System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring an image input by a user; means for analyzing the acquired image; means for generating a travel plan based on the analyzed information; and means for providing the generated travel plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] For many people, planning a trip requires time and effort, which can result in a loss of motivation to travel. Furthermore, planning a trip requires collecting and organizing a large amount of information, which can be difficult to achieve in busy daily lives. As a result, even if people want to go somewhere, they often cannot make concrete plans and end up abandoning the trip. The present invention aims to solve these problems and enable users to instantly plan a trip with motivation. [Means for solving the problem]

[0005] The present invention provides a system including a means for acquiring images input by a user, a means for analyzing the acquired images, a means for generating a travel plan based on the analyzed information, and a means for providing the generated travel plan to the user. The system may also include a means for identifying a destination and related tourist attractions based on the acquired images, a means for estimating a budget based on the travel plan, a means for generating a travel guide for the user through the provided travel plan, and a means for collecting detailed tourist information via the Internet based on the information extracted by the analysis means. This system allows users to easily obtain a detailed travel plan simply by inputting images that interest them, significantly reducing the effort required for travel and increasing motivation.

[0006] "User" refers to the individual or organization that inputs images and receives the generated itinerary.

[0007] "Image" refers to visual data such as a photograph or picture entered by the user.

[0008] "Means for acquiring" refers to devices or software for receiving and storing images entered by a user.

[0009] "Means for analyzing" refers to devices and software for analyzing the content of acquired images and extracting information therefrom.

[0010] "Means for generating" refers to the device or software that generates a travel plan based on the analyzed information.

[0011] The "means for providing" refers to a device or software that transmits the generated travel plan to the user.

[0012] A "travel plan" refers to a detailed plan of the schedule, attractions, budget, etc. required for a trip to a specific destination.

[0013] "Destination" refers to a location to which a user is traveling.

[0014] "Tourist spots" refers to tourist attractions and sights located at the destination and its surrounding areas.

[0015] "Via the Internet" refers to any method or means of collecting or transmitting information via the Internet.

[0016] A "travel guide" refers to a document or electronic data guide that summarizes the generated travel plan.

[0017] "Detailed tourist information" refers to specific information such as detailed descriptions of tourist attractions, how to get there, and prices.

[0018] "Budget" refers to the estimated cost of the entire trip. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] To implement this invention, a system is constructed that allows users to input images and generate and provide travel plans based on them. The following is a detailed program process and its explanation.

[0041] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0042] 1. Receiving image input

[0043] The user launches the dedicated application and selects or takes a picture of an image that interests them.

[0044] The terminal acquires the image and converts it into a data format for transmission to the server.

[0045] 2. Image Analysis

[0046] The server receives the received image data and passes it to the image analysis engine.

[0047] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[0048] 3. Travel plan generation

[0049] Based on the information identified, the AI ​​generates a travel plan, including destination attractions, accommodations, transportation options, and a specific itinerary.

[0050] The generative AI creates a detailed schedule for each day and estimates the budget based on this plan.

[0051] 4. Bookmark creation and provision

[0052] The generative AI creates a "travel itinerary" based on the generated travel plan.

[0053] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[0054] Specific examples

[0055] Example 1: Planning a beach trip

[0056] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0057] 2. The server receives the image and instructs the generating AI to analyze it.

[0058] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[0059] 4. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[0060] 5. The AI ​​generator creates daily schedules for things like the Blue Lagoon, sightseeing in Valletta, and a tour of Gozo.

[0061] 6. The generation AI calculates the budget for the entire trip and presents the total amount.

[0062] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[0063] 8. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0064] Example 2: Mountain trip planning

[0065] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0066] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[0067] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[0068] 4. The generation AI collects information on tourist spots in Arashiyama and generates a 3-day, 2-night travel plan based on that information.

[0069] 5. The AI ​​generator assigns tourist spots such as Tenryu-ji Temple, the Bamboo Forest Path, and Arashiyama Park to each day.

[0070] 6. The generative AI calculates the budget for the entire trip and presents the final amount.

[0071] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[0072] 8. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[0073] In this way, this system can automatically generate and provide detailed travel plans to users simply by inputting images, allowing users to easily plan their trips and stay motivated to travel.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0077] The user can either open the gallery from the app interface or launch the camera to capture an image.

[0078] Step 2:

[0079] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[0080] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[0081] Step 3:

[0082] The terminal transmits the image data to the server.

[0083] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[0084] Step 4:

[0085] The server prepares to analyze the received image data.

[0086] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[0087] Step 5:

[0088] The server instructs the generated AI to analyze the image.

[0089] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[0090] Step 6:

[0091] The generative AI analyzes the image features and extracts the information contained in the image.

[0092] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[0093] For example, "beach," "mountain," "city," etc. may be identified in this step.

[0094] Step 7:

[0095] Based on the analyzed information, the generative AI identifies the destination and surrounding tourist attractions.

[0096] The generative AI searches a database based on the recognized features and lists relevant tourist attractions and accommodations.

[0097] Step 8:

[0098] A server collects detailed tourist information via the Internet.

[0099] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[0100] Step 9:

[0101] The generative AI generates a travel plan based on the collected information.

[0102] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[0103] Step 10:

[0104] Generative AI calculates a budget based on your travel plans.

[0105] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[0106] Step 11:

[0107] The server generates a detailed "travel itinerary."

[0108] The server creates a "travel guide" as rich content including travel plans, details of tourist spots, budget information, maps, links, and more.

[0109] Step 12:

[0110] The server sends the generated "travel guide" to the user's terminal.

[0111] The server provides the bookmark data to the terminal in URL format or directly as data.

[0112] Step 13:

[0113] The terminal receives the "travel guide" and displays it to the user.

[0114] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[0115] Step 14:

[0116] The user checks the "Travel Itinerary" and customizes the content as needed.

[0117] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[0118] In this way, through the above steps, the system allows the user to easily receive a specific travel plan.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] Modern travelers face the challenge of finding the right travel destination and itinerary on their own from the vast amount of information available. Planning a trip, including selecting destinations and tourist spots, creating an itinerary, and estimating a budget, takes a lot of time and effort. Furthermore, customizing travel plans and meeting user needs is not easy.

[0122] 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.

[0123] In this invention, the server includes means for acquiring images input by a user, means for converting the acquired images into an optimal data format, means for transmitting the acquired images to the server, means for analyzing the images received by the server, means for identifying image features based on the analyzed information and using a generative AI model to determine destinations and tourist spots, means for generating a travel plan based on the identified information, means for estimating a budget based on the generated travel plan, and means for providing the generated travel plan to the user. This reduces the burden of travel planning because a user only needs to input an image to automatically generate a travel plan and be provided with a detailed itinerary and budget.

[0124] "User" means any person or entity that operates the System.

[0125] "Image" refers to visual data provided by a user through a dedicated application.

[0126] "Data format" refers to the particular format in which image data is processed by the server.

[0127] "Server" refers to a computing system for receiving, analyzing, processing data, and providing results.

[0128] "Image analysis" refers to technical methods used to identify features or content in image data.

[0129] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence technology to analyze images and data and automatically generate travel plans.

[0130] A "travel plan" refers to a detailed schedule including destinations, attractions, accommodations, and transportation methods that is generated based on the user's needs.

[0131] "Budgeting" refers to the process of calculating approximate costs based on travel plans.

[0132] A "travel itinerary" refers to a document or digital data that summarizes a generated travel plan.

[0133] "Analyzed information" refers to data such as destinations and tourist spots obtained through image analysis.

[0134] To implement this invention, we will build a system that allows users to input images and generate and provide travel plans based on them. This system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0135] Receiving image input

[0136] The user launches a dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for sending to the server. For example, the device converts the image into JPEG format and sends it to an API endpoint.

[0137] Image analysis

[0138] The server reviews the received image data and passes it to an image analysis engine. A generative AI model analyzes the image features and identifies the destination or tourist spot represented by the image. For example, a generative AI model (e.g., ResNet, YOLO) analyzes the visual features and generates a tag for "beach."

[0139] Identifying destinations and attractions

[0140] The generative AI model identifies the destination shown in the image based on the analysis results. For example, in the case of a beach, it identifies the "main island." The generative AI model then collects information about tourist attractions related to that destination from a database.

[0141] Generate a travel plan

[0142] The generative AI model creates a travel plan based on the collected information, including destination attractions, accommodation, transportation, and a specific schedule. For example, the generative AI model could automatically generate a four-day, three-night itinerary with a daily schedule that includes visiting the beach on the first day and sightseeing in the city on the second day.

[0143] Creating and sending bookmarks

[0144] The generative AI model generates a "travel itinerary" based on the travel plan. The server sends the generated travel itinerary to the user's device. The device displays a notification that the "travel itinerary" has been received, allowing the user to view it.

[0145] Hardware and software used

[0146] Device: Smartphone or tablet (e.g., iPhone, Android device)

[0147] Server: Cloud server environment (e.g. AWS, Google Cloud)

[0148] Generative AI models: Machine learning models (e.g., TensorFlow, PyTorch)

[0149] Prompt Sentence Examples

[0150] Here are some examples of prompts to input to a generative AI model:

[0151] "Analyze this beach image and generate the best itinerary."

[0152] "Generate a trip plan based on this mountain view image."

[0153] Specific examples

[0154] Planning a beach trip

[0155] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0156] 2. The server receives the image and instructs the generative AI model to analyze it.

[0157] 3. A generative AI model analyzes the image and recognizes the beach as a "prime location."

