommendations based on the customer's food preferences.

[1164] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1165] Step 1:

[1166] Users input their travel needs and preferences through a smartphone or web application.

[1167] Input: User's travel needs and preferences (e.g., "beach resort," "sushi," "Italian")

[1168] Output: Data on input needs and preferences

[1169] Step 2:

[1170] The server receives the user's input data and inputs it into the machine learning model.

[1171] Input: Data about user needs and preferences

[1172] Output: The data input into the machine learning model

[1173] Step 3:

[1174] The server uses machine learning models to learn user preferences.

[1175] Input: Data about user needs and preferences

[1176] Output: A feature vector based on user preferences

[1177] Step 4:

[1178] The server generates optimal itineraries, sightseeing spots, and restaurant suggestions based on the user's preferences.

[1179] Input: A feature vector based on user preferences

[1180] Output: A list of recommended itineraries, attractions, and restaurants

[1181] Step 5:

[1182] The server retrieves real-time flight information, weather information, and local traffic information and provides it to the user.

[1183] Input: Real-time information obtained from an external API

[1184] Output: Latest flight, weather, and traffic information provided to the user

[1185] Step 6:

[1186] The server learns the user's food preferences and past ordering history and suggests the most suitable restaurant and dish.

[1187] Input: User's food preferences, past order history

[1188] Output: A list of recommendations for the best restaurants and dishes

[1189] Step 7:

[1190] The server inputs prompts into the generative AI model to get optimal restaurant suggestions based on the user's preferences.

[1191] Input: Prompt text (e.g., "User preferences: ["Sushi", "Italian", "Spicy food"]")

[1192] Output: Restaurant suggestions from a generative AI model

[1193] Step 8:

[1194] The server provides the final proposal to the user, allowing the user to make a selection.

[1195] Input: A list of the best itineraries, attractions, and restaurants

[1196] Output: The final list of suggestions provided to the user

[1197] Example 3

[1198] Next, a description will be given of Example 3 of Form Example 3. 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."

[1199] Traditional travel assistance systems lacked the ability to provide real-time information, making it difficult to quickly respond to problems customers faced during their trip. They also lacked the ability to provide appropriate alternatives for unexpected situations such as flight delays, weather changes, and traffic congestion. This meant customers had to spend a great deal of time and effort changing their travel plans.

[1200] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1201] In this invention, the server includes a means for providing real-time flight information, weather information, and local traffic information, a means for integrating and analyzing the flight information, weather information, and traffic information, and a means for transmitting the analysis results to the customer's terminal, thereby enabling the customer to receive the latest information in real time and take appropriate action promptly.

[1202] An "AI-powered personal travel assistant" is a system that uses artificial intelligence technology to assist customers in planning their trips.

[1203] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[1204] "A means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, places to visit, and places to eat based on the customer's input needs and preferences.

[1205] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[1206] The "means for integrating and analyzing the flight information, weather information, and traffic information" is a function that brings together and analyzes the various types of information acquired to generate information that is useful to customers.

[1207] "Means for transmitting analysis results to the customer's terminal" is a function for transmitting analyzed information to the device used by the customer.

[1208] "Means for displaying information on the customer's terminal" refers to a function that visually displays the transmitted information on the customer's device.

[1209] "Means for customers to check information and take necessary actions" refers to an interface that allows customers to select and carry out necessary actions based on the displayed information.

[1210] This invention provides an AI-powered personal travel assistant system, which includes a means for inputting a customer's travel needs and preferences, a means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, and a means for providing real-time flight information, weather information, and local traffic information.

[1211] The server uses multiple APIs to collect real-time flight information, weather information, and local traffic information. Specifically, it uses the "Flight Information API" to obtain flight information, the "Weather Data API" to obtain weather information, and the "Traffic Information API" to obtain traffic information. By integrating and analyzing the data obtained from these APIs, it generates information that is useful to customers.

[1212] The server then sends the analysis results to the customer's device, including flight delay information, local weather information, traffic information, etc. This allows the customer to receive the latest information in real time.

[1213] The terminal displays the information received from the server to the user, such as flight delays, local weather, traffic congestion, etc. The terminal provides the information in a visually easy-to-understand format so that the user can easily check the information.

[1214] The user checks the information displayed on the terminal and takes the necessary action. For example, if a flight is delayed, the user can book an alternative flight or consider other means of transportation. The system also has the function of suggesting alternatives to the user.

[1215] As a concrete example, suppose a user has booked a flight from Tokyo to New York. If the flight is delayed, the server obtains the delay information from the "Flight Information API" and notifies the user's device. The server then obtains weather information for New York from the "Weather Data API" and provides it to the user. Finally, the server obtains local traffic information from the "Traffic Information API" and suggests alternative transportation options to the user.

[1216] Examples of prompts to be input to a generative AI model include:

[1217] Prompt: "If a flight from Tokyo to New York is delayed, suggest alternative flights or transportation."

[1218] By inputting this prompt sentence into the generative AI model, the system can provide the user with an appropriate alternative. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1219] Step 1:

[1220] The server collects real-time data.

[1221] Input: Data from flight information API, weather data API, and traffic information API.

[1222] Specific operation: The server calls each API every minute to obtain the latest flight, weather, and traffic information. For example, it obtains flight delay information from the flight information API, weather information for the destination from the weather data API, and local traffic conditions from the traffic information API.

[1223] Output: Real-time data obtained.

[1224] Step 2:

[1225] The server consolidates and analyzes the data.

[1226] Input: Real-time data obtained in step 1.

[1227] How it works: The server integrates the acquired data into a single database. For example, if a flight is delayed, the server compares that information with weather and traffic data to analyze the cause and impact of the delay.

[1228] Output: Consolidated and analyzed data.

[1229] Step 3:

[1230] The server sends the analysis results to the device.

[1231] Input: Data integrated and analyzed in Step 2.

[1232] Specific operation: The server sends the analysis results in JSON format to the terminal. For example, if a flight is delayed, JSON data containing information about the delay time, cause, and alternative flight information is sent to the terminal.

[1233] Output: Analysis results sent to the device.

[1234] Step 4:

[1235] The terminal displays the information to the user.

[1236] Input: Analysis results submitted in step 3.

[1237] Specific operation: The device parses the received JSON data and displays it to the user in a visually understandable format, for example, highlighting flight delay information in red and displaying information about alternative flights in a list format.

[1238] Output: Information displayed to the user.

[1239] Step 5:

[1240] The user reviews the information and takes the necessary action.

[1241] Input: The information displayed in step 4.

[1242] Specific operation: The user checks the information displayed on the terminal and books an alternative flight if necessary. For example, if the flight is delayed by two hours, the user can select the alternative flight suggested by the system and proceed with the booking process.

[1243] Output: The action taken by the user.

[1244] (Application example 3)

[1245] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1246] Conventional personal travel assistant systems provide real-time flight, weather, and local traffic information to assist customers in planning their trips, but there is a need for delivery services to utilize similar real-time information to improve delivery efficiency and avoid delays. In particular, food delivery services, which are easily affected by traffic congestion and bad weather, require optimal route suggestions based on real-time information and alternative routes in the event of delays. This will increase delivery efficiency and customer satisfaction.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1248] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for suggesting optimal routes based on real-time traffic and weather information, and means for suggesting alternative routes or handing over to another delivery person in the event of a delivery delay. This enables efficient route suggestions using real-time information and rapid response to delays in delivery work.

[1249] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to help customers plan their trips.

[1250] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[1251] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to customers based on their input needs and preferences.

[1252] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[1253] "A means of suggesting the optimal route based on real-time traffic and weather information" is a function that suggests the optimal delivery route to delivery personnel, taking into account current traffic conditions and weather.

[1254] "Means to suggest alternative routes or handover to other delivery personnel in the event of a delivery delay" is a function that suggests alternative routes or handover to other delivery personnel in the event of a delivery delay.

[1255] The following system configuration will be described as an embodiment of the present invention.

[1256] System Configuration

[1257] The system consists of three main components: a server, a terminal, and a user. The server obtains real-time flight, weather, and local traffic information and calculates the optimal route based on this information. The terminal is a device such as a smartphone or tablet that the user uses to access the system. The user inputs their travel needs and preferences and receives suggestions from the system.

[1258] Program processing

[1259] The server processes the data in the following steps:

[1260] 1. Data Acquisition: The server acquires real-time data from the flight information API, weather information API, and traffic information API using the Python Requests library.

[1261] 2. Data analysis: Analyze the acquired data in JSON format and extract the necessary information.

[1262] 3. Route Calculation: Optimal routes are calculated based on real-time traffic and weather information, using AI models and machine learning algorithms.

[1263] 4. Proposal Generation: Proposes the calculated route and alternative routes to the user. In case of a delay, proposes an alternative route or a handover to another delivery person.

[1264] Hardware and software used

[1265] Hardware: smartphones, tablets, servers

[1266] Software: Python, Requests library, AI model (machine learning algorithm)

[1267] Specific examples

[1268] For example, if a user delivers food from Tokyo to Osaka, the server calculates the optimal route based on real-time traffic and weather information and suggests it to the device. If traffic congestion or bad weather is predicted, the server will suggest an alternative route or hand over to another delivery person.

[1269] Prompt Sentence Examples

[1270] "Enter your current location and destination, and we'll suggest the best route for you based on real-time traffic and weather information."

[1271] In this way, the system provides users with efficient delivery routes and allows for quick response in the event of delays.

[1272] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1273] Step 1:

[1274] The server retrieves real-time data from flight information API, weather information API, and traffic information API.

[1275] Input: API endpoint and request parameters

[1276] Data processing: Sending API requests and receiving responses

[1277] Output: JSON data of flight information, weather information, and traffic information

[1278] Step 2:

[1279] The server analyzes the acquired JSON data and extracts the necessary information.

[1280] Input: JSON data of flight information, weather information, and traffic information

[1281] Data processing: Parse JSON data and extract necessary fields

[1282] Output: Specific information about flight status, weather conditions, and traffic conditions

[1283] Step 3:

[1284] The server calculates the optimal route based on real-time traffic and weather information.

[1285] Input: Specific information about flight status, weather conditions, and traffic conditions

[1286] Data computation: Using AI models to calculate optimal routes that take traffic congestion and weather conditions into account

[1287] Output: Optimal route information

[1288] Step 4:

[1289] The server transmits the calculated optimal route information to the terminal.

[1290] Input: Optimal route information

[1291] Data processing: Converting route information into a format for sending to the device

[1292] Output: Route information sent to the device

[1293] Step 5:

[1294] The terminal displays the route information received from the server to the user.

[1295] Input: Route information received from the server

[1296] Data processing: Display route information in a user-friendly format

[1297] Output: Route information displayed to the user

[1298] Step 6:

[1299] The user inputs the current location information and destination through the terminal.

[1300] Input: current location, destination

[1301] Data processing: Converting location information and destination into a format for sending to the server

[1302] Output: Location and destination sent to the server

[1303] Step 7:

[1304] The server reacquires real-time information based on the location information and destination received from the user and recalculates the optimal route.

[1305] Input: location information, destination

[1306] Data calculation: Send another API request to get the latest flight, weather, and traffic information and recalculate the optimal route

[1307] Output: Updated optimal route information

[1308] Step 8:

[1309] The server sends the updated optimal route information to the terminal, which displays it to the user.

[1310] Input: Updated optimal route information

[1311] Data processing: Converts route information into a format suitable for sending to the device and sends it to the device.

[1312] Output: Updated route information displayed to the user

[1313] In this way, the system utilizes real-time information to provide efficient delivery routes to users.

[1314] 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.

[1315] "Example 1"

[1316] One embodiment of the present invention combines an AI-powered personal travel assistant with an emotion engine. This system recognizes the user's emotions and tailors travel suggestions based on those emotions. Specifically, if the system recognizes that the user is feeling happy or excited, it will suggest active tourist spots and entertainment facilities. On the other hand, if the system recognizes that the user is feeling tired or stressed, it will suggest relaxation facilities and quiet tourist spots.

[1317] "Example 2"

[1318] The emotion engine also recognizes the user's emotions in real time and provides services based on those emotions. For example, if it recognizes that the user is feeling angry or frustrated, it will provide support to resolve the problem. Specifically, it will respond quickly and appropriately to problems that may arise during travel, such as flight delays or hotel troubles.

[1319] "Example 3"

[1320] Furthermore, the emotion engine learns changes in the user's emotions and reflects them in the next travel recommendation. For example, if it recognizes that the user felt joy after visiting a particular tourist spot, it will suggest tourist spots with similar characteristics. This improves user satisfaction and makes the travel experience more personalized.

[1321] The processing flow of each embodiment will be described below.

[1322] "Example 1"

[1323] Step 1: The user launches the application and enters their travel needs and preferences.

[1324] Step 2: The system recognizes the user's emotions using the emotion engine.

[1325] Step 3: Based on the emotions recognized by the system, optimal travel recommendations are made.

[1326] "Example 2"

[1327] Step 1: A user encounters a problem during their trip (e.g., flight delay, hotel trouble).

[1328] Step 2: The system recognizes the user's emotions in real time using an emotion engine. Step 3: Based on the emotions recognized by the system (e.g., anger, frustration), the system provides support for resolving the problem.

[1329] "Example 3"

[1330] Step 1: A user visits a specific tourist attraction.

[1331] Step 2: The system recognizes the user's emotions using an emotion engine and learns changes in those emotions.

[1332] Step 3: The system reflects the learned changes in emotions in its next travel suggestions.

[1333] Example 1

[1334] Next, a description will be given of Example 1 of Form 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."

[1335] Conventional personal travel assistant systems make suggestions based on the user's travel needs and preferences, but they are unable to take the user's emotions into account when making suggestions, which means they are unable to fully increase user satisfaction. Furthermore, they lack real-time information provision and advanced suggestion generation using generative AI models, making it difficult to provide optimal travel plans for users.

[1336] 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.

[1337] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for recognizing a user's emotions and adjusting the travel suggestions based on the emotions, means for generating travel suggestions using a generative AI model, and means for providing the generated travel suggestions to a user terminal, thereby enabling the provision of optimal travel plans that take the user's emotions into consideration.

[1338] An "AI-powered personal travel assistant" is a system that uses artificial intelligence technology to assist users in planning their trips.

[1339] "Means for inputting customer travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[1340] "Means for suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants based on information input by the user.

[1341] "Means for providing real-time flight information, weather information, and local traffic information" is a function that provides users with the latest information on current flight status, weather, traffic conditions, etc.

[1342] The "means for recognizing the user's emotions and adjusting travel suggestions based on those emotions" is a function that analyzes the user's emotional state and adjusts the travel plan based on the results.

[1343] "Means for generating travel suggestions using a generative AI model" refers to a function that uses a generative AI model to automatically create travel suggestions based on a user's needs and emotions.

[1344] The "means for providing the generated travel proposal to the user terminal" is a function for transmitting the generated travel plan to the user's device and displaying it.

[1345] MODE FOR CARRYING OUT THE INVENTION

[1346] This invention relates to an AI-powered personal travel assistant system that provides optimal travel plans that take into account the user's travel needs, preferences, and emotions. The system receives input from the user through a dedicated application or chatbot, generates travel suggestions using an emotion recognition engine and generative AI model, and provides them to the user's device.

[1347] Hardware and software used

[1348] Hardware: Servers, user devices (smartphones, tablets, PCs)

[1349] Software: dedicated applications, chatbots, emotion recognition engines, generative AI models (e.g., GPT-4)

[1350] Data processing and calculation

[1351] 1. Getting user input:

[1352] Users launch a dedicated application or chatbot and input their travel needs and preferences, for example, "I'd like to relax at a resort near the beach."

[1353] The terminal receives the user's input and sends it to the server.

[1354] 2. Performing emotion recognition:

[1355] The server passes the received user input data to the emotion recognition engine, which analyzes emotions from the user's input, voice, facial expressions, etc.

[1356] If the user inputs "tired," the emotion recognition engine determines that the user is tired and returns the analysis result to the server.

[1357] 3. Generate travel suggestions:

[1358] The server generates travel suggestions using a generative AI model (e.g., GPT-4) based on the analysis results from the emotion recognition engine and the user's needs and preferences.

[1359] If the generative AI model recognizes that the user is tired, it will suggest relaxation facilities and quiet tourist spots. For example, it will generate suggestions such as, "The resort we recommend for you is X resort with a quiet beach."

[1360] 4. Providing Proposals:

[1361] The server transmits the generated travel proposal to the user terminal.

[1362] The device displays the received travel suggestions to the user, for example, "The resort we recommend for you is XX Resort, which has a quiet beach."

[1363] Specific examples

[1364] User input: The user opens the application and types, "I've been stressed lately and would like to go somewhere to relax."

[1365] Emotion recognition result: The emotion recognition engine analyzes the user's input and determines that the user is feeling stressed.

[1366] Travel suggestion generation: The generative AI model generates suggestions such as, "The recommended travel destination for you is the hot spring resort of X hot springs. It has a quiet and relaxing environment."

[1367] Providing suggestions: The server sends the generated suggestions to the user's device, which then displays the message, "The recommended travel destination for you is the hot spring resort of X hot springs. It has a quiet and relaxing environment."

[1368] Prompt Sentence Examples

[1369] "If a user is feeling stressed, suggest a relaxing travel destination."

[1370] "If the user is experiencing joy, suggest active tourist destinations."

[1371] The above is an embodiment of the present invention.

[1372] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1373] Step 1:

[1374] Input of user needs and preferences

[1375] User: Launches a dedicated application or chatbot and inputs their travel needs and preferences, for example, "I'd like to relax at a resort near the beach."

[1376] Input: Text data about the user's travel wishes and preferences.

[1377] Output: User input data is sent to the terminal.

[1378] What happens: The user enters "I want to relax at a resort near the beach" into the application's input field and presses the submit button.

[1379] Step 2:

[1380] Sending User Input

[1381] Terminal: Sends user input data to the server.

[1382] Input: User input data.

[1383] Output: User input data sent to the server.

[1384] Specific operation: The device sends the user's input data to the server via the Internet.

[1385] Step 3:

[1386] Performing emotion recognition

[1387] Server: Passes the received user input data to the emotion recognition engine, which analyzes emotions from the user's input, voice, facial expressions, etc.

[1388] Input: User input data.

[1389] Output: Analysis result by the emotion recognition engine (e.g., user is tired).

[1390] Specific operation: The server passes the user's input data to the emotion recognition engine, which analyzes the emotion "tired" and returns the result.

[1391] Step 4:

[1392] Generate travel suggestions

[1393] Server: Generates travel recommendations using a generative AI model (e.g., GPT-4) based on the analysis results from the emotion recognition engine and the user's needs and preferences.

[1394] Input: Emotion recognition engine analysis results and user needs and preferences.

[1395] Output: Generated travel suggestions (e.g. "The resort we recommend for you is X resort with a quiet beach").

[1396] Specific operation: The server inputs a prompt to the generated AI model, such as "If the user is tired, please suggest a relaxing travel destination," and the model generates a suggestion such as "The resort recommended for you is XX resort with a quiet beach."

[1397] Step 5:

[1398] Providing suggestions

[1399] Server: Sends the generated travel proposals to the user terminal.

[1400] Input: Generated travel suggestions.

[1401] Output: Trip suggestions sent to the user device.

[1402] Specific operations: The server transmits the generated travel proposal to the user terminal via the Internet.

[1403] Step 6:

[1404] View Suggestions

[1405] Terminal: Displays received travel suggestions to the user.

[1406] Input: Travel proposals sent by the server.

[1407] Output: The trip suggestions displayed to the user.

[1408] Specific operation: The device will display suggestions on the screen, such as "The resort we recommend for you is XX Resort, which has a quiet beach."

[1409] The above is the specific processing flow of the program of this system.

[1410] (Application example 1)

[1411] Next, a description will be given of Application Example 1 of Form 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."

[1412] Conventional personal travel assistant systems make suggestions based on the user's travel needs and preferences, but they are unable to take the user's emotions into account when making suggestions, which prevents them from fully increasing user satisfaction. Furthermore, they are unable to make suggestions to improve the shopping experience in physical stores, making it difficult to meet the diverse needs of users. Furthermore, since they do not utilize generative AI models to generate suggestions or prompts, the quality and accuracy of the suggestions is insufficient.

[1413] 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.

[1414] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist attractions, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for recognizing a user's emotions and adjusting suggestions based on the emotions, means for making suggestions to improve the shopping experience in physical stores, means for generating suggestions using a generative AI model, and means for generating prompt sentences and making suggestions. This enables optimal suggestions based on the user's emotions, thereby improving the shopping experience in physical stores. Furthermore, utilizing the generative AI model can improve the quality and accuracy of suggestions.

[1415] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to suggest optimal itineraries, tourist spots, and restaurants based on the user's travel needs and preferences.

[1416] "Means for inputting customer travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[1417] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to users based on their input needs and preferences.

[1418] "Means for providing real-time flight information, weather information, and local traffic information" is a function that provides users with current flight status, weather, and traffic information in real time.

[1419] The "means for recognizing the user's emotions and adjusting the suggestions based on those emotions" is a function for analyzing the user's emotions and changing the suggestions based on those emotions.

[1420] The "means for making suggestions to improve the shopping experience in physical stores" is a function that suggests products and services based on emotions and preferences so that users can enjoy shopping in physical stores more.