[0158] 4. The generative AI model collects tourist information about the destination and generates a 3-night, 4-day plan.

[0159] 5. The generative AI model creates a daily schedule and calculates the overall budget for the trip.

[0160] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[0161] 7. The user checks the "Travel Itinerary" and begins preparing for their trip.

[0162] Mountain Travel Planning

[0163] 1. A user finds a beautiful mountain scene with autumn leaves on social media and imports a screenshot into a dedicated app.

[0164] 2. The device sends the image to the server, which then requests the generative AI model to analyze it.

[0165] 3. A generative AI model analyzes the image and identifies the location as an "another prime location."

[0166] 4. The generative AI model collects tourist spot information and generates a 2-night, 3-day travel plan based on that information.

[0167] 5. The generative AI model allocates tourist attractions by day and calculates the overall budget for the trip.

[0168] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[0169] 7. The user checks the provided "Travel Itinerary" and begins preparing for their trip.

[0170] The above is an embodiment of this system.

[0171] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0172] Step 1:

[0173] Image acquisition

[0174] The user launches the dedicated application and selects or takes a picture of an image they are interested in. As the user operates the device, it retrieves image data from the photo library. The input here is the image selected or taken by the user, and this image becomes the output displayed within the application.

[0175] Step 2:

[0176] Converting to data format

[0177] The image data acquired by the terminal from the user is converted into the optimal data format for sending to the server. For example, the image data is converted into JPEG format. The input is the image data provided by the user, and the output is the converted JPEG format image.

[0178] Step 3:

[0179] Sending images

[0180] The device sends the converted image data to the server. The image data is sent as a POST request to a specific API endpoint. The input is the image data in JPEG format, and the output is the data sent to the server. The device displays a success message.

[0181] Step 4:

[0182] Image Receipt and Verification

[0183] The server checks the image data received from the terminal and verifies that there is no corruption or format mismatch. The input is the image data that arrived at the server, and the output is the verification result (success or error).

[0184] Step 5:

[0185] Image analysis request

[0186] The server passes image data to the image analysis engine and requests an analysis. The generative AI model analyzes the image's features and identifies the elements represented by the image (e.g., ocean, mountain, city). The input is the image data stored on the server, and the output is the feature extraction results.

[0187] Step 6:

[0188] Determining specific destinations and attractions

[0189] The generative AI model determines destinations and tourist spots based on the identified features. For example, if an image of a beach is used, it will identify "specific islands" and "tourist spots." The input is the feature extraction results, and the output is information on the identified destinations and tourist spots.

[0190] Step 7:

[0191] Generate a travel plan

[0192] The generative AI model generates a travel plan based on the identified destinations and tourist attractions. Specifically, it creates a schedule including accommodations, tourist attractions, and transportation. The input is the identified tourist attractions, and the output is a detailed travel plan.

[0193] Step 8:

[0194] Budget Estimates

[0195] The generative AI model estimates a budget based on a travel plan, calculating the cost of each item (e.g., accommodation, transportation) and calculating the total amount. The input is the details of the travel plan, and the output is the estimated budget.

[0196] Step 9:

[0197] Creating bookmarks

[0198] A generative AI model creates a "travel itinerary" based on a travel plan, including itinerary, attractions to visit, budget, etc. The input is a travel plan and estimated budget, and the output is a detailed itinerary.

[0199] Step 10:

[0200] Send a bookmark

[0201] The server sends the generated "travel itinerary" to the user's device. The user receives the bookmark and is notified so that they can view it. The input is the generated "travel itinerary," and the output is the result of transmission to the user's device.

[0202] Step 11:

[0203] Check bookmarks

[0204] The user checks the "travel itinerary" on the terminal and makes a final check of the details of the travel plan. At this stage, the user begins preparing for the trip. The input is the bookmark sent to the terminal, and the output is the bookmark confirmation result.

[0205] The above is a description of the specific processing steps.

[0206] (Application example 1)

[0207] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0208] Conventional travel plan generation systems analyze images entered by users to suggest specific travel destinations and tourist spots, but they are unable to accommodate diverse user needs, such as meal delivery plans. For this reason, there has been a demand for a system that allows users to easily create both travel plans and meal delivery plans.

[0209] 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.

[0210] In this invention, the server includes means for acquiring images input by the user, means for analyzing the acquired images, means for generating a travel plan or meal provision plan based on the analyzed information, and means for providing the generated travel plan or meal provision plan to the user. This allows the user to easily acquire both the travel plan and the meal provision plan, greatly improving convenience.

[0211] "User" refers to the individual or end user who inputs images using a dedicated application.

[0212] "Means for acquiring images" refers to an interface for importing images input or taken by the user into the system.

[0213] "Means for analyzing images" refers to an algorithm or engine that analyzes acquired image data and identifies or classifies its contents.

[0214] "Travel Plan" refers to a plan that suggests a schedule and budget including specific travel destinations and tourist attractions based on a user's input image.

[0215] A "meal plan" refers to a plan based on an image of a dish entered by a user, including the location where the dish will be served and delivery arrangements.

[0216] "Provision means" refers to a mechanism for sending the generated travel plan or meal plan to the user's terminal so that the user can check and use it.

[0217] "Means for identification" refers to a mechanism for identifying details of a location or dish based on the captured image and extracting related information.

[0218] "Delivery arrangements" refers to the procedure for delivering food to a designated location based on a meal provision plan.

[0219] To implement this invention, it is necessary to build a system that allows a user to input an image and generates and provides a travel plan or meal plan based on that image. This system is composed of the following processes.

[0220] Program Generation

[0221] The system is programmed as follows:

[0222] 1. Receiving image input

[0223] The user launches the dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for transmission to the server.

[0224] 2. Image Analysis

[0225] The server receives the received image data and passes it to an image analysis engine (e.g., OpenAI's CLIP model). The generative AI analyzes the image features and identifies the destination and cuisine depicted in the image.

[0226] 3. Travel or meal plan generation

[0227] Based on the information it identifies, the AI ​​generates a travel plan or meal plan. The travel plan includes destination attractions, accommodations, transportation, and a specific schedule. The meal plan includes identifying food locations and arranging delivery.

[0228] 4. Providing a plan

[0229] The server creates a "travel guide" or a "meal plan" based on the generated travel plan or meal plan. The server sends this information to the user's terminal, allowing the user to view it.

[0230] Hardware and Software Used

[0231] Hardware: Smartphone (iOS / Android)

[0232] Software: OpenAI CLIP model, Google Places API, Uber Eats API, Python, Flask, cloud server such as AWS (Amazon Web Services)

[0233] Specific examples

[0234] Travel plan execution example

[0235] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0236] 2. The server receives the image and instructs the generating AI to analyze it.

[0237] 3. The generative AI analyzes the image and recognizes that the beach is a specific tourist destination.

[0238] 4. The AI ​​generator collects tourist information about the relevant tourist destination and generates a 3-night, 4-day plan.

[0239] 5. Generative AI creates a detailed schedule for each day and calculates the budget for the entire trip.

[0240] 6. The server creates a "travel itinerary" and sends it to the device.

[0241] 7. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0242] Example of meal provision plan implementation

[0243] 1. The user uses a dedicated app to upload an image of paella, and the device sends the image to the server.

[0244] 2. The server receives the image and requests the generating AI to analyze it.

[0245] 3. The generative AI analyzes the image and recognizes the dish as "paella."

[0246] 4. The generating AI uses the Google Places API to identify the locations to serve.

[0247] 5. The generative AI uses the Uber Eats API to arrange food delivery based on the search results.

[0248] 6. The server sends the meal plan to the terminal, and the user confirms and completes the order.

[0249] Prompt Sentence Examples

[0250] Prompt: "What is the dish in this picture?"

[0251] Input image: A food image taken or uploaded by the user

[0252] In this way, the system can automatically generate and provide detailed travel plans and meal plans to users simply by inputting images, allowing users to easily make travel and meal arrangements.

[0253] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0254] Step 1:

[0255] The user launches the dedicated application and selects or takes a picture. The user uploads the image they are interested in to the application, and the device acquires the image. In this step, the image entered by the user is directly saved on the device and converted into a data format for processing.

[0256] Input: An image taken or selected by the user

[0257] Output: Image data saved on the device (converted to a data format)

[0258] Step 2:

[0259] The device sends the acquired image data to the server. Specifically, the device executes a procedure to upload the image data to the server via the Internet. This allows the image data to be stored on the server for analysis.

[0260] Input: Image data stored on the device

[0261] Output: Image data sent to the server

[0262] Step 3:

[0263] The server passes the received image data to an image analysis engine for analysis. The server then calls the analysis engine (e.g., OpenAI's CLIP model), receives the image data as input, and analyzes its features. Specifically, it performs image recognition and feature extraction.

[0264] Input: Image data sent to the server

[0265] Output: Image features (analyzed data)

[0266] Step 4:

[0267] The server identifies specific destinations or dishes based on the analyzed image features. In the case of travel plans, the server retrieves relevant location information from a database. In the case of meal delivery plans, the server identifies the names of dishes and their locations. This provides the basis for generating the next plan.

[0268] Input: Image features (analyzed data)

[0269] Output: Identified destination or cuisine information

[0270] Step 5:

[0271] The generation AI generates a travel plan or meal plan based on the identified information. The travel plan includes a schedule of tourist spots and a budget calculation, while the meal plan includes meal locations and delivery arrangements. Specific operations include creating a schedule with detailed destinations and suggesting delivery options.