[1421] The "means for generating proposals using a generative AI model" is a function that utilizes a generative AI model to automatically generate proposals for users.

[1422] The "means for generating prompt sentences and making suggestions" is a function that generates appropriate prompt sentences based on the user's feelings and preferences and makes suggestions based on them.

[1423] The following system configuration will be described as an embodiment of the present invention.

[1424] System Configuration

[1425] This system includes a server, a user terminal, and various sensors and cameras. The server runs a program to provide an AI-powered personal travel assistant, and the user terminal is a device such as a smartphone or tablet. The sensors and cameras are used to recognize the user's emotions.

[1426] Program processing

[1427] The server performs the process using the following means.

[1428] 1. User needs and preferences input:

[1429] Through the user terminal, the user inputs their travel needs and preferences, for example, a specific preference such as "I want to relax at a resort near the beach."

[1430] 2. How to make the best offer:

[1431] Based on your needs and preferences, the server will suggest the best itineraries, attractions, and restaurants, taking into account real-time flight, weather, and local traffic information.

[1432] 3. Emotion recognition means:

[1433] Recognizes user emotions using sensors and cameras. For example, EmotionRecognizer analyzes images captured by a camera to determine whether the user is feeling happy or tired.

[1434] 4. Emotion-based suggestion adjustment measures:

[1435] The system tailors its recommendations based on the perceived emotion, for example suggesting relaxation spots if the user is feeling tired, or suggesting active tourist spots if the user is feeling happy.

[1436] 5. Ways to improve your shopping experience:

[1437] To improve the shopping experience in brick-and-mortar stores, we suggest products and services based on the user's emotions and preferences. For example, if the user prefers casual clothing, we suggest casual clothes.

[1438] 6. How to generate suggestions using generative AI models:

[1439] Use generative AI models to automatically generate suggestions for users, for example, by using the OpenAI API to generate prompts based on the user's emotions and preferences.

[1440] 7. Prompt generation method:

[1441] Based on the user's feelings and preferences, appropriate prompts are generated and suggestions are made. As a concrete example, the following prompts are generated:

[1442] Example prompt sentence:

[1443] Suggest product or service recommendations when the user is feeling joyful. The user prefers casual clothing.

[1444] Hardware and software used

[1445] Hardware: Camera (to capture the user's image)

[1446] Software: OpenAI API, EmotionRecognizer (emotion recognition library)

[1447] This will enable optimal suggestions based on user emotions, improving the shopping experience in physical stores. Furthermore, by utilizing generative AI models, it will be possible to improve the quality and accuracy of suggestions.

[1448] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1449] Step 1:

[1450] The user uses the terminal to input their travel needs and preferences.

[1451] Input: User's travel wishes and preferences (e.g., "I want to relax at a resort near the beach")

[1452] Output: Data about user needs and preferences

[1453] Specific operation: Users enter their travel wishes and preferences in text format through an application on their smartphone or tablet.

[1454] Step 2:

[1455] The server will suggest the best itineraries, attractions, and restaurants based on your needs and preferences.

[1456] Input: Data about user needs and preferences

[1457] Output: A list of suggested itineraries, attractions, and restaurants

[1458] How it works: The server uses AI algorithms to analyze the input data and generate an optimal travel plan.

[1459] Step 3:

[1460] A user uses the device to capture an image of themselves with the camera.

[1461] Input: User image

[1462] Output: Captured image data

[1463] Specific actions: The user uses the camera function of their smartphone or tablet to take a picture of their face.

[1464] Step 4:

[1465] The server uses the captured image to recognize the user's emotions.

[1466] Input: Captured image data

[1467] Output: Recognized emotion data (e.g., happy, tired)

[1468] Specific operation: The server uses the EmotionRecognizer library to analyze image data and recognize the user's emotions.

[1469] Step 5:

[1470] The server adjusts the suggestions based on the perceived sentiment.

[1471] Input: Recognized emotion data, suggested itineraries, tourist attractions, and restaurant lists

[1472] Output: Adjusted proposal

[1473] Specific operation: The server changes the suggestions based on the recognized emotion data. For example, if the user feels tired, it adds relaxation facilities.

[1474] Step 6:

[1475] The server makes suggestions to improve the shopping experience in physical stores.

[1476] Input: Recognized emotion data, user preference data

[1477] Output: Shopping suggestions

[1478] Specific operation: Based on the user's emotions and preferences, the server suggests products and services suitable for shopping in a physical store.

[1479] Step 7:

[1480] The server uses the generative AI model to generate the proposals.

[1481] Input: Recognized emotion data, user preference data

[1482] Output: Generated proposals

[1483] How it works: The server uses the OpenAI API to generate prompts based on the user's emotions and preferences, and then makes suggestions based on those prompts.

[1484] Step 8:

[1485] The server generates a prompt and makes suggestions.

[1486] Input: Recognized emotion data, user preference data

[1487] Output: Prompt and suggestions

[1488] How it works: The server uses a generative AI model to generate prompts like the one below and makes suggestions based on them.

[1489] Example prompt sentence:

[1490] Suggest product or service recommendations when the user is feeling joyful. The user prefers casual clothing.

[1491] Example 2

[1492] Next, a description will be given of Example 2 of Form 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."

[1493] Conventional travel assistant systems often fail to provide recommendations based on user needs and preferences, and struggle to recognize emotions in real time and provide services accordingly. This results in low user satisfaction and often delays in resolving travel issues. Furthermore, they are unable to effectively utilize users' past choices and feedback, making it difficult to provide personalized recommendations.

[1494] 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.

[1495] In this invention, the server includes means for inputting a user's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time transportation information, weather information, and local traffic information, means for recognizing a user's emotions in real time and providing services according to the emotions, means for learning the user's preferences using a machine learning algorithm, and means for generating suggestions using a generative AI model. This enables personalized suggestions based on the user's needs and preferences, and realizes real-time emotion recognition and prompt provision of services accordingly.

[1496] "Means for inputting user's travel needs and preferences" refers to an interface through which a user inputs their travel wishes and preferences into the system.

[1497] "Means of suggesting optimal itineraries, tourist spots, and restaurants based on input needs and preferences" refers to algorithms and systems that suggest optimal travel plans, tourist spots, and restaurants based on information input by the user.

[1498] "Means for providing real-time transportation information, weather information, and local traffic information" refers to a system that provides users with the latest information on current traffic conditions, weather, and local transportation.

[1499] "Means for recognizing a user's emotions in real time and providing services in accordance with those emotions" refers to a system that analyzes a user's emotional state in real time and provides appropriate services based on the results.

[1500] "Means for learning user preferences using machine learning algorithms" refers to algorithms for learning user preferences based on the user's past behavior and choices.

[1501] The "means for generating proposals using a generative AI model" is a system for automatically generating optimal proposals for users using AI technology.

[1502] MODE FOR CARRYING OUT THE INVENTION

[1503] This invention is a system that suggests optimal itineraries, tourist spots, and restaurants based on a user's travel needs and preferences. The system receives user input, generates suggestions using machine learning algorithms and generative AI models, and provides services by recognizing user emotions in real time.

[1504] Hardware and software used

[1505] The server uses machine learning libraries such as Python's Scikit-learn and TensorFlow to learn user preferences, and Google Cloud's Natural Language API and IBM Watson's sentiment analysis tools to recognize user emotions in real time. The device provides the user interface, such as a smartphone or PC, and receives user input.

[1506] Data processing and calculation

[1507] The server receives data input from the user and uses machine learning algorithms to learn the user's preferences. For example, if a user inputs "I want to go to a beach resort on my next vacation," the server generates a list of beach resorts based on past data and suggests them to the user. Suggestions are automatically generated using a generative AI model.

[1508] Specific examples

[1509] A user opens an app on their smartphone and types, "I want to go to a beach resort on my next vacation." The device sends this input data to the server. The server analyzes the received input data and compares it with past data. For example, if the user previously input that they like "beach resorts" and "relaxing," the server uses this as learning data.

[1510] The server uses a generative AI model based on the training data to generate a list of beach resorts, suggesting resorts such as "Waikiki Beach in Hawaii" and "Kuta Beach in Bali."

[1511] If a user types "my flight is delayed," the device analyzes this text in real time and uses an emotion engine to recognize the user's dissatisfaction. After recognizing the user's dissatisfaction, the server supports the user in suggesting alternative flights or extending their hotel stay. For example, it might suggest to the user, "The next flight departs in two hours. Would you like to extend your hotel stay?"

[1512] Prompt Sentence Examples

[1513] "I'd like to go to a beach resort on my next vacation. Can you recommend any places?"

[1514] "My flight is delayed. What should I do?"

[1515] In this way, the server and the terminal work together to provide the most suitable service according to the user's needs and emotions.

[1516] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] The user uses the terminal to input their travel needs and preferences. For example, they may input "I want to go to a beach resort on my next vacation." The input data includes the user's travel wishes and preferences. The terminal then transmits the input data to the server.

[1519] Step 2:

[1520] The server analyzes the input data it receives. Specifically, it uses machine learning libraries such as Python's Scikit-learn and TensorFlow to learn the user's preferences. Based on the input data, it takes into account past choices and feedback and models the user's preferences. The output is the learned results of the user's preferences.

[1521] Step 3:

[1522] The server uses a generative AI model based on the learning results to generate optimal suggestions. For example, if the server learns that a user prefers beach resorts, it generates a list of resorts that are close to the beach. Specifically, it suggests resorts such as "Waikiki Beach in Hawaii" and "Kuta Beach in Bali." The output is a list of suggested resorts.

[1523] Step 4:

[1524] The terminal displays the suggestions received from the server to the user, who can then review the list of suggested resorts and make a selection. The input is the suggestion data from the server, and the output is the display to the user.

[1525] Step 5:

[1526] When a user reports a problem during a trip, for example, by typing "my flight is delayed," the device analyzes this text in real time and sends it to the server. The input is the user's problem report.

[1527] Step 6:

[1528] The server analyzes the received problem report and recognizes the user's emotion using an emotion engine. Specifically, it uses Google Cloud Natural Language API and IBM Watson's emotion analysis tool. The input is the user's problem report, and the output is the emotion recognition result.

[1529] Step 7:

[1530] The server provides appropriate services based on the results of emotion recognition. For example, if the user is dissatisfied, it can suggest alternative flights or extend the stay at the hotel. Specifically, it suggests to the user, "The next flight departs in two hours. Would you like to extend your stay at the hotel?" The input is the result of emotion recognition, and the output is a service suggestion.

[1531] Step 8:

[1532] The terminal displays the service proposals from the server to the user, who can then review and select the proposed services. The input is the service proposal from the server, and the output is the display to the user.

[1533] In this way, the server and the terminal work together to provide the most suitable service according to the user's needs and emotions.

[1534] (Application example 2)

[1535] Next, a description will be given of Application Example 2 of Form 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."

[1536] Conventional food delivery systems do not adequately provide optimal suggestions based on user preferences and needs. Furthermore, they lack a mechanism for quickly responding to user complaints or anger, resulting in a poor user experience.

[1537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1538] In this invention, the server includes a means for providing an AI-equipped personal assistant, a means for inputting customer needs and preferences, a means for making optimal suggestions based on the input needs and preferences, a means for providing real-time information, a means for recognizing user emotions in real time, and a means for providing support in accordance with the user emotions. This makes it possible to make optimal suggestions based on the user's preferences and needs, and also to respond quickly when the user feels dissatisfied or anger.

[1539] An "AI-powered personal assistant" is a system that uses artificial intelligence to provide optimal suggestions and support based on the user's needs and preferences.

[1540] "Means for inputting customer needs and preferences" refers to the interface or method by which a user inputs their wishes and preferences into the system.

[1541] "Means for making optimal suggestions based on input needs and preferences" refers to algorithms or processes that present optimal options based on information input by the user.

[1542] A "real-time information provider" is a system or method for providing users with up-to-date information based on current circumstances and conditions.

[1543] "Means for recognizing a user's emotions in real time" refers to technology or devices that can instantly determine a user's emotions from their facial expressions, voice, etc.

[1544] The "means for providing support according to the user's emotions" refers to a system or method for providing appropriate responses or support based on the recognized user's emotions.

[1545] A system for carrying out this invention has the following configuration: A server includes means for providing an AI-equipped personal assistant, means for inputting customer needs and preferences, means for making optimal suggestions based on the input needs and preferences, means for providing real-time information, means for recognizing user emotions in real time, and means for providing support in accordance with the user emotions.

[1546] Hardware and software used

[1547] Hardware: Smartphone (camera, microphone)

[1548] Software: TensorFlow (machine learning model), OpenCV (image processing), Google Cloud Speech-to-Text API (voice recognition)

[1549] Data processing and calculation

[1550] 1. Learning user preferences:

[1551] The server collects past order data and uses TensorFlow to build a model that learns user preferences.

[1552] Example: If a user has frequently ordered "sushi" in the past, the next time suggestions are given, priority will be given to "sushi" restaurants.

[1553] 2. Best Suggestion:

[1554] The server uses the learned model to suggest restaurants and menus that best suit the user's current needs.

[1555] Example: Suggesting the best restaurant based on user preferences and current location.

[1556] 3. Emotion recognition:

[1557] The server uses OpenCV to process the image data acquired from the camera and analyze the user's facial expression.

[1558] The server uses the Google Cloud Speech-to-Text API to convert the audio data obtained from the microphone into text and analyze emotions.

[1559] Example: Recognizing emotions from a user's facial expressions using camera images as input.

[1560] 4. Problem-solving support:

[1561] Based on the emotion recognition results, the server automatically connects the user to customer support if they are feeling dissatisfied or angry.

[1562] Example: If the user's emotion is recognized as "anger," send a notification to customer support.

[1563] Prompt Sentence Examples

[1564] Learn user preferences: "Learn user preferences based on past order data."

[1565] Emotion Recognition: "Recognize user emotions in real time based on camera images and audio data."

[1566] In this way, the embodiment of the invention provides a system that can make optimal suggestions based on the user's preferences and needs, and can also respond quickly when the user feels dissatisfied or angered.

[1567] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1568] Step 1:

[1569] Users input their needs and preferences using a terminal. The input data is sent to the server. The input data includes past order history and evaluation information.

[1570] Step 2:

[1571] The server uses TensorFlow to learn the user's preferences based on the received data. Specifically, it uses past order history and rating information as input to generate a model that characterizes the user's tastes. The output is a feature vector that reflects the user's preferences.

[1572] Step 3:

[1573] The server uses the generated feature vector to suggest restaurants and menus that best suit the user's current needs. Specifically, it runs an algorithm that selects the best restaurant using the user's current location information and feature vector as input. The output is a list of suggested restaurants and menus.

[1574] Step 4:

[1575] The user uses the device to review suggested restaurants and menus, and once the user makes a selection, the information is sent to the server.

[1576] Step 5:

[1577] The server processes the order based on the user's selection, sending the order to the selected restaurant and receiving order confirmation information. The output is order confirmation information.

[1578] Step 6:

[1579] The server provides real-time information, specifically notifying the user of the order progress and delivery time. The input is progress information from the restaurant, and the output is notification information to the user.

[1580] Step 7:

[1581] The user uses the device to express emotions through the camera and microphone. The server processes the camera images using OpenCV and converts the voice data into text using the Google Cloud Speech-to-Text API. The input is the camera images and voice data, and the output is the user's emotional information.

[1582] Step 8:

[1583] If the server determines that the user is feeling dissatisfied or angry based on the emotion recognition results, it automatically connects the user to customer support. Specifically, it takes emotion information as input and sends a notification to customer support. The output is notification information for customer support.

[1584] In this way, the system provides a system that can make optimal suggestions based on the user's preferences and needs through the specific actions performed at each step, and can also quickly respond when the user feels dissatisfied or angered.

[1585] Example 3

[1586] Next, a description will be given of Example 3 of Form Example 3. 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."

[1587] Conventional travel assistant systems do not adequately provide real-time information or personalized suggestions based on the user's emotions. As a result, users have difficulty obtaining prompt and appropriate information when changing their travel plans or selecting a new travel destination. Furthermore, they are unable to effectively utilize the user's past travel history and emotional data, which makes it difficult to improve user satisfaction.

[1588] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1589] In this invention, the server includes means for collecting real-time flight information, weather information, and local traffic information and storing them in a database, means for retrieving and displaying the latest information from the server when accessed by a customer, means for the customer to input travel feedback and transmit the data to the server, means for learning customer emotion data using a machine learning model and reflecting the data in next travel proposals, and means for generating and displaying personalized travel proposals based on the customer's past emotion data, thereby enabling users to quickly obtain real-time information and receive personalized proposals based on past emotion data.

[1590] "Real-time flight information" refers to information that provides the operational status of aircraft in real time.

[1591] "Weather information" is information that indicates the weather conditions in a specific area or location.

[1592] "Local traffic information" refers to information about traffic conditions and modes of transportation in a specific area.

[1593] A "database" is a system for systematically organizing and storing information.

[1594] A "server" is a computer system that provides data and services over a network.

[1595] A "terminal" is a device that is directly operated by a user and that communicates with a server to obtain information.

[1596] A "customer" is a user who uses the system to plan and obtain travel information.

[1597] "Feedback" refers to evaluations and opinions provided by users to the system.

[1598] A "machine learning model" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[1599] "Emotion data" is information relating to the user's emotions and thoughts.

[1600] "Personalized travel suggestions" are travel suggestions that are customized based on a user's individual preferences and historical data.

[1601] This invention is a system that provides real-time flight information, weather information, and local traffic information, and makes personalized travel suggestions based on the user's emotional data. Specific embodiments of this system are described below.

[1602] Real-time information system

[1603] The server uses external APIs (e.g., flight information API, weather information API, traffic information API) to collect real-time flight information, weather information, and local traffic information and stores it in the database, ensuring that the latest information is always kept in the database.

[1604] When a user accesses the app, the device retrieves the latest information from the server and displays it to the user. For example, if a user opens an app to plan a trip, the device sends a request to the server to retrieve the latest flight and weather information.

[1605] Users can check real-time information through the app and plan their trips. For example, if a flight is delayed, users can receive real-time information through the app and be offered alternative flights or transportation options.

[1606] For example, if a user opens the app and types "What are the next flight details?", the device will send a request to the server to get the latest flight information and display it to the user, like this:

[1607] "Flight AA123 is delayed by one hour. The alternative flight is BB456."

[1608] Travel recommendation system using emotion engine

[1609] The server collects user emotion data and uses machine learning models (e.g., TensorFlow, PyTorch) to learn emotion changes. This data is obtained from user feedback and behavioral history.

[1610] When a user inputs feedback about their trip, the device sends the data to the server. For example, if a user inputs feedback such as "I had fun" in the app after visiting a particular tourist spot, the data is sent to the server.

[1611] The next time a user requests travel suggestions, they will receive personalized suggestions based on their past emotional data. For example, if the system recognizes that a user felt joy after visiting a particular tourist spot, it will suggest tourist spots with similar characteristics.

[1612] For example, if a user inputs feedback after a trip such as "I really enjoyed this tourist spot," the device will send that data to the server. Next time, if the user inputs "Please suggest my next travel destination," the server will use the emotion engine to generate the following suggestions, which the device will display to the user:

[1613] "I recommend the following tourist spots that are similar to the ones I enjoyed on my last trip: Tourist Spot A, Tourist Spot B."

[1614] In this way, the system can provide real-time information and personalized travel suggestions utilizing the emotion engine. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1615] Real-time information system

[1616] Step 1: Gather information

[1617] The server uses external APIs (e.g., flight information API, weather information API, traffic information API) to collect real-time flight information, weather information, and local traffic information, which is periodically updated and stored in a database.

[1618] Input: Data request from external API

[1619] Output: The latest information stored in the database

[1620] What happens: The server sends an API request, for example:

[1621] GET https: / / api.flightinfo.com / flights

[1622] GET https: / / api.weatherinfo.com / weather

[1623] GET https: / / api.trafficinfo.com / traffic

[1624] The acquired data is stored in a database.

[1625] Step 2: Request Information

[1626] If a user opens the app and wants to check the next flight information, they type "What is the next flight information?" in the app.

[1627] Input: User prompt text

[1628] Output: Request from terminal to server

[1629] What happens: The user enters a prompt in the app.

[1630] Step 3: Information Acquisition

[1631] The terminal sends the user's request to the server, which retrieves the latest flight information from the database and returns it to the terminal.

[1632] Input: Request from terminal

[1633] Output: Latest information from the server

[1634] Specific operation: The device sends the following request to the server:

[1635] GET / flights?flightNumber=AA123

[1636] The server retrieves the information from the database and returns it to the terminal.

[1637] Step 4: Display information

[1638] The terminal displays the information received from the server to the user. For example, if the flight is delayed, the terminal displays the following information:

[1639] Input: Latest information from the server

[1640] Output: Displaying information to the user

[1641] What happens: The device displays the following information to the user:

[1642] "Flight AA123 is delayed by one hour. The alternative flight is BB456."

[1643] Travel recommendation system using emotion engine

[1644] Step 1: Collecting Emotional Data

[1645] After a trip, the user enters feedback in the app, for example, "I really enjoyed this tourist spot."

[1646] Input: User feedback

[1647] Output: Sending data from the device to the server

[1648] What happens: A user enters feedback in the app.