[0272] Input: Specific destination or cuisine information

[0273] Output: Generated travel or meal plan

[0274] Step 6:

[0275] The server creates a "travel itinerary" or "meal plan" based on the generated plan and sends it to the user's device. The server then converts the results into a format that is easy for the user to view (such as text or PDF) and sends the data to the device.

[0276] Input: Generated travel or meal plan

[0277] Output: "Travel guide" or "Meal plan" (sent to user device)

[0278] Step 7:

[0279] The user checks the provided "travel itinerary" or "meal plan" and proceeds with travel or meal arrangements. Specific operations include checking the plan contents and making the necessary reservations and arrangements.

[0280] Input: "Travel guide" or "Meal plan" (sent to user device)

[0281] Output: Travel or dining arrangements confirmed by the user

[0282] 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.

[0283] To implement this invention, a system for generating travel plans based on user-entered images is combined with an emotion engine that recognizes the user's emotions and customizes the travel plans based on those emotions. The following is a detailed program process and its explanation for this system.

[0284] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0285] 1. Receiving image input

[0286] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0287] The terminal acquires the image and converts it into a data format for transmission to the server.

[0288] 2. Image Analysis

[0289] The server receives the received image data and passes it to the image analysis engine.

[0290] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[0291] 3. Emotion recognition

[0292] The device uses a camera and microphone to capture facial expressions and tone of voice to recognize the user's emotions.

[0293] The emotion engine analyzes the captured data and recognizes the user's emotions, for example, determining whether the user is excited or relaxed.

[0294] 4. Information Collection

[0295] Based on the information identified by the generative AI, a list of tourist attractions at and around the destination is created.

[0296] The server collects detailed tourist information (e.g., travel guide reviews, transportation options, accommodation information, etc.) via the Internet.

[0297] 5. Travel Plan Generation

[0298] Based on the information collected, the generative AI generates a travel plan that corresponds to the user's emotions.

[0299] For example, if the user is feeling relaxed, a leisurely schedule will be suggested, whereas if the user is feeling excited, a schedule including many active activities will be suggested.

[0300] 6. Budget Estimates

[0301] Generative AI will estimate a budget based on your travel plans, including transportation, accommodation, meals, etc.

[0302] 7. Bookmark Creation and Provision

[0303] The generative AI creates a "travel itinerary" based on the generated travel plan.

[0304] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[0305] Specific examples

[0306] Example 1: Planning a beach trip

[0307] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0308] 2. The server receives the image and instructs the generating AI to analyze it.

[0309] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[0310] 4. The emotion engine identifies the user's emotions and recognizes that the user wants to relax.

[0311] 5. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[0312] 6. Generative AI suggests relaxing itineraries, creating plans that include hot spring resorts and relaxing activities, for example.

[0313] 7. The generation AI calculates the budget for the entire trip and presents the total amount.

[0314] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[0315] 9. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0316] Example 2: Mountain trip planning

[0317] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0318] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[0319] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[0320] 4. The emotion engine identifies the user's emotions and recognizes that the user is looking for an exciting activity.

[0321] 5. The generation AI collects information on tourist spots in Arashiyama and generates a 2-night, 3-day travel plan based on that information.

[0322] 6. Generative AI suggests active itineraries and sets out plans that include, for example, hikes or bike tours.

[0323] 7. The generative AI calculates the budget for the entire trip and presents the final amount.

[0324] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[0325] 9. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[0326] In this way, by combining the emotion engine, the system can provide more personalized travel plans according to the user's emotions, allowing users to have a more satisfying travel experience.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0330] The user can either open the gallery from the app interface or launch the camera to capture an image.

[0331] Step 2:

[0332] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[0333] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[0334] Step 3:

[0335] The terminal transmits the image data to the server.

[0336] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[0337] Step 4:

[0338] The server prepares to analyze the received image data.

[0339] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[0340] Step 5:

[0341] The server instructs the generated AI to analyze the image.

[0342] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[0343] Step 6:

[0344] The generative AI analyzes the image features and extracts the information contained in the image.

[0345] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[0346] For example, "beach," "mountain," "city," etc. may be identified in this step.

[0347] Step 7:

[0348] The device uses a camera and microphone to capture the user's facial expressions and tone of voice to recognize their emotions.

[0349] The device will use facial recognition and voice analysis technologies to more accurately understand the user's current emotional state.

[0350] Step 8:

[0351] The emotion engine analyzes the captured data and recognizes the user's emotions.

[0352] The emotion engine determines whether the user is excited, relaxed, or in some other emotional state.

[0353] Step 9:

[0354] The generative AI identifies the destination and surrounding tourist attractions based on the analyzed image information and emotion data from the emotion engine.

[0355] The generative AI searches a database based on the recognized features and emotion data to list relevant tourist attractions and accommodations.

[0356] Step 10:

[0357] A server collects detailed tourist information via the Internet.

[0358] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[0359] Step 11:

[0360] The generative AI generates a travel plan based on the collected information.

[0361] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[0362] The generative AI selects activities and locations that match the user's emotions.

[0363] Step 12:

[0364] Generative AI calculates a budget based on your travel plans.

[0365] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[0366] Step 13:

[0367] Generative AI generates a detailed "travel itinerary."

[0368] The generative AI creates a "travel itinerary" as rich content including travel plans, details of tourist attractions, budget information, maps, links, etc.

[0369] Step 14:

[0370] The server sends the generated "travel guide" to the user's terminal.

[0371] The server provides the bookmark data to the terminal in URL format or directly as data.

[0372] Step 15:

[0373] The terminal receives the "travel guide" and displays it to the user.

[0374] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[0375] Step 16:

[0376] The user checks the "Travel Itinerary" and customizes the content as needed.

[0377] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[0378] Through these steps, the system allows users to easily receive specific travel plans. By combining it with an emotion engine, it is possible to provide more personalized travel plans that reflect the user's emotions.

[0379] Example 2

[0380] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] Conventional travel planning systems often generate uniform travel plans based on user-entered information, without taking into account the individual user's emotions or mood at the time. As a result, the travel plans may not meet the user's expectations, resulting in low satisfaction. Furthermore, they lack the ability to flexibly customize plans based on the user's emotions or provide specific travel budget estimates.

[0382] 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.

[0383] In this invention, the server includes means for acquiring an image input by a user, means for transmitting the acquired image to the server, means for the server to analyze the image, means for the terminal to recognize the user's emotion, means for customizing a travel plan based on the emotion, and means for providing the generated travel plan to the user, thereby enabling the generation of a more personalized travel plan based on the user's emotion.

[0384] "User" refers to an individual or organization that uses the system to make travel plans.

[0385] "Image" refers to digital data containing visual information captured or selected by a user, and is saved in formats such as JPEG or PNG.

[0386] "Terminal" refers to a computer device or mobile device operated by a user, which performs tasks such as acquiring images and collecting emotional data.

[0387] "Server" refers to the computing resources and software for receiving and analyzing image data, and generating and providing itineraries.

[0388] An "emotion engine" refers to a hardware and software system that analyzes and recognizes a user's emotional state from facial expressions, tone of voice, etc.

[0389] "Travel Itinerary" refers to a detailed plan including a user's travel destinations, schedule, activities, and estimated budget.

[0390] "Analysis" refers to the process of processing the acquired image data and emotion data, extracting features, and analyzing them.

[0391] "Features" refer to significant data patterns and attributes extracted from image and emotion data, and are elements that identify destinations, user emotions, etc.

[0392] "Tourist attractions" refer to places and facilities that tourists visit, including tourist destinations and activities.

[0393] "Customization" refers to the process of individually adjusting and modifying a travel plan based on the user's feelings and desires.

[0394] A "bookmark" is a document or digital file that details your ultimate travel plans, including schedules and important information.

[0395] MODE FOR CARRYING OUT THE INVENTION

[0396] To implement this invention, a system including a user terminal, a server, an image analysis engine, an emotion engine, and a generative AI model is required. This system generates a travel plan based on images entered by the user and analyzes the user's emotions to customize the travel plan.

[0397] 1. Receiving image input

[0398] The user launches a dedicated application and selects or takes a photo of the image that will trigger the trip. The device acquires the image and converts it into a data format (e.g., JPEG or PNG format) for transmission to the server.

[0399] 2. Sending and receiving images

[0400] The device sends image data to a server via the Internet, and the server receives the image data and temporarily stores it.

[0401] 3. Image Analysis

[0402] The image data stored on the server is input into an image analysis engine (e.g., TensorFlow or OpenCV). The generative AI extracts image features and identifies the destination or tourist spot indicated by the image. For example, it can recognize an image of a beach as a "famous tourist spot."

[0403] 4. Preparation for Emotion Recognition

[0404] The device activates the camera and microphone to recognize the user's emotions, capturing the user's facial expressions and tone of voice and collecting data in real time.

[0405] 5. Emotion analysis

[0406] The device sends the captured data to an emotion engine (e.g., Microsoft Azure Emotion API or Face API), which analyzes the data and recognizes the user's emotion. For example, it determines whether the user is relaxed or excited.

[0407] 6. Information Collection

[0408] Based on the destination specified, the AI ​​collects tourist attractions and nearby activities from the internet. For example, it uses travel guide review sites and web services. The server organizes the necessary information and passes it to the AI.

[0409] 7. Travel Plan Generation

[0410] The generative AI customizes travel plans based on the collected information and the user's emotional state. For example, if the user wants to relax, it will suggest a schedule that includes hot spring resorts and spas. If the user is excited, it will create a schedule that includes more active activities like hiking and surfing.