[1649] Step 2: Send data

[1650] The device sends the user's feedback to the server, which then sends this data to the emotion engine.

[1651] Input: User feedback

[1652] Output: Send data to the server

[1653] Specific operation: The device sends the following data to the server:

[1654] POST / feedback

[1655] {

[1656] "userId": "12345",

[1657] "feedback": "I really enjoyed this tourist spot"

[1658] }

[1659] Step 3: Emotional Learning

[1660] The server uses machine learning models (e.g., TensorFlow, PyTorch) to learn the user's emotional data, which allows it to understand changes in the user's emotions and reflect them in its next travel suggestions.

[1661] Input: User feedback data

[1662] Output: Learned emotion model

[1663] What happens: The server uses a machine learning model to learn the data.

[1664] Step 4: Travel proposal

[1665] When a user wants suggestions for their next trip, they type "Suggest my next trip" into the app.

[1666] Input: User prompt text

[1667] Output: Request from terminal to server

[1668] What happens: The user enters a prompt in the app.

[1669] Step 5: Proposal Generation

[1670] The server uses an emotion engine to generate personalized travel suggestions based on the user's past emotion data, for example, suggesting places similar to tourist spots enjoyed on a previous trip.

[1671] Input: User's past emotional data

[1672] Output: Personalized travel suggestions

[1673] What it does: The server uses the emotion engine to generate suggestions such as:

[1674] {

[1675] "recommendations": ["Tourist Spot A", "Tourist Spot B"]

[1676] }

[1677] Step 6: Display suggestions

[1678] The device displays the travel suggestions received from the server to the user, for example, suggestions such as:

[1679] Input: Travel suggestions from the server

[1680] Output: Proposal displayed to the user

[1681] What it does: The device displays the following suggestions to the user:

[1682] "I recommend the following tourist spots that are similar to the ones I enjoyed on my last trip: Tourist Spot A, Tourist Spot B."

[1683] In this way, the system can provide real-time information and personalized travel recommendations using an emotion engine.

[1684] (Application example 3)

[1685] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1686] Conventional food delivery systems were unable to predict delivery times that took into account real-time traffic and weather information, which led to lower customer satisfaction. Furthermore, they lacked the ability to learn customer sentiment and suggest preferred dishes and restaurants for the next order, making it difficult to provide personalized service. This made it difficult to improve customer repeat rates and satisfaction.

[1687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1688] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for predicting delivery times based on real-time traffic and weather information, and means for learning customer emotions and suggesting preferred dishes and restaurants for the next order. This enables accurate delivery time predictions based on real-time information and personalized suggestions based on customer emotions.

[1689] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to help customers plan their trips.

[1690] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel needs and preferences into the system.

[1691] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to customers based on their input needs and preferences.

[1692] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[1693] "Means for predicting delivery times based on real-time traffic and weather information" refers to a function that predicts food delivery times based on current traffic conditions and weather information.

[1694] "Means of learning customer emotions and suggesting preferred dishes and restaurants the next time they order" refers to a function that analyzes a customer's past order history and ratings, learns their emotions, and suggests dishes and restaurants that suit the customer's preferences the next time they order.

[1695] A system for carrying out this invention is configured as follows: The server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for predicting delivery times based on real-time traffic and weather information, and means for learning customer sentiment and suggesting preferred dishes and restaurants for the next order.

[1696] Hardware and software used

[1697] Hardware: Smartphone (iOS or Android)

[1698] software:

[1699] Real-time data acquisition API (e.g., Google Maps API, OpenWeatherMap API)

[1700] Sentiment analysis engines (e.g., IBM Watson, Microsoft Azure Emotion API)

[1701] Databases (e.g., Firebase, MongoDB)

[1702] Data processing and calculation

[1703] Real-time data acquisition

[1704] The server uses the Google Maps API to get current traffic information and the OpenWeatherMap API to get current weather information, and then uses this information to predict delivery times.

[1705] sentiment analysis

[1706] The server inputs the user's past order history and reviews into a sentiment analysis engine to analyze the user's sentiment. If the user has positive feelings about a particular dish or restaurant, the information is stored in a database.

[1707] Next proposal

[1708] The next time the user uses the app, the server will suggest their favorite dishes and restaurants based on their past emotional data.

[1709] Specific examples

[1710] If a user has previously ordered pizza and left a review saying it was delicious, the next time they open the app, a pizza suggestion will be displayed. The app also displays a delivery time forecast based on current traffic and weather information.

[1711] Prompt Sentence Examples

[1712] Below are some examples of prompt sentences:

[1713] python

[1714] Get traffic information using Google Maps API

[1715] import requests

[1716] def get_traffic_info(api_key, origin, destination):

[1717] url = f"https: / / maps.googleapis.com / maps / api / directions / json?origin={origin}&destination={destination}&key={api_key}"

[1718] response = requests.get(url)

[1719] traffic_data = response.json()

[1720] return traffic_data

[1721] Get weather information using the OpenWeatherMap API

[1722] def get_weather_info(api_key, location):

[1723] url = f"http: / / api.openweathermap.org / data / 2.5 / weather?q={location}&appid={api_key}"

[1724] response = requests.get(url)

[1725] weather_data = response.json()

[1726] return weather_data

[1727] Analyze user sentiment using a sentiment analysis engine

[1728] def analyze_emotion(text):

[1729] Here we use a hypothetical sentiment analysis function

[1730] if "delicious" in text:

[1731] return "positive"

[1732] else:

[1733] return "neutral"

[1734] Store emotion data in a database

[1735] def save_emotion_data(user_id, emotion_data):

[1736] Here we use a hypothetical database save function

[1737] pass

[1738] Generate next proposal

[1739] def generate_recommendation(user_id):

[1740] Here we use a tentative proposal generation function

[1741] return ["Pizza", "Sushi"]

[1742] Main Processing

[1743] def main():

[1744] api_key_maps = "YOUR_GOOGLE_MAPS_API_KEY"

[1745] api_key_weather = "YOUR_OPENWEATHERMAP_API_KEY"

[1746] origin = "user's current location"

[1747] destination = "restaurant location"

[1748] location = "user's current location"

[1749] traffic_info = get_traffic_info(api_key_maps, origin, destination)

[1750] weather_info = get_weather_info(api_key_weather, location)

[1751] user_review = "This pizza was delicious"

[1752] emotion = analyze_emotion(user_review)

[1753] save_emotion_data("user123", emotion)

[1754] recommendation = generate_recommendation("user123")

[1755] print(f"Next recommendation: {recommendation}")

[1756] print(f"Traffic information: {traffic_info}")

[1757] print(f"Weather information: {weather_info}")

[1758] if __name__ == "__main__":

[1759] main()

[1760] The program obtains real-time traffic and weather information, analyzes user sentiment, and generates next-generation recommendations. The specific API key and database implementation need to be configured according to the actual environment.

[1761] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1762] Step 1:

[1763] The server gets traffic information using the Google Maps API. Specifically, it sends a request to the Google Maps API with the user's current location (origin) and the restaurant's location (destination) as input. It receives the traffic information (traffic_data) returned by the API and stores it for use in the next processing step.

[1764] Step 2:

[1765] The server retrieves weather information using the OpenWeatherMap API. Specifically, it sends a request to the OpenWeatherMap API with the user's current location as input. It receives the weather information (weather_data) returned by the API and stores it for use in the next processing step.

[1766] Step 3:

[1767] The server inputs the user's past order history and reviews into a sentiment analysis engine to analyze the user's emotions. Specifically, it sends the user's review (user_review) as input to the sentiment analysis engine. It receives the emotion data (emotion) returned by the sentiment analysis engine and stores it in a database.

[1768] Step 4:

[1769] The server generates the next recommendation based on the emotion data stored in the database. Specifically, it inputs the user ID (user_id), retrieves the emotion data from the database, and generates a list (recommendation) of dishes and restaurants that match the user's preferences.

[1770] Step 5:

[1771] The server displays the next recommendation to the user. Specifically, it sends the generated recommendation list (recommendation) and the acquired traffic information (traffic_info) and weather information (weather_info) to the user's device, and displays this information when the user opens the app.

[1772] Step 6:

[1773] The user places their next order based on the suggestion list, traffic information, and weather information provided by the server. Specifically, the user selects the suggested dishes and restaurants and confirms the order. The server receives the user's selection and stores it in a database as the next order history.

[1774] 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.

[1775] 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> ) 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.

[1776] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1777] 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.

[1778] [Third embodiment]

[1779] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1780] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1781] 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).

[1782] 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.

[1783] 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.

[1784] 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).

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1791] "Example 1"

[1792] One example of the present invention is a system that provides an AI-powered personal travel assistant. This system accepts input of travel needs and preferences from customers through a dedicated application or chatbot. For example, a customer can input a preference such as "I want to relax at a resort near the beach."

[1793] "Example 2"

[1794] Based on the customer's needs and preferences, the system will suggest the best itineraries, tourist attractions, and restaurants. These suggestions are made using AI machine learning to learn the customer's preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[1795] "Example 3"

[1796] In addition, the system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently. For example, if a flight is delayed, the system can provide that information to customers in real time and suggest alternative flights or transportation options.

[1797] The processing flow of each embodiment will be described below.

[1798] "Example 1"

[1799] Step 1: Customers input their travel needs and preferences through a dedicated application or chatbot, for example, "I want to relax at a resort near the beach."

[1800] Step 2: The system analyzes your needs and preferences and makes recommendations based on them, including the best itinerary, attractions, and restaurants.

[1801] Step 3: The proposal is communicated to the customer, who reviews the proposal and adjusts the itinerary as necessary.

[1802] "Example 2"

[1803] Step 1: The system uses AI machine learning to learn customer preferences. For example, if the customer prefers beach resorts, the system will suggest resorts near the beach.

[1804] Step 2: The learned results are stored in a database and used the next time a recommendation is made. Step 3: When the customer enters new travel needs or preferences, the system combines them with the learned results to make more accurate recommendations.

[1805] "Example 3"

[1806] Step 1: The system provides real-time flight, weather, and local traffic information, allowing customers to plan their trips more efficiently.

[1807] Step 2: For example, if a flight is delayed, the system provides that information to the customer in real time.

[1808] Step 3: Additionally, the system can suggest alternative flights and transportation options, allowing customers to quickly adjust their travel plans.

[1809] Example 1

[1810] Next, a description will be given of Example 1 of Form 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."

[1811] Conventional travel planning systems have difficulty providing optimal travel plans that meet the individual needs and preferences of users. They also cannot respond to requests for real-time revisions or additions, which makes it difficult to increase user satisfaction. Furthermore, since proposals do not take into account past travel history or rating information, they are unable to provide plans that perfectly match the user's preferences.

[1812] 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.

[1813] In this invention, the server includes: a means for a user to input travel needs and preferences; a means for the terminal to send the input data to the server; a means for the server to input a prompt sentence to the generative AI model; a means for the generative AI model to generate a travel plan; a means for the server to send the generated travel plan to the terminal; a means for the user to check the travel plan and input corrections or additions as necessary; a means for the terminal to send the correction data to the server; a means for the server to again input a prompt sentence to the generative AI model to generate an updated plan; and a means for the server to send the updated travel plan to the terminal. This makes it possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction.

[1814] "User" means an individual or entity that inputs travel needs and preferences and reviews and modifies proposed travel plans.

[1815] "Terminal" refers to an electronic device that allows a user to input their travel needs and preferences and communicate with the server. Examples of such devices include smartphones, tablets, and PCs.

[1816] The "server" is a central processing unit that receives data sent by the user, inputs prompt sentences into the generative AI model, and sends the generated travel plan to the user's terminal.

[1817] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates optimal travel plans. Specifically, it includes models that use machine learning algorithms.

[1818] A "prompt" is text data that is input into the generative AI model and contains information such as the user's needs, preferences, budget, and travel duration.

[1819] A "trip plan" is a travel suggestion, including accommodation, attractions, and dining options, generated by a generative AI model based on the user's needs and preferences.

[1820] "Correction data" refers to data that includes additional requests or changes that a user inputs to a proposed travel plan.

[1821] "Real-time" refers to the generative AI model's ability to respond immediately to user input and modifications, quickly generating new travel plans.

[1822] This invention provides an AI-powered personal travel assistant system that allows users to input their travel needs and preferences and generate an optimal travel plan based on the input. Specific embodiments of this system are described below.

[1823] First, a user uses a dedicated application or chatbot to input their travel needs and preferences, such as a specific request such as "I want to relax at a resort near the beach." They also input a budget of "under 100,000 yen," a trip duration of "three days," and a departure point of "Tokyo."

[1824] Next, the device sends the data entered by the user to the server in real time. The server inputs a prompt into the generative AI model based on the received data. The prompt specifically describes the user's requests and conditions. For example, the following prompts can be generated:

[1825] User Request: Relax at a resort near the beach

[1826] Budget: Under 100,000 yen

[1827] Trip duration: 3 days

[1828] Departure point: Tokyo

[1829] The generative AI model analyzes the input prompt and generates a travel plan that best suits the user's requests. Specifically, this includes models that use machine learning algorithms. For example, a generative AI model such as OpenAI's GPT-4 is used. This model takes into account the user's preferences, budget, weather information at the travel destination, and other factors to create a plan that includes accommodation, tourist attractions, and dining options.

[1830] The generated travel plan is sent from the server to the user's device. The user can review the plan through the device and input any corrections or additions they need. For example, they could input a request such as "I'd like to increase my budget a little and stay at a more luxurious accommodation."

[1831] The device sends the revised data entered by the user to the server, which then inputs prompts into the generative AI model based on the revised data to generate an updated travel plan. For example, the budget could be increased to 150,000 yen, and a plan including more luxurious accommodations would be generated.

[1832] The updated travel plan is then sent from the server to the user's terminal, where the user can review the plan and make a final decision.

[1833] In this way, it is possible to provide optimal travel plans tailored to the user's individual needs and preferences in real time, thereby increasing user satisfaction. The system can also take into account the user's past travel history and rating information when making suggestions, thereby providing more personalized travel plans.

[1834] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1835] Step 1:

[1836] Users input their travel needs and preferences.

[1837] Specifically, a user uses a dedicated application or chatbot to input a request such as "I want to relax at a resort near the beach," and also inputs a budget of "under 100,000 yen," a travel period of "3 days," and a departure point of "Tokyo."

[1838] Input: User's travel needs and preferences, budget, travel duration, departure point

[1839] Output: Input data

[1840] Step 2:

[1841] The terminal sends the input data to the server.

[1842] Specifically, the terminal transmits the data input by the user to the server in real time.

[1843] Input: Data entered by the user

[1844] Output: Data sent to the server

[1845] Step 3:

[1846] The server inputs a prompt sentence into the generative AI model.

[1847] Specifically, the server generates a prompt based on the received data:

[1848] User Request: Relax at a resort near the beach

[1849] Budget: Under 100,000 yen

[1850] Trip duration: 3 days

[1851] Departure point: Tokyo

[1852] This prompt sentence is input into the generative AI model.

[1853] Input: Data sent to the server

[1854] Output: The prompt sentence that is input to the generative AI model

[1855] Step 4:

[1856] A generative AI model generates a travel plan.

[1857] Specifically, the generative AI model analyzes the prompt and generates a travel plan that best suits the user's needs, including accommodation, tourist attractions, and dining options.

[1858] Input: prompt statement

[1859] Output: Generated itinerary

[1860] Step 5:

[1861] The server transmits the generated travel plan to the terminal.

[1862] Specifically, the server sends the generated travel plan to the user's terminal, where the user can check the plan.

[1863] Input: Generated itinerary

[1864] Output: Trip plan sent to the device

[1865] Step 6:

[1866] The user checks the travel plan and enters any corrections or additions as necessary.

[1867] Specifically, the user checks the proposed plan and inputs any modifications or additional requests, such as "I'd like to increase my budget a little and get a more luxurious accommodation."

[1868] Input: Travel plan sent to the device

[1869] Output: Corrections and additions requested

[1870] Step 7:

[1871] The terminal transmits the correction data to the server.

[1872] As a specific operation, the terminal transmits the correction data input by the user to the server.

[1873] Input: Corrections and additions

[1874] Output: Corrected data sent to the server

[1875] Step 8:

[1876] The server again inputs the prompt sentence into the generative AI model and generates an updated plan.

[1877] Specifically, the server inputs the prompt sentences into the generative AI model again based on the revised data, and generates an updated travel plan, for example, increasing the budget to 150,000 yen and including more luxurious accommodations.

[1878] Input: Correction data

[1879] Output: Updated itinerary

[1880] Step 9:

[1881] The server sends the updated travel plan to the terminal.

[1882] Specifically, the server sends the updated travel plan to the terminal, where the user can review the plan again and make a final decision.

[1883] Input: Updated travel plans

[1884] Output: Updated itinerary sent to device

[1885] (Application example 1)

[1886] Next, a description will be given of Application Example 1 of Form 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."

[1887] Conventional travel planning systems have difficulty providing optimal travel plans based on customer needs and preferences. They also lack real-time information and suggestions that take into account customers' past travel history, making it difficult to increase customer satisfaction. Furthermore, they lacked the ability to automatically generate and customize travel plans using generative AI models, making it difficult to provide a user-friendly system for customers.

[1888] 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.

[1889] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on the customer's input using a generative AI model, and means for displaying the generated travel plan to the customer so that the customer can select and customize it. This makes it possible to provide optimal travel plans that meet the customer's needs, thereby increasing customer satisfaction.

[1890] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to provide optimal travel plans for individual customers.

[1891] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[1892] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel schedules, tourist spots, and restaurants based on customer input.

[1893] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that provides current flight status, weather, and local traffic conditions in real time.

[1894] A "generative AI model" is an artificial intelligence model that automatically generates travel plans based on customer input.

[1895] "Means for generating travel plans" refers to a function that uses a generative AI model to create travel plans based on customer needs.

[1896] "Means for displaying travel plans to customers and allowing them to select and customize" refers to the function of presenting generated travel plans to customers and allowing them to select or change those plans.

[1897] The following system configuration is adopted as an embodiment of this invention: The server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for generating a travel plan based on customer input using a generative AI model, and means for displaying the generated travel plan to the customer, allowing them to select and customize it.

[1898] Hardware and software used

[1899] Hardware: Smartphone

[1900] Software: Python, OpenAI API

[1901] Data processing and calculation

[1902] 1. User Input: Users input their travel needs and preferences through a smartphone application, such as a specific preference such as "I want to relax at a resort near the beach."

[1903] 2. Use of generative AI model: The server sends the user's input to the generative AI model (OpenAI's GPT-3) as a prompt. An example of a prompt sentence is as follows:

[1904] User travel needs and preferences: Relax at a resort near the beach

[1905] Please suggest a recommended travel plan.

[1906] 3. Itinerary Generation: The generative AI model generates an itinerary based on the prompts, including accommodations, attractions, restaurants, activities, etc.

[1907] 4. Display and customize itinerary: The server displays the generated itinerary to the user and allows the user to select and customize the itinerary. For example, the user can select "Beachfront Resorts in Maui" and add "Sunset Cruise."

[1908] Specific examples

[1909] If a user inputs, "I want to relax at a resort near the beach," the generative AI model will suggest the following itinerary:

[1910] Recommended travel plans:

[1911] 1. Accommodation: Maui Beachfront Resorts

[1912] 2. Sightseeing spot: Snorkeling at Hanauma Bay

[1913] 3. Restaurant: Beachside seafood restaurant

[1914] 4. Activity: Sunset Cruise

[1915] In this way, it becomes possible to provide the user with an optimal travel plan that meets their needs, thereby increasing customer satisfaction.

[1916] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1917] Step 1:

[1918] The user launches the smartphone application and inputs their travel needs and preferences.

[1919] Input: User's travel needs and preferences (e.g., "I want to relax at a resort near the beach")

[1920] Output: User input data

[1921] Specific operation: The user enters the desired travel conditions in text format into the application's input form and presses the submit button.

[1922] Step 2:

[1923] The terminal transmits the user's input data to the server.

[1924] Input: User-entered data

[1925] Output: User input data sent to the server

[1926] Specific operation: The terminal sends the user's input data to the server as an HTTP request.

[1927] Step 3:

[1928] The server receives the user's input data and sends it to the generative AI model as prompts.

[1929] Input: User input data sent to the server

[1930] Output: The prompt sent to the generative AI model

[1931] Specific operation: The server converts the user's input data into a prompt sentence and sends it to the generative AI model using the OpenAI API. An example of a prompt sentence is as follows:

[1932] User travel needs and preferences: Relax at a resort near the beach

[1933] Please suggest a recommended travel plan.

[1934] Step 4:

[1935] A generative AI model generates a travel plan based on the prompt.

[1936] Input: The prompt sent to the generative AI model

[1937] Output: Generated itinerary

[1938] How it works: The generative AI model analyzes the prompt and generates an appropriate itinerary, including accommodations, tourist attractions, restaurants, activities, etc.

[1939] Step 5:

[1940] The server receives the generated itinerary and displays it to the user.

[1941] Input: Generated itinerary

[1942] Output: The itinerary displayed to the user

[1943] Specific operation: The server sends the travel plan received from the generative AI model to the user's device and displays it on the application.

[1944] Step 6:

[1945] Users select and customize the itinerary displayed.