[0411] 8. Budget Estimates

[0412] The generation AI estimates costs based on the travel plan, including transportation, accommodation, and meals, and then sends the estimate to the server.

[0413] 9. Bookmark Creation and Provision

[0414] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. The itinerary includes the entire trip schedule, important contact information, and a checklist. The server then sends this "travel itinerary" to the user's device in PDF format or other format. The user can then view the "travel itinerary" through the application and begin preparing for their trip.

[0415] Specific examples

[0416] Example 1: Planning a beach trip

[0417] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device converts the image to JPEG format.

[0418] 2. The device sends a JPEG image to the server, which receives and saves the image.

[0419] 3. The server uses an image analysis engine (e.g., TensorFlow) to extract image features.

[0420] 4. The generative AI analyzes the image of the beach and identifies it as a "famous tourist destination."

[0421] 5. The device activates the camera and captures the user's facial expression.

[0422] 6. The emotion engine analyzes the user's facial expression data and recognizes that the user wants to relax.

[0423] 7. The generation AI collects tourist information from the internet and generates a 3-night, 4-day travel plan.

[0424] 8. Generative AI suggests a relaxing itinerary that includes hot spring resorts and spas.

[0425] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[0426] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[0427] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[0428] Example 2: Mountain trip planning

[0429] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0430] 2. The device sends the screenshot to the server, which receives and saves the image.

[0431] 3. The server uses an image analysis engine (e.g., OpenCV) to extract image features.

[0432] 4. The generative AI analyzes the image and identifies the location as a "famous tourist spot."

[0433] 5. The device activates the microphone and captures the tone of the user's voice.

[0434] 6. The emotion engine analyzes tone of voice data and recognizes that the user is in an excited mood.

[0435] 7. The generation AI collects tourist spot information from the internet and generates a 2-night, 3-day travel plan.

[0436] 8. Generative AI suggests active itineraries including hikes and bike tours.

[0437] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[0438] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[0439] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[0440] Prompt Sentence Examples

[0441] "I uploaded a beach image and want you to generate a relaxing itinerary based on it."

[0442] "I captured a mountain scene of autumn leaves from Instagram. Please suggest some exciting travel plans."

[0443] In this way, users can easily obtain a personalized travel plan that matches their emotions at the time. This system is expected to make users' travel experiences more satisfying.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] The user launches the dedicated application and selects or takes a photo that will trigger the trip. The user's operation is the input, and an image file is obtained as the output. The specific operation is to launch the camera function within the application and support taking photos or selecting images from the gallery.

[0447] Step 2:

[0448] The image acquired by the device is converted to JPEG or PNG format and sent to the server. The input is an image selected or taken by the user, and the output is image data sent to the server. Specifically, the data format is converted using an image conversion library, and the data is uploaded to the server using the HTTP protocol.

[0449] Step 3:

[0450] The server temporarily stores the image data it receives. The input is image data sent from the terminal, and the output is a saved image file. Specifically, it saves the image in the file system and records the storage location in the database.

[0451] Step 4:

[0452] The server inputs the stored image data into the image analysis engine. The stored image data is the input, and image feature data is obtained as the output. Specifically, TensorFlow or OpenCV is used to extract image features and identify tourist spots and destinations.

[0453] Step 5:

[0454] The device activates the camera and microphone to recognize the user's emotions. The input is real-time data of the user's facial expressions and voice, and the output is captured emotional data. Specifically, the camera captures facial expressions and the microphone records the tone of voice.

[0455] Step 6:

[0456] The device sends the captured data to the emotion engine, which then analyzes the user's emotions. The input is captured facial expression data and voice data, and the output is analyzed emotional state data. Specific operations include recognizing emotions using the Microsoft Azure Emotion API or Face API.

[0457] Step 7:

[0458] Based on the identified destination, the generative AI collects information on tourist attractions and nearby activities from the internet. The input is the identified destination information and emotional state data, and the output is a list of tourist information. Specifically, it uses web services and APIs to collect information from tourist guide sites.

[0459] Step 8:

[0460] The generative AI customizes a travel plan based on the collected information and the user's emotional state. The input is a list of tourist information and emotional state data, and the output is a customized travel plan. Specifically, it generates a schedule based on the information and suggests activities according to the user's emotional state.

[0461] Step 9:

[0462] The generative AI estimates costs based on a travel plan. It takes a customized travel plan as input and provides a budget estimate as output. Specifically, it calculates transportation, accommodation, meal costs, etc., and calculates the total amount.

[0463] Step 10:

[0464] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. It takes a customized travel plan and budget estimate as input, and produces a completed "travel itinerary" as output. Specifically, it compiles the entire trip schedule, important contacts, checklists, and more into a document.

[0465] Step 11:

[0466] The server sends the "Travel Itinerary" to the user's device in PDF format or other format. The input is the completed "Travel Itinerary," and the output is the data sent to the user's device. Specific operations include sending the "Travel Itinerary" via email or in-app notifications.

[0467] Step 12:

[0468] The user checks the "travel itinerary" through the application and begins preparing for the trip. The "travel itinerary" is the input, and the trip preparations are completed as the output. The specific operation is to open the "travel itinerary" on the device and check the schedule and activities.

[0469] (Application example 2)

[0470] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] Conventional food delivery systems have the problem that it is difficult for users to easily find the appropriate meal based on their preferences and emotions. In addition, suggestions based on food images are limited, and personalized suggestions that take into account the user's individual emotions and moods are not possible. To solve this problem, a system is needed that can customize meal plans based on the images entered by the user and their current emotions, and suggest appropriate restaurants and food delivery services.

[0472] 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.

[0473] In this invention, the server includes means for acquiring an image input by a user, means for analyzing the acquired image, means for generating a meal provision plan based on the analyzed information, means for recognizing the user's emotions and customizing the meal provision plan based on the acquired emotion information, and means for providing the generated meal provision plan to the user. This makes it possible to easily generate and provide a personalized meal provision plan that meets the user's wishes and emotions.

[0474] "User" means an individual or organization that uses this system.

[0475] "Image" refers to visual data stored in digital form.

[0476] "Acquisition" refers to the process of gathering or receiving information or data.

[0477] "Analysis" refers to the process of unraveling the content of data or information and finding meaning.

[0478] "Meal Plan" means a schedule or plan that includes specific dish or meal suggestions.

[0479] "Emotion" refers to data that indicates a user's psychological state and mood.

[0480] "Customization" refers to the process of modifying or optimizing content based on the needs and desires of an individual user.

[0481] "Providing" refers to delivering information or services to users.

[0482] "System" refers to a technological device or application that includes multiple components and functions that work in conjunction with each other.

[0483] To put this invention into practice, it is necessary to build a system that allows users to input images of the food they want to eat, generates a meal plan based on those images, and customizes it according to the user's emotions. The detailed configuration and specific operating procedures of this system are described below.

[0484] Hardware and software used

[0485] Hardware: Smartphone (including camera and microphone)

[0486] Software: Image analysis engine (e.g., Google Cloud Vision API), emotion recognition engine (e.g., Microsoft Azure Face API), generative AI (OpenAI GPT-4), server for data processing and management

[0487] Detailed system configuration

[0488] 1. To receive image input, the user selects or takes a picture of the food they want to eat using the smartphone camera and inputs the image into the application. The device converts the image data into JSON format and sends it to the server.

[0489] 2. Image analysis uses the Google Cloud Vision API to analyze the image data received by the server, and the type and characteristics of the food are identified as a result of the analysis.

[0490] 3. Emotion recognition uses the camera and microphone on the user's smartphone to capture the user's facial expressions and tone of voice, and then uses the Microsoft Azure Face API to analyze the user's emotions, for example, to determine whether they are feeling relaxed or excited.

[0491] 4. Information gathering and suggestion generation are performed using generative AI (OpenAI GPT-4) based on the analysis results. For example, restaurant and menu suggestions are generated using the following prompt:

[0492] If the user wants to relax:

[0493] text

[0494] Suggest restaurants delivering sushi for someone feeling relaxed.

[0495] If the user is in an exciting mood:

[0496] text

[0497] Suggest restaurants delivering spicy Mexican food for someone feeling excited.

[0498] 5. The generation and provision of meal plan involves the generation AI generating an appropriate meal plan based on the proposed content, and the server sending this plan to the user's device and providing it in a format that is easy for the user to check.

[0499] Specific examples

[0500] For example, a user takes a picture of "sushi" with their smartphone and inputs it into a "Smart Food Delivery" app. The system analyzes the image with Google Cloud Vision API, identifying "sushi" as the result. It then uses Microsoft Azure Face API to recognize the user's emotions and determine whether they are in a relaxed mood. OpenAI GPT-4 suggests suitable restaurants using the following prompt:

[0501] text

[0502] Suggest restaurants delivering sushi for someone feeling relaxed.

[0503] This presents the user with a delivery service that offers home-style sushi.

[0504] The present invention can generate a personalized meal plan based on the user's emotions and meal images, providing a suitable dining experience.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] A user launches the app on their smartphone and selects or takes a picture of the food they want to eat. The device converts this image data into an appropriate data format (e.g., JSON) and sends it to the server.

[0508] Input: Food image

[0509] Output: Image data sent to the server (JSON format)

[0510] Specific behavior:

[0511] The user takes a picture of the dish with the camera, selects the image on the app, and presses the upload button. The application converts the image data into JSON format and sends it to the server.

[0512] Step 2:

[0513] The server acquires the received image data and analyzes it using an image analysis engine (Google Cloud Vision API). As a result of the analysis, the type and characteristics of the food are identified from the image features.

[0514] Input: Image data sent to the server (JSON format)

[0515] Output: Food type and characteristics (e.g., sushi, pizza)

[0516] Specific behavior:

[0517] The server calls the Google Cloud Vision API, analyzes the characteristics of the food based on the received image data, and obtains the analysis results in JSON format to identify the type of food.

[0518] Step 3:

[0519] The device uses the user's smartphone camera and microphone to capture the user's facial expressions and tone of voice, and then analyzes emotions using an emotion recognition engine (Microsoft Azure Face API).

[0520] Input: User facial and voice data

[0521] Output: User's emotional state (e.g., relaxed, excited)

[0522] Specific behavior:

[0523] The user looks into the smartphone camera to capture their facial expressions and the tone of their voice with the microphone, which are then sent to the Azure Face API, which then sends the analysis results back to the server.

[0524] Step 4:

[0525] Based on the analyzed food information and emotional information, the server uses generative AI (OpenAI GPT-4) to generate restaurant and food suggestions suitable for the user.

[0526] Input: type and characteristics of food, user's emotional state

[0527] Output: A list of suggested restaurants and dishes

[0528] Specific behavior:

[0529] The server queries the AI ​​generator with a prompt based on the analysis information. The AI ​​generator generates a list of restaurants and dishes based on the prompt, and the server retrieves the results.

[0530] Step 5:

[0531] The server transmits the generated meal plan to the user's terminal, and the user confirms it.

[0532] Input: A list of suggested restaurants and dishes

[0533] Output: Meal plan displayed on user's device

[0534] Specific behavior:

[0535] The generated plan is sent to the user's device in JSON format. The application parses the data and displays it to the user as a list or detailed information. The user can then review the proposed information and place an order.

[0536] 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.

[0537] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0538] 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.

[0539] [Second embodiment]

[0540] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0541] 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.

[0542] 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).

[0543] 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.

[0544] 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.

[0545] 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).

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0552] To implement this invention, a system is constructed that allows users to input images and generate and provide travel plans based on them. The following is a detailed program process and its explanation.

[0553] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0554] 1. Receiving image input

[0555] The user launches the dedicated application and selects or takes a picture of an image that interests them.

[0556] The terminal acquires the image and converts it into a data format for transmission to the server.

[0557] 2. Image Analysis

[0558] The server receives the received image data and passes it to the image analysis engine.

[0559] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[0560] 3. Travel plan generation

[0561] Based on the information identified, the AI ​​generates a travel plan, including destination attractions, accommodations, transportation options, and a specific itinerary.

[0562] The generative AI creates a detailed schedule for each day and estimates the budget based on this plan.

[0563] 4. Bookmark creation and provision

[0564] The generative AI creates a "travel itinerary" based on the generated travel plan.

[0565] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[0566] Specific examples

[0567] Example 1: Planning a beach trip

[0568] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0569] 2. The server receives the image and instructs the generating AI to analyze it.

[0570] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[0571] 4. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[0572] 5. The AI ​​generator creates daily schedules for things like the Blue Lagoon, sightseeing in Valletta, and a tour of Gozo.

[0573] 6. The generation AI calculates the budget for the entire trip and presents the total amount.

[0574] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[0575] 8. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0576] Example 2: Mountain trip planning

[0577] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0578] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[0579] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[0580] 4. The generation AI collects information on tourist spots in Arashiyama and generates a 3-day, 2-night travel plan based on that information.

[0581] 5. The AI ​​generator assigns tourist spots such as Tenryu-ji Temple, the Bamboo Forest Path, and Arashiyama Park to each day.

[0582] 6. The generative AI calculates the budget for the entire trip and presents the final amount.

[0583] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[0584] 8. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[0585] In this way, this system can automatically generate and provide detailed travel plans to users simply by inputting images, allowing users to easily plan their trips and stay motivated to travel.

[0586] The processing flow will be explained below.

[0587] Step 1:

[0588] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0589] The user can either open the gallery from the app interface or launch the camera to capture an image.

[0590] Step 2:

[0591] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[0592] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[0593] Step 3:

[0594] The terminal transmits the image data to the server.

[0595] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[0596] Step 4:

[0597] The server prepares to analyze the received image data.

[0598] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[0599] Step 5:

[0600] The server instructs the generated AI to analyze the image.

[0601] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[0602] Step 6:

[0603] The generative AI analyzes the image features and extracts the information contained in the image.

[0604] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[0605] For example, "beach," "mountain," "city," etc. may be identified in this step.

[0606] Step 7:

[0607] Based on the analyzed information, the generative AI identifies the destination and surrounding tourist attractions.

[0608] The generative AI searches a database based on the recognized features and lists relevant tourist attractions and accommodations.

[0609] Step 8:

[0610] A server collects detailed tourist information via the Internet.

[0611] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[0612] Step 9:

[0613] The generative AI generates a travel plan based on the collected information.

[0614] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[0615] Step 10:

[0616] Generative AI calculates a budget based on your travel plans.

[0617] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[0618] Step 11:

[0619] The server generates a detailed "travel itinerary."

[0620] The server creates a "travel guide" as rich content including travel plans, details of tourist spots, budget information, maps, links, and more.

[0621] Step 12:

[0622] The server sends the generated "travel guide" to the user's terminal.

[0623] The server provides the bookmark data to the terminal in URL format or directly as data.

[0624] Step 13:

[0625] The terminal receives the "travel guide" and displays it to the user.

[0626] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[0627] Step 14:

[0628] The user checks the "Travel Itinerary" and customizes the content as needed.

[0629] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[0630] In this way, through the above steps, the system allows the user to easily receive a specific travel plan.

[0631] Example 1

[0632] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] Modern travelers face the challenge of finding the right travel destination and itinerary on their own from the vast amount of information available. Planning a trip, including selecting destinations and tourist spots, creating an itinerary, and estimating a budget, takes a lot of time and effort. Furthermore, customizing travel plans and meeting user needs is not easy.

[0634] 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.

[0635] In this invention, the server includes means for acquiring images input by a user, means for converting the acquired images into an optimal data format, means for transmitting the acquired images to the server, means for analyzing the images received by the server, means for identifying image features based on the analyzed information and using a generative AI model to determine destinations and tourist spots, means for generating a travel plan based on the identified information, means for estimating a budget based on the generated travel plan, and means for providing the generated travel plan to the user. This reduces the burden of travel planning because a user only needs to input an image to automatically generate a travel plan and be provided with a detailed itinerary and budget.

[0636] "User" means any person or entity that operates the System.

[0637] "Image" refers to visual data provided by a user through a dedicated application.

[0638] "Data format" refers to the particular format in which image data is processed by the server.

[0639] "Server" refers to a computing system for receiving, analyzing, processing data, and providing results.

[0640] "Image analysis" refers to technical methods used to identify features or content in image data.

[0641] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence technology to analyze images and data and automatically generate travel plans.

[0642] A "travel plan" refers to a detailed schedule including destinations, attractions, accommodations, and transportation methods that is generated based on the user's needs.

[0643] "Budgeting" refers to the process of calculating approximate costs based on travel plans.

[0644] A "travel itinerary" refers to a document or digital data that summarizes a generated travel plan.

[0645] "Analyzed information" refers to data such as destinations and tourist spots obtained through image analysis.

[0646] To implement this invention, we will build a system that allows users to input images and generate and provide travel plans based on them. This system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0647] Receiving image input

[0648] The user launches a dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for sending to the server. For example, the device converts the image into JPEG format and sends it to an API endpoint.

[0649] Image analysis

[0650] The server reviews the received image data and passes it to an image analysis engine. A generative AI model analyzes the image features and identifies the destination or tourist spot represented by the image. For example, a generative AI model (e.g., ResNet, YOLO) analyzes the visual features and generates a tag for "beach."

[0651] Identifying destinations and attractions

[0652] The generative AI model identifies the destination shown in the image based on the analysis results. For example, in the case of a beach, it identifies the "main island." The generative AI model then collects information about tourist attractions related to that destination from a database.

[0653] Generate a travel plan

[0654] The generative AI model creates a travel plan based on the collected information, including destination attractions, accommodation, transportation, and a specific schedule. For example, the generative AI model could automatically generate a four-day, three-night itinerary with a daily schedule that includes visiting the beach on the first day and sightseeing in the city on the second day.

[0655] Creating and sending bookmarks

[0656] The generative AI model generates a "travel itinerary" based on the travel plan. The server sends the generated travel itinerary to the user's device. The device displays a notification that the "travel itinerary" has been received, allowing the user to view it.

[0657] Hardware and software used

[0658] Device: Smartphone or tablet (e.g., iPhone, Android device)

[0659] Server: Cloud server environment (e.g. AWS, Google Cloud)

[0660] Generative AI models: Machine learning models (e.g., TensorFlow, PyTorch)

[0661] Prompt Sentence Examples

[0662] Here are some examples of prompts to input to a generative AI model:

[0663] "Analyze this beach image and generate the best itinerary."

[0664] "Generate a trip plan based on this mountain view image."

[0665] Specific examples

[0666] Planning a beach trip

[0667] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0668] 2. The server receives the image and instructs the generative AI model to analyze it.

[0669] 3. A generative AI model analyzes the image and recognizes the beach as a "prime location."

[0670] 4. The generative AI model collects tourist information about the destination and generates a 3-night, 4-day plan.

[0671] 5. The generative AI model creates a daily schedule and calculates the overall budget for the trip.