[1946] Input: A travel plan displayed to the user

[1947] Output: User selected and customized travel plan

[1948] What it does: The user selects the itinerary they want from the displayed options and customizes it as needed, for example, adding specific activities or changing accommodations.

[1949] Step 7:

[1950] The device sends the user's selected and customized travel plan to the server.

[1951] Input: User-selected and customized travel plans

[1952] Output: The customized itinerary sent to the server.

[1953] Specific operation: The device sends the travel plan selected and customized by the user to the server as an HTTP request.

[1954] Step 8:

[1955] The server saves the final itinerary and sends a confirmation message to the user.

[1956] Input: Customized itinerary sent to the server

[1957] Output: A confirmation message sent to the user

[1958] What happens: The server saves the customized itinerary to a database and sends the user a confirmation message that includes the final itinerary details.

[1959] Example 2

[1960] Next, a description will be given of Example 2 of Form 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."

[1961] Conventional travel planning systems have difficulty making optimal suggestions based on the user's individual needs and preferences. Furthermore, they are unable to provide real-time information on transportation options, weather, and local traffic information in an integrated manner, which makes it difficult to improve the efficiency and satisfaction of users' travel plans.

[1962] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting a user's travel needs and preferences, a means for proposing optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, a means for executing a machine learning model to generate the proposals, a means for transmitting the proposals to the user's terminal, a means for displaying the proposals on the user's terminal, and a means for providing real-time information on transportation means, weather information, and local traffic information. This makes it possible to make optimal proposals based on the user's individual needs and preferences, thereby improving the efficiency and satisfaction of travel planning.

[1963] "User" means an individual or entity that utilizes the System to input travel needs and preferences and receive suggestions.

[1964] "Travel needs and preferences" refers to the conditions and preferences a user has for travel, and specifically includes the places they want to go, the types of accommodations they like, and their food preferences.

[1965] An "itinerary" refers to a travel plan or schedule, including details of places to visit, transportation, accommodation, etc.

[1966] "Tourist attractions" refer to places and sights that are worth visiting at a travel destination, including historical buildings, natural landscapes, theme parks, etc.

[1967] "Eating establishment" refers to a place where a user visits to eat, and includes restaurants, cafes, bars, etc.

[1968] "Suggestions" refers to lists of itineraries, attractions, and dining options generated by the system based on the user's needs and preferences.

[1969] A "machine learning model" refers to an algorithm that learns patterns based on data and makes predictions and classifications.

[1970] "Terminal" refers to the device used by a user to access the system, and specifically includes a PC, smartphone, tablet, etc.

[1971] "Real-time transportation information" refers to up-to-date information about current traffic conditions and schedules.

[1972] "Weather information" refers to the latest information about the weather at your travel destination.

[1973] "Local traffic information" refers to up-to-date information about public transport and road conditions at your travel destination.

[1974] This invention is a system that suggests optimal itineraries, tourist spots, and restaurants based on a user's travel needs and preferences. The system receives user input, uses machine learning models to generate suggestions, and displays them on the user's device.

[1975] Hardware and software used

[1976] Hardware: Servers, devices (PCs, smartphones, tablets)

[1977] Software: Machine learning models (e.g., TensorFlow, PyTorch), databases (e.g., MySQL, PostgreSQL), web servers (e.g., Apache, Nginx)

[1978] Explanation of program processing

[1979] 1. Accepting user input

[1980] The user uses the device to input their travel needs and preferences, for example, "I want to go to a beach resort."

[1981] 2. Data transmission

[1982] The terminal sends the entered data to the server as an HTTP request.

[1983] 3. Data storage

[1984] The server stores the received data in a database, which also stores the user's previous input data.

[1985] 4. Running the machine learning model

[1986] The server runs a machine learning model on the stored data, which learns the user's preferences and generates optimal suggestions.

[1987] 5. Proposal Generation

[1988] The server then generates a list of itineraries, tourist spots, and restaurants that are optimal for the user based on the learning results. For example, if a user prefers beach resorts, it will suggest resorts that are close to the beach.

[1989] 6. Submit your proposal

[1990] The server sends the generated proposal to the device, and the data is sent as an HTTP response.

[1991] 7. Display of Suggestions

[1992] The terminal displays the received suggestions to the user, who can then review the suggested list and make a selection.

[1993] Specific examples

[1994] Examples:

[1995] The user enters, "I want to go to a beach resort."

[1996] The server receives this information and stores it in a database.

[1997] A machine learning model learns user preferences and generates beach resort suggestions.

[1998] The server sends suggestions such as "Waikiki Beach Resort in Hawaii" or "Nusa Dua Beach Resort in Bali" to the device.

[1999] The terminal displays these suggestions to the user.

[2000] Example prompt sentence:

[2001] User input: "I want to go to a beach resort"

[2002] Server processing: Learn user preferences and generate beach resort suggestions

[2003] Example suggestions: "Waikiki Beach Resort in Hawaii", "Nusa Dua Beach Resort in Bali"

[2004] In this way, the system can make optimal suggestions based on the user's needs and preferences.

[2005] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2006] Step 1:

[2007] A user uses a terminal to input travel needs and preferences. For example, the user inputs "I want to go to a beach resort." The input data is text data that specifically indicates the user's travel needs and preferences.

[2008] Step 2:

[2009] The device sends the entered data to the server as an HTTP POST request. The input is text data indicating the user's needs and preferences, and the output is the request data sent to the server.

[2010] Step 3:

[2011] The server stores the received data in a database, which also stores the user's previous input data. The input is the text data received as an HTTP request, and the output is the data stored in the database.

[2012] Step 4:

[2013] The server runs a machine learning model based on the stored data. The model learns the user's preferences and generates optimal suggestions. The input is the user's needs and preferences stored in the database, and the output is the suggestion data that is the result of learning. Specifically, the server uses TensorFlow to run the model and learn the user's preferences.

[2014] Step 5:

[2015] Based on the learning results, the server generates a list of itineraries, tourist spots, and restaurants that are optimal for the user. The input is the learning results of the machine learning model, and the output is a list of suggestions. Specifically, the server generates suggestions such as "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali."

[2016] Step 6:

[2017] The server sends the generated proposals to the device. The data is sent as an HTTP response. The input is the proposal list, and the output is the response data sent to the device.

[2018] Step 7:

[2019] The device displays the received suggestions to the user. The user can check the suggested list and select one. The input is the response data received from the server, and the output is the suggested list displayed to the user. Specifically, the device displays information about "Waikiki Beach Resort in Hawaii" and "Nusa Dua Beach Resort in Bali" on the app screen.

[2020] (Application example 2)

[2021] Next, a description will be given of Application Example 2 of Form 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."

[2022] Conventional personal travel assistant systems can suggest itineraries, tourist spots, and restaurants based on a customer's travel needs and preferences, but they have the problem of not being able to suggest restaurants or dishes that take into account a customer's food preferences or past ordering history.In addition to providing real-time flight information, weather information, and local traffic information, there is a demand for more personalized services by making suggestions based on a customer's food preferences.

[2023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2024] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, and means for learning the customer's food preferences and past order history and suggesting optimal restaurants and dishes, thereby making it possible to further personalize the customer's travel experience and suggest optimal restaurants and dishes based on the customer's food preferences.

[2025] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to suggest optimal itineraries, sightseeing spots, and restaurants based on each customer's travel needs and preferences.

[2026] "Means for inputting customer travel needs and preferences" means any interface or device through which a customer inputs their travel wishes and preferences into the system.

[2027] "Means of suggesting optimal itineraries, tourist spots, and restaurants based on input needs and preferences" refers to algorithms and software that suggest optimal travel plans, tourist spots, and places to eat based on information input by customers.

[2028] "Means of providing real-time flight information, weather information, and local traffic information" refers to systems and devices that obtain information such as current flight status, weather, and traffic conditions in real time and provide it to customers.

[2029] "Means of learning customers' food preferences and past order history and suggesting the most suitable restaurant and cuisine" refers to algorithms and software that use machine learning to analyze customers' food preferences and past order history, and then suggest the most suitable restaurant and cuisine based on that.

[2030] As an embodiment of the present invention, the following system can be constructed.

[2031] First, the server runs software to provide an AI-powered personal travel assistant. The server provides an interface for customers to input their travel needs and preferences. This interface is typically implemented as a smartphone application or a web application.

[2032] The server then runs an algorithm to suggest optimal itineraries, attractions, and restaurants based on the input needs and preferences. The algorithm uses machine learning to learn the customer's preferences and makes suggestions based on them. For example, if the server learns that the customer prefers beach resorts, it will suggest resorts near the beach.

[2033] Additionally, the server accesses databases and APIs that provide real-time flight, weather, and local traffic information, and provides this information to customers, allowing them to plan their trips based on the most up-to-date information.

[2034] The server also runs an algorithm that learns the customer's food preferences and past order history to suggest the most suitable restaurant and cuisine. This algorithm takes into account the customer's past order history and rating information to suggest restaurants and cuisines that best suit the customer's preferences. For example, if a customer inputs that they like "sushi," "Italian," and "spicy food," the server will suggest the best restaurant based on these preferences.

[2035] As a concrete example, by inputting the following prompt sentence into a generative AI model, optimal restaurant suggestions based on the customer's preferences can be obtained.

[2036] Example prompt sentence:

[2037] User Preferences: ["Sushi", "Italian", "Spicy Food"]

[2038] Restaurant Data:

[2039] {"name": "Sushi Taro", "description": "A restaurant serving fresh sushi"},

[2040] {"name": "Italian Mario", "description": "A restaurant where you can enjoy authentic Italian cuisine"},

[2041] {"name": "Spicy food specialty restaurant", "description": "A restaurant that specializes in spicy food"},

[2042] {"name": "Japanese restaurant", "description": "Restaurant serving traditional Japanese cuisine"},

[2043] {"name": "French restaurant", "description": "A restaurant where you can enjoy high-class French cuisine"}

[2044] ]

[2045] In this way, servers can better personalize a customer's travel experience and make optimal restaurant and cuisine recommendations based on the customer's food preferences.

[2046] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2047] Step 1:

[2048] Users input their travel needs and preferences through a smartphone or web application.

[2049] Input: User's travel needs and preferences (e.g., "beach resort," "sushi," "Italian")

[2050] Output: Data on input needs and preferences

[2051] Step 2:

[2052] The server receives the user's input data and inputs it into the machine learning model.

[2053] Input: Data about user needs and preferences

[2054] Output: The data input into the machine learning model

[2055] Step 3:

[2056] The server uses machine learning models to learn user preferences.

[2057] Input: Data about user needs and preferences

[2058] Output: A feature vector based on user preferences

[2059] Step 4:

[2060] The server generates optimal itineraries, sightseeing spots, and restaurant suggestions based on the user's preferences.