[0672] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[0673] 7. The user checks the "Travel Itinerary" and begins preparing for their trip.

[0674] Mountain Travel Planning

[0675] 1. A user finds a beautiful mountain scene with autumn leaves on social media and imports a screenshot into a dedicated app.

[0676] 2. The device sends the image to the server, which then requests the generative AI model to analyze it.

[0677] 3. A generative AI model analyzes the image and identifies the location as an "another prime location."

[0678] 4. The generative AI model collects tourist spot information and generates a 2-night, 3-day travel plan based on that information.

[0679] 5. The generative AI model allocates tourist attractions by day and calculates the overall budget for the trip.

[0680] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[0681] 7. The user checks the provided "Travel Itinerary" and begins preparing for their trip.

[0682] The above is an embodiment of this system.

[0683] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0684] Step 1:

[0685] Image acquisition

[0686] The user launches the dedicated application and selects or takes a picture of an image they are interested in. As the user operates the device, it retrieves image data from the photo library. The input here is the image selected or taken by the user, and this image becomes the output displayed within the application.

[0687] Step 2:

[0688] Converting to data format

[0689] The image data acquired by the terminal from the user is converted into the optimal data format for sending to the server. For example, the image data is converted into JPEG format. The input is the image data provided by the user, and the output is the converted JPEG format image.

[0690] Step 3:

[0691] Sending images

[0692] The device sends the converted image data to the server. The image data is sent as a POST request to a specific API endpoint. The input is the image data in JPEG format, and the output is the data sent to the server. The device displays a success message.

[0693] Step 4:

[0694] Image Receipt and Verification

[0695] The server checks the image data received from the terminal and verifies that there is no corruption or format mismatch. The input is the image data that arrived at the server, and the output is the verification result (success or error).

[0696] Step 5:

[0697] Image analysis request

[0698] The server passes image data to the image analysis engine and requests an analysis. The generative AI model analyzes the image's features and identifies the elements represented by the image (e.g., ocean, mountain, city). The input is the image data stored on the server, and the output is the feature extraction results.

[0699] Step 6:

[0700] Determining specific destinations and attractions

[0701] The generative AI model determines destinations and tourist spots based on the identified features. For example, if an image of a beach is used, it will identify "specific islands" and "tourist spots." The input is the feature extraction results, and the output is information on the identified destinations and tourist spots.

[0702] Step 7:

[0703] Generate a travel plan

[0704] The generative AI model generates a travel plan based on the identified destinations and tourist attractions. Specifically, it creates a schedule including accommodations, tourist attractions, and transportation. The input is the identified tourist attractions, and the output is a detailed travel plan.

[0705] Step 8:

[0706] Budget Estimates

[0707] The generative AI model estimates a budget based on a travel plan, calculating the cost of each item (e.g., accommodation, transportation) and calculating the total amount. The input is the details of the travel plan, and the output is the estimated budget.

[0708] Step 9:

[0709] Creating bookmarks

[0710] A generative AI model creates a "travel itinerary" based on a travel plan, including itinerary, attractions to visit, budget, etc. The input is a travel plan and estimated budget, and the output is a detailed itinerary.

[0711] Step 10:

[0712] Send a bookmark

[0713] The server sends the generated "travel itinerary" to the user's device. The user receives the bookmark and is notified so that they can view it. The input is the generated "travel itinerary," and the output is the result of transmission to the user's device.

[0714] Step 11:

[0715] Check bookmarks

[0716] The user checks the "travel itinerary" on the terminal and makes a final check of the details of the travel plan. At this stage, the user begins preparing for the trip. The input is the bookmark sent to the terminal, and the output is the bookmark confirmation result.

[0717] The above is a description of the specific processing steps.

[0718] (Application example 1)

[0719] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0720] Conventional travel plan generation systems analyze images entered by users to suggest specific travel destinations and tourist spots, but they are unable to accommodate diverse user needs, such as meal delivery plans. For this reason, there has been a demand for a system that allows users to easily create both travel plans and meal delivery plans.

[0721] 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.

[0722] In this invention, the server includes means for acquiring images input by the user, means for analyzing the acquired images, means for generating a travel plan or meal provision plan based on the analyzed information, and means for providing the generated travel plan or meal provision plan to the user. This allows the user to easily acquire both the travel plan and the meal provision plan, greatly improving convenience.

[0723] "User" refers to the individual or end user who inputs images using a dedicated application.

[0724] "Means for acquiring images" refers to an interface for importing images input or taken by the user into the system.

[0725] "Means for analyzing images" refers to an algorithm or engine that analyzes acquired image data and identifies or classifies its contents.

[0726] "Travel Plan" refers to a plan that suggests a schedule and budget including specific travel destinations and tourist attractions based on a user's input image.

[0727] A "meal plan" refers to a plan based on an image of a dish entered by a user, including the location where the dish will be served and delivery arrangements.

[0728] "Provision means" refers to a mechanism for sending the generated travel plan or meal plan to the user's terminal so that the user can check and use it.

[0729] "Means for identification" refers to a mechanism for identifying details of a location or dish based on the captured image and extracting related information.

[0730] "Delivery arrangements" refers to the procedure for delivering food to a designated location based on a meal provision plan.

[0731] To implement this invention, it is necessary to build a system that allows a user to input an image and generates and provides a travel plan or meal plan based on that image. This system is composed of the following processes.

[0732] Program Generation

[0733] The system is programmed as follows:

[0734] 1. Receiving image input

[0735] The user launches the dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for transmission to the server.

[0736] 2. Image Analysis

[0737] The server receives the received image data and passes it to an image analysis engine (e.g., OpenAI's CLIP model). The generative AI analyzes the image features and identifies the destination and cuisine depicted in the image.

[0738] 3. Travel or meal plan generation

[0739] Based on the information it identifies, the AI ​​generates a travel plan or meal plan. The travel plan includes destination attractions, accommodations, transportation, and a specific schedule. The meal plan includes identifying food locations and arranging delivery.

[0740] 4. Providing a plan

[0741] The server creates a "travel guide" or a "meal plan" based on the generated travel plan or meal plan. The server sends this information to the user's terminal, allowing the user to view it.

[0742] Hardware and Software Used

[0743] Hardware: Smartphone (iOS / Android)

[0744] Software: OpenAI CLIP model, Google Places API, Uber Eats API, Python, Flask, cloud server such as AWS (Amazon Web Services)

[0745] Specific examples

[0746] Travel plan execution example

[0747] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0748] 2. The server receives the image and instructs the generating AI to analyze it.

[0749] 3. The generative AI analyzes the image and recognizes that the beach is a specific tourist destination.

[0750] 4. The AI ​​generator collects tourist information about the relevant tourist destination and generates a 3-night, 4-day plan.

[0751] 5. Generative AI creates a detailed schedule for each day and calculates the budget for the entire trip.

[0752] 6. The server creates a "travel itinerary" and sends it to the device.

[0753] 7. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0754] Example of meal provision plan implementation

[0755] 1. The user uses a dedicated app to upload an image of paella, and the device sends the image to the server.

[0756] 2. The server receives the image and requests the generating AI to analyze it.

[0757] 3. The generative AI analyzes the image and recognizes the dish as "paella."

[0758] 4. The generating AI uses the Google Places API to identify the locations to serve.

[0759] 5. The generative AI uses the Uber Eats API to arrange food delivery based on the search results.

[0760] 6. The server sends the meal plan to the terminal, and the user confirms and completes the order.

[0761] Prompt Sentence Examples

[0762] Prompt: "What is the dish in this picture?"

[0763] Input image: A food image taken or uploaded by the user

[0764] In this way, the system can automatically generate and provide detailed travel plans and meal plans to users simply by inputting images, allowing users to easily make travel and meal arrangements.

[0765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0766] Step 1:

[0767] The user launches the dedicated application and selects or takes a picture. The user uploads the image they are interested in to the application, and the device acquires the image. In this step, the image entered by the user is directly saved on the device and converted into a data format for processing.

[0768] Input: An image taken or selected by the user

[0769] Output: Image data saved on the device (converted to a data format)

[0770] Step 2:

[0771] The device sends the acquired image data to the server. Specifically, the device executes a procedure to upload the image data to the server via the Internet. This allows the image data to be stored on the server for analysis.

[0772] Input: Image data stored on the device

[0773] Output: Image data sent to the server

[0774] Step 3:

[0775] The server passes the received image data to an image analysis engine for analysis. The server then calls the analysis engine (e.g., OpenAI's CLIP model), receives the image data as input, and analyzes its features. Specifically, it performs image recognition and feature extraction.

[0776] Input: Image data sent to the server

[0777] Output: Image features (analyzed data)

[0778] Step 4:

[0779] The server identifies specific destinations or dishes based on the analyzed image features. In the case of travel plans, the server retrieves relevant location information from a database. In the case of meal delivery plans, the server identifies the names of dishes and their locations. This provides the basis for generating the next plan.

[0780] Input: Image features (analyzed data)

[0781] Output: Identified destination or cuisine information

[0782] Step 5:

[0783] The generation AI generates a travel plan or meal plan based on the identified information. The travel plan includes a schedule of tourist spots and a budget calculation, while the meal plan includes meal locations and delivery arrangements. Specific operations include creating a schedule with detailed destinations and suggesting delivery options.

[0784] Input: Specific destination or cuisine information

[0785] Output: Generated travel or meal plan

[0786] Step 6:

[0787] The server creates a "travel itinerary" or "meal plan" based on the generated plan and sends it to the user's device. The server then converts the results into a format that is easy for the user to view (such as text or PDF) and sends the data to the device.

[0788] Input: Generated travel or meal plan

[0789] Output: "Travel guide" or "Meal plan" (sent to user device)

[0790] Step 7:

[0791] The user checks the provided "travel itinerary" or "meal plan" and proceeds with travel or meal arrangements. Specific operations include checking the plan contents and making the necessary reservations and arrangements.

[0792] Input: "Travel guide" or "Meal plan" (sent to user device)

[0793] Output: Travel or dining arrangements confirmed by the user

[0794] 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.

[0795] To implement this invention, a system for generating travel plans based on user-entered images is combined with an emotion engine that recognizes the user's emotions and customizes the travel plans based on those emotions. The following is a detailed program process and its explanation for this system.

[0796] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[0797] 1. Receiving image input

[0798] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0799] The terminal acquires the image and converts it into a data format for transmission to the server.

[0800] 2. Image Analysis

[0801] The server receives the received image data and passes it to the image analysis engine.

[0802] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[0803] 3. Emotion recognition

[0804] The device uses a camera and microphone to capture facial expressions and tone of voice to recognize the user's emotions.

[0805] The emotion engine analyzes the captured data and recognizes the user's emotions, for example, determining whether the user is excited or relaxed.

[0806] 4. Information Collection

[0807] Based on the information identified by the generative AI, a list of tourist attractions at and around the destination is created.

[0808] The server collects detailed tourist information (e.g., travel guide reviews, transportation options, accommodation information, etc.) via the Internet.

[0809] 5. Travel Plan Generation

[0810] Based on the information collected, the generative AI generates a travel plan that corresponds to the user's emotions.

[0811] For example, if the user is feeling relaxed, a leisurely schedule will be suggested, whereas if the user is feeling excited, a schedule including many active activities will be suggested.

[0812] 6. Budget Estimates

[0813] Generative AI will estimate a budget based on your travel plans, including transportation, accommodation, meals, etc.

[0814] 7. Bookmark Creation and Provision

[0815] The generative AI creates a "travel itinerary" based on the generated travel plan.

[0816] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[0817] Specific examples

[0818] Example 1: Planning a beach trip

[0819] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[0820] 2. The server receives the image and instructs the generating AI to analyze it.

[0821] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[0822] 4. The emotion engine identifies the user's emotions and recognizes that the user wants to relax.

[0823] 5. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[0824] 6. Generative AI suggests relaxing itineraries, creating plans that include hot spring resorts and relaxing activities, for example.

[0825] 7. The generation AI calculates the budget for the entire trip and presents the total amount.

[0826] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[0827] 9. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[0828] Example 2: Mountain trip planning

[0829] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0830] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[0831] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[0832] 4. The emotion engine identifies the user's emotions and recognizes that the user is looking for an exciting activity.

[0833] 5. The generation AI collects information on tourist spots in Arashiyama and generates a 2-night, 3-day travel plan based on that information.

[0834] 6. Generative AI suggests active itineraries and sets out plans that include, for example, hikes or bike tours.

[0835] 7. The generative AI calculates the budget for the entire trip and presents the final amount.

[0836] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[0837] 9. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[0838] In this way, by combining the emotion engine, the system can provide more personalized travel plans according to the user's emotions, allowing users to have a more satisfying travel experience.

[0839] The processing flow will be explained below.

[0840] Step 1:

[0841] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[0842] The user can either open the gallery from the app interface or launch the camera to capture an image.

[0843] Step 2:

[0844] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[0845] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[0846] Step 3:

[0847] The terminal transmits the image data to the server.

[0848] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[0849] Step 4:

[0850] The server prepares to analyze the received image data.

[0851] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[0852] Step 5:

[0853] The server instructs the generated AI to analyze the image.

[0854] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[0855] Step 6:

[0856] The generative AI analyzes the image features and extracts the information contained in the image.

[0857] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[0858] For example, "beach," "mountain," "city," etc. may be identified in this step.

[0859] Step 7:

[0860] The device uses a camera and microphone to capture the user's facial expressions and tone of voice to recognize their emotions.

[0861] The device will use facial recognition and voice analysis technologies to more accurately understand the user's current emotional state.

[0862] Step 8:

[0863] The emotion engine analyzes the captured data and recognizes the user's emotions.

[0864] The emotion engine determines whether the user is excited, relaxed, or in some other emotional state.

[0865] Step 9:

[0866] The generative AI identifies the destination and surrounding tourist attractions based on the analyzed image information and emotion data from the emotion engine.

[0867] The generative AI searches a database based on the recognized features and emotion data to list relevant tourist attractions and accommodations.

[0868] Step 10:

[0869] A server collects detailed tourist information via the Internet.

[0870] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[0871] Step 11:

[0872] The generative AI generates a travel plan based on the collected information.

[0873] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[0874] The generative AI selects activities and locations that match the user's emotions.

[0875] Step 12:

[0876] Generative AI calculates a budget based on your travel plans.

[0877] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[0878] Step 13:

[0879] Generative AI generates a detailed "travel itinerary."

[0880] The generative AI creates a "travel itinerary" as rich content including travel plans, details of tourist attractions, budget information, maps, links, etc.

[0881] Step 14:

[0882] The server sends the generated "travel guide" to the user's terminal.

[0883] The server provides the bookmark data to the terminal in URL format or directly as data.

[0884] Step 15:

[0885] The terminal receives the "travel guide" and displays it to the user.

[0886] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[0887] Step 16:

[0888] The user checks the "Travel Itinerary" and customizes the content as needed.

[0889] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[0890] Through these steps, the system allows users to easily receive specific travel plans. By combining it with an emotion engine, it is possible to provide more personalized travel plans that reflect the user's emotions.

[0891] Example 2

[0892] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0893] Conventional travel planning systems often generate uniform travel plans based on user-entered information, without taking into account the individual user's emotions or mood at the time. As a result, the travel plans may not meet the user's expectations, resulting in low satisfaction. Furthermore, they lack the ability to flexibly customize plans based on the user's emotions or provide specific travel budget estimates.

[0894] 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.

[0895] In this invention, the server includes means for acquiring an image input by a user, means for transmitting the acquired image to the server, means for the server to analyze the image, means for the terminal to recognize the user's emotion, means for customizing a travel plan based on the emotion, and means for providing the generated travel plan to the user, thereby enabling the generation of a more personalized travel plan based on the user's emotion.

[0896] "User" refers to an individual or organization that uses the system to make travel plans.

[0897] "Image" refers to digital data containing visual information captured or selected by a user, and is saved in formats such as JPEG or PNG.

[0898] "Terminal" refers to a computer device or mobile device operated by a user, which performs tasks such as acquiring images and collecting emotional data.

[0899] "Server" refers to the computing resources and software for receiving and analyzing image data, and generating and providing itineraries.

[0900] An "emotion engine" refers to a hardware and software system that analyzes and recognizes a user's emotional state from facial expressions, tone of voice, etc.

[0901] "Travel Itinerary" refers to a detailed plan including a user's travel destinations, schedule, activities, and estimated budget.

[0902] "Analysis" refers to the process of processing the acquired image data and emotion data, extracting features, and analyzing them.

[0903] "Features" refer to significant data patterns and attributes extracted from image and emotion data, and are elements that identify destinations, user emotions, etc.

[0904] "Tourist attractions" refer to places and facilities that tourists visit, including tourist destinations and activities.

[0905] "Customization" refers to the process of individually adjusting and modifying a travel plan based on the user's feelings and desires.

[0906] A "bookmark" is a document or digital file that details your ultimate travel plans, including schedules and important information.

[0907] MODE FOR CARRYING OUT THE INVENTION

[0908] To implement this invention, a system including a user terminal, a server, an image analysis engine, an emotion engine, and a generative AI model is required. This system generates a travel plan based on images entered by the user and analyzes the user's emotions to customize the travel plan.

[0909] 1. Receiving image input

[0910] The user launches a dedicated application and selects or takes a photo of the image that will trigger the trip. The device acquires the image and converts it into a data format (e.g., JPEG or PNG format) for transmission to the server.

[0911] 2. Sending and receiving images

[0912] The device sends image data to a server via the Internet, and the server receives the image data and temporarily stores it.

[0913] 3. Image Analysis

[0914] The image data stored on the server is input into an image analysis engine (e.g., TensorFlow or OpenCV). The generative AI extracts image features and identifies the destination or tourist spot indicated by the image. For example, it can recognize an image of a beach as a "famous tourist spot."

[0915] 4. Preparation for Emotion Recognition

[0916] The device activates the camera and microphone to recognize the user's emotions, capturing the user's facial expressions and tone of voice and collecting data in real time.

[0917] 5. Emotion analysis

[0918] The device sends the captured data to an emotion engine (e.g., Microsoft Azure Emotion API or Face API), which analyzes the data and recognizes the user's emotion. For example, it determines whether the user is relaxed or excited.

[0919] 6. Information Collection

[0920] Based on the destination specified, the AI ​​collects tourist attractions and nearby activities from the internet. For example, it uses travel guide review sites and web services. The server organizes the necessary information and passes it to the AI.

[0921] 7. Travel Plan Generation

[0922] The generative AI customizes travel plans based on the collected information and the user's emotional state. For example, if the user wants to relax, it will suggest a schedule that includes hot spring resorts and spas. If the user is excited, it will create a schedule that includes more active activities like hiking and surfing.

[0923] 8. Budget Estimates

[0924] The generation AI estimates costs based on the travel plan, including transportation, accommodation, and meals, and then sends the estimate to the server.