[2061] Input: A feature vector based on user preferences

[2062] Output: A list of recommended itineraries, attractions, and restaurants

[2063] Step 5:

[2064] The server retrieves real-time flight information, weather information, and local traffic information and provides it to the user.

[2065] Input: Real-time information obtained from an external API

[2066] Output: Latest flight, weather, and traffic information provided to the user

[2067] Step 6:

[2068] The server learns the user's food preferences and past ordering history and suggests the most suitable restaurant and dish.

[2069] Input: User's food preferences, past order history

[2070] Output: A list of recommendations for the best restaurants and dishes

[2071] Step 7:

[2072] The server inputs prompts into the generative AI model to get optimal restaurant suggestions based on the user's preferences.

[2073] Input: Prompt text (e.g., "User preferences: ["Sushi", "Italian", "Spicy food"]")

[2074] Output: Restaurant suggestions from a generative AI model

[2075] Step 8:

[2076] The server provides the final proposal to the user, allowing the user to make a selection.

[2077] Input: A list of the best itineraries, attractions, and restaurants

[2078] Output: The final list of suggestions provided to the user

[2079] Example 3

[2080] Next, a third embodiment of the third embodiment will be described. 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."

[2081] Traditional travel assistance systems lacked the ability to provide real-time information, making it difficult to quickly respond to problems customers faced during their trip. They also lacked the ability to provide appropriate alternatives for unexpected situations such as flight delays, weather changes, and traffic congestion. This meant customers had to spend a great deal of time and effort changing their travel plans.

[2082] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[2083] In this invention, the server includes a means for providing real-time flight information, weather information, and local traffic information, a means for integrating and analyzing the flight information, weather information, and traffic information, and a means for transmitting the analysis results to the customer's terminal, thereby enabling the customer to receive the latest information in real time and take appropriate action promptly.

[2084] An "AI-powered personal travel assistant" is a system that uses artificial intelligence technology to assist customers in planning their trips.

[2085] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[2086] "A means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, places to visit, and places to eat based on the customer's input needs and preferences.

[2087] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[2088] The "means for integrating and analyzing the flight information, weather information, and traffic information" is a function that brings together and analyzes the various types of information acquired to generate information that is useful to customers.

[2089] "Means for transmitting analysis results to the customer's terminal" is a function for transmitting analyzed information to the device used by the customer.

[2090] "Means for displaying information on the customer's terminal" refers to a function that visually displays the transmitted information on the customer's device.

[2091] "Means for customers to check information and take necessary actions" refers to an interface that allows customers to select and carry out necessary actions based on the displayed information.

[2092] This invention provides an AI-powered personal travel assistant system, which includes a means for inputting a customer's travel needs and preferences, a means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, and a means for providing real-time flight information, weather information, and local traffic information.

[2093] The server uses multiple APIs to collect real-time flight information, weather information, and local traffic information. Specifically, it uses the "Flight Information API" to obtain flight information, the "Weather Data API" to obtain weather information, and the "Traffic Information API" to obtain traffic information. By integrating and analyzing the data obtained from these APIs, it generates information that is useful to customers.

[2094] The server then sends the analysis results to the customer's device, including flight delay information, local weather information, traffic information, etc. This allows the customer to receive the latest information in real time.

[2095] The terminal displays the information received from the server to the user, such as flight delays, local weather, traffic congestion, etc. The terminal provides the information in a visually easy-to-understand format so that the user can easily check the information.

[2096] The user checks the information displayed on the terminal and takes the necessary action. For example, if a flight is delayed, the user can book an alternative flight or consider other means of transportation. The system also has the function of suggesting alternatives to the user.

[2097] As a concrete example, suppose a user has booked a flight from Tokyo to New York. If the flight is delayed, the server obtains the delay information from the "Flight Information API" and notifies the user's device. The server then obtains weather information for New York from the "Weather Data API" and provides it to the user. Finally, the server obtains local traffic information from the "Traffic Information API" and suggests alternative transportation options to the user.

[2098] Examples of prompts to be input to a generative AI model include:

[2099] Prompt: "If a flight from Tokyo to New York is delayed, suggest alternative flights or transportation."

[2100] By inputting this prompt sentence into the generative AI model, the system can provide the user with an appropriate alternative. The flow of the identification process in the third embodiment will be described with reference to FIG.

[2101] Step 1:

[2102] The server collects real-time data.

[2103] Input: Data from flight information API, weather data API, and traffic information API.

[2104] Specific operation: The server calls each API every minute to obtain the latest flight, weather, and traffic information. For example, it obtains flight delay information from the flight information API, weather information for the destination from the weather data API, and local traffic conditions from the traffic information API.

[2105] Output: Real-time data obtained.

[2106] Step 2:

[2107] The server consolidates and analyzes the data.

[2108] Input: Real-time data obtained in step 1.

[2109] How it works: The server integrates the acquired data into a single database. For example, if a flight is delayed, the server compares that information with weather and traffic data to analyze the cause and impact of the delay.

[2110] Output: Consolidated and analyzed data.

[2111] Step 3:

[2112] The server sends the analysis results to the device.

[2113] Input: Data integrated and analyzed in Step 2.

[2114] Specific operation: The server sends the analysis results in JSON format to the terminal. For example, if a flight is delayed, JSON data containing information about the delay time, cause, and alternative flight information is sent to the terminal.

[2115] Output: Analysis results sent to the device.

[2116] Step 4:

[2117] The terminal displays the information to the user.

[2118] Input: Analysis results submitted in step 3.

[2119] Specific operation: The device parses the received JSON data and displays it to the user in a visually understandable format, for example, highlighting flight delay information in red and displaying information about alternative flights in a list format.

[2120] Output: Information displayed to the user.

[2121] Step 5:

[2122] The user reviews the information and takes the necessary action.

[2123] Input: The information displayed in step 4.

[2124] Specific operation: The user checks the information displayed on the terminal and books an alternative flight if necessary. For example, if the flight is delayed by two hours, the user can select the alternative flight suggested by the system and proceed with the booking process.

[2125] Output: The action taken by the user.

[2126] (Application example 3)

[2127] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[2128] Conventional personal travel assistant systems provide real-time flight, weather, and local traffic information to assist customers in planning their trips, but there is a need for delivery services to utilize similar real-time information to improve delivery efficiency and avoid delays. In particular, food delivery services, which are easily affected by traffic congestion and bad weather, require optimal route suggestions based on real-time information and alternative routes in the event of delays. This will increase delivery efficiency and customer satisfaction.

[2129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[2130] In this invention, the server includes means for providing an AI-powered personal travel assistant, means for inputting a customer's travel needs and preferences, means for suggesting optimal itineraries, tourist spots, and restaurants based on the input needs and preferences, means for providing real-time flight information, weather information, and local traffic information, means for suggesting optimal routes based on real-time traffic and weather information, and means for suggesting alternative routes or handing over to another delivery person in the event of a delivery delay. This enables efficient route suggestions using real-time information and rapid response to delays in delivery work.

[2131] An "AI-powered personal travel assistant" is a system that uses artificial intelligence to help customers plan their trips.

[2132] "Means for inputting customer travel needs and preferences" means an interface through which a customer inputs their travel wishes and preferences into the system.

[2133] "Means of suggesting optimal itineraries, tourist spots, and restaurants" is a function that suggests optimal travel plans, tourist spots, and restaurants to customers based on their input needs and preferences.

[2134] "Means of providing real-time flight information, weather information, and local traffic information" refers to a function that obtains current flight status, weather, and traffic conditions in real time and provides them to customers.

[2135] "A means of suggesting the optimal route based on real-time traffic and weather information" is a function that suggests the optimal delivery route to delivery personnel, taking into account current traffic conditions and weather.

[2136] "Means to suggest alternative routes or handover to other delivery personnel in the event of a delivery delay" is a function that suggests alternative routes or handover to other delivery personnel in the event of a delivery delay.

[2137] The following system configuration will be described as an embodiment of the present invention.

[2138] System Configuration

[2139] The system consists of three main components: a server, a terminal, and a user. The server obtains real-time flight, weather, and local traffic information and calculates the optimal route based on this information. The terminal is a device such as a smartphone or tablet that the user uses to access the system. The user inputs their travel needs and preferences and receives suggestions from the system.

[2140] Program processing

[2141] The server processes the data in the following steps:

[2142] 1. Data Acquisition: The server acquires real-time data from the flight information API, weather information API, and traffic information API using the Python Requests library.

[2143] 2. Data analysis: Analyze the acquired data in JSON format and extract the necessary information.

[2144] 3. Route Calculation: Optimal routes are calculated based on real-time traffic and weather information, using AI models and machine learning algorithms.

[2145] 4. Proposal Generation: Proposes the calculated route and alternative routes to the user. In case of a delay, proposes an alternative route or a handover to another delivery person.

[2146] Hardware and software used

[2147] Hardware: smartphones, tablets, servers

[2148] Software: Python, Requests library, AI model (machine learning algorithm)

[2149] Specific examples

[2150] For example, if a user delivers food from Tokyo to Osaka, the server calculates the optimal route based on real-time traffic and weather information and suggests it to the device. If traffic congestion or bad weather is predicted, the server will suggest an alternative route or hand over to another delivery person.

[2151] Prompt Sentence Examples

[2152] "Enter your current location and destination, and we'll suggest the best route for you based on real-time traffic and weather information."

[2153] In this way, the system provides users with efficient delivery routes and allows for quick response in the event of delays.

[2154] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[2155] Step 1:

[2156] The server retrieves real-time data from flight information API, weather information API, and traffic information API.

[2157] Input: API endpoint and request parameters

[2158] Data processing: Sending API requests and receiving responses

[2159] Output: JSON data of flight information, weather information, and traffic information

[2160] Step 2:

[2161] The server analyzes the acquired JSON data and extracts th...

Claims

1. a means for a user to input their travel needs and preferences; means for the terminal to transmit input data relating to the user's travel needs and preferences to a server; A means for the server to input prompt sentences to a generative AI model, the prompt sentences being questions related to a travel plan to satisfy the user's travel needs and preferences; means for generating a travel plan for the generative AI model to satisfy the user's travel needs and preferences; means for transmitting the generated travel plan to the terminal by the server; A means for the user to check the travel plan on the terminal and input corrections or additional requests as necessary; A means for transmitting correction data related to a request for correction or addition by the terminal to the server; A means for the server to again input prompt sentences of questions about the travel plan to satisfy requests for correction or addition to the generation AI model, and generate an updated travel plan; means for the server to transmit the updated travel plan to the terminal; A system including:

2. The system of claim 1 , wherein the generative AI model includes means for analyzing user input data and generating an optimal travel plan.

3. The system of claim 1 , wherein the generative AI model includes means for making suggestions based on a user's past travel history and rating information.

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