[0925] 9. Bookmark Creation and Provision

[0926] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. The itinerary includes the entire trip schedule, important contact information, and a checklist. The server then sends this "travel itinerary" to the user's device in PDF format or other format. The user can then view the "travel itinerary" through the application and begin preparing for their trip.

[0927] Specific examples

[0928] Example 1: Planning a beach trip

[0929] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device converts the image to JPEG format.

[0930] 2. The device sends a JPEG image to the server, which receives and saves the image.

[0931] 3. The server uses an image analysis engine (e.g., TensorFlow) to extract image features.

[0932] 4. The generative AI analyzes the image of the beach and identifies it as a "famous tourist destination."

[0933] 5. The device activates the camera and captures the user's facial expression.

[0934] 6. The emotion engine analyzes the user's facial expression data and recognizes that the user wants to relax.

[0935] 7. The generation AI collects tourist information from the internet and generates a 3-night, 4-day travel plan.

[0936] 8. Generative AI suggests a relaxing itinerary that includes hot spring resorts and spas.

[0937] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[0938] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[0939] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[0940] Example 2: Mountain trip planning

[0941] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[0942] 2. The device sends the screenshot to the server, which receives and saves the image.

[0943] 3. The server uses an image analysis engine (e.g., OpenCV) to extract image features.

[0944] 4. The generative AI analyzes the image and identifies the location as a "famous tourist spot."

[0945] 5. The device activates the microphone and captures the tone of the user's voice.

[0946] 6. The emotion engine analyzes tone of voice data and recognizes that the user is in an excited mood.

[0947] 7. The generation AI collects tourist spot information from the internet and generates a 2-night, 3-day travel plan.

[0948] 8. Generative AI suggests active itineraries including hikes and bike tours.

[0949] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[0950] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[0951] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[0952] Prompt Sentence Examples

[0953] "I uploaded a beach image and want you to generate a relaxing itinerary based on it."

[0954] "I captured a mountain scene of autumn leaves from Instagram. Please suggest some exciting travel plans."

[0955] In this way, users can easily obtain a personalized travel plan that matches their emotions at the time. This system is expected to make users' travel experiences more satisfying.

[0956] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The user launches the dedicated application and selects or takes a photo that will trigger the trip. The user's operation is the input, and an image file is obtained as the output. The specific operation is to launch the camera function within the application and support taking photos or selecting images from the gallery.

[0959] Step 2:

[0960] The image acquired by the device is converted to JPEG or PNG format and sent to the server. The input is an image selected or taken by the user, and the output is image data sent to the server. Specifically, the data format is converted using an image conversion library, and the data is uploaded to the server using the HTTP protocol.

[0961] Step 3:

[0962] The server temporarily stores the image data it receives. The input is image data sent from the terminal, and the output is a saved image file. Specifically, it saves the image in the file system and records the storage location in the database.

[0963] Step 4:

[0964] The server inputs the stored image data into the image analysis engine. The stored image data is the input, and image feature data is obtained as the output. Specifically, TensorFlow or OpenCV is used to extract image features and identify tourist spots and destinations.

[0965] Step 5:

[0966] The device activates the camera and microphone to recognize the user's emotions. The input is real-time data of the user's facial expressions and voice, and the output is captured emotional data. Specifically, the camera captures facial expressions and the microphone records the tone of voice.

[0967] Step 6:

[0968] The device sends the captured data to the emotion engine, which then analyzes the user's emotions. The input is captured facial expression data and voice data, and the output is analyzed emotional state data. Specific operations include recognizing emotions using the Microsoft Azure Emotion API or Face API.

[0969] Step 7:

[0970] Based on the identified destination, the generative AI collects information on tourist attractions and nearby activities from the internet. The input is the identified destination information and emotional state data, and the output is a list of tourist information. Specifically, it uses web services and APIs to collect information from tourist guide sites.

[0971] Step 8:

[0972] The generative AI customizes a travel plan based on the collected information and the user's emotional state. The input is a list of tourist information and emotional state data, and the output is a customized travel plan. Specifically, it generates a schedule based on the information and suggests activities according to the user's emotional state.

[0973] Step 9:

[0974] The generative AI estimates costs based on a travel plan. It takes a customized travel plan as input and provides a budget estimate as output. Specifically, it calculates transportation, accommodation, meal costs, etc., and calculates the total amount.

[0975] Step 10:

[0976] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. It takes a customized travel plan and budget estimate as input, and produces a completed "travel itinerary" as output. Specifically, it compiles the entire trip schedule, important contacts, checklists, and more into a document.

[0977] Step 11:

[0978] The server sends the "Travel Itinerary" to the user's device in PDF format or other format. The input is the completed "Travel Itinerary," and the output is the data sent to the user's device. Specific operations include sending the "Travel Itinerary" via email or in-app notifications.

[0979] Step 12:

[0980] The user checks the "travel itinerary" through the application and begins preparing for the trip. The "travel itinerary" is the input, and the trip preparations are completed as the output. The specific operation is to open the "travel itinerary" on the device and check the schedule and activities.

[0981] (Application example 2)

[0982] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0983] Conventional food delivery systems have the problem that it is difficult for users to easily find the appropriate meal based on their preferences and emotions. In addition, suggestions based on food images are limited, and personalized suggestions that take into account the user's individual emotions and moods are not possible. To solve this problem, a system is needed that can customize meal plans based on the images entered by the user and their current emotions, and suggest appropriate restaurants and food delivery services.

[0984] 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.

[0985] In this invention, the server includes means for acquiring an image input by a user, means for analyzing the acquired image, means for generating a meal provision plan based on the analyzed information, means for recognizing the user's emotions and customizing the meal provision plan based on the acquired emotion information, and means for providing the generated meal provision plan to the user. This makes it possible to easily generate and provide a personalized meal provision plan that meets the user's wishes and emotions.

[0986] "User" means an individual or organization that uses this system.

[0987] "Image" refers to visual data stored in digital form.

[0988] "Acquisition" refers to the process of gathering or receiving information or data.

[0989] "Analysis" refers to the process of unraveling the content of data or information and finding meaning.

[0990] "Meal Plan" means a schedule or plan that includes specific dish or meal suggestions.

[0991] "Emotion" refers to data that indicates a user's psychological state and mood.

[0992] "Customization" refers to the process of modifying or optimizing content based on the needs and desires of an individual user.

[0993] "Providing" refers to delivering information or services to users.

[0994] "System" refers to a technological device or application that includes multiple components and functions that work in conjunction with each other.

[0995] To put this invention into practice, it is necessary to build a system that allows users to input images of the food they want to eat, generates a meal plan based on those images, and customizes it according to the user's emotions. The detailed configuration and specific operating procedures of this system are described below.

[0996] Hardware and software used

[0997] Hardware: Smartphone (including camera and microphone)

[0998] Software: Image analysis engine (e.g., Google Cloud Vision API), emotion recognition engine (e.g., Microsoft Azure Face API), generative AI (OpenAI GPT-4), server for data processing and management

[0999] Detailed system configuration

[1000] 1. To receive image input, the user selects or takes a picture of the food they want to eat using the smartphone camera and inputs the image into the application. The device converts the image data into JSON format and sends it to the server.

[1001] 2. Image analysis uses the Google Cloud Vision API to analyze the image data received by the server, and the type and characteristics of the food are identified as a result of the analysis.

[1002] 3. Emotion recognition uses the camera and microphone on the user's smartphone to capture the user's facial expressions and tone of voice, and then uses the Microsoft Azure Face API to analyze the user's emotions, for example, to determine whether they are feeling relaxed or excited.

[1003] 4. Information gathering and suggestion generation are performed using generative AI (OpenAI GPT-4) based on the analysis results. For example, restaurant and menu suggestions are generated using the following prompt:

[1004] If the user wants to relax:

[1005] text

[1006] Suggest restaurants delivering sushi for someone feeling relaxed.

[1007] If the user is in an exciting mood:

[1008] text

[1009] Suggest restaurants delivering spicy Mexican food for someone feeling excited.

[1010] 5. The generation and provision of meal plan involves the generation AI generating an appropriate meal plan based on the proposed content, and the server sending this plan to the user's device and providing it in a format that is easy for the user to check.

[1011] Specific examples

[1012] For example, a user takes a picture of "sushi" with their smartphone and inputs it into a "Smart Food Delivery" app. The system analyzes the image with Google Cloud Vision API, identifying "sushi" as the result. It then uses Microsoft Azure Face API to recognize the user's emotions and determine whether they are in a relaxed mood. OpenAI GPT-4 suggests suitable restaurants using the following prompt:

[1013] text

[1014] Suggest restaurants delivering sushi for someone feeling relaxed.

[1015] This presents the user with a delivery service that offers home-style sushi.

[1016] The present invention can generate a personalized meal plan based on the user's emotions and meal images, providing a suitable dining experience.

[1017] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1018] Step 1:

[1019] A user launches the app on their smartphone and selects or takes a picture of the food they want to eat. The device converts this image data into an appropriate data format (e.g., JSON) and sends it to the server.

[1020] Input: Food image

[1021] Output: Image data sent to the server (JSON format)

[1022] Specific behavior:

[1023] The user takes a picture of the dish with the camera, selects the image on the app, and presses the upload button. The application converts the image data into JSON format and sends it to the server.

[1024] Step 2:

[1025] The server acquires the received image data and analyzes it using an image analysis engine (Google Cloud Vision API). As a result of the analysis, the type and characteristics of the food are identified from the image features.

[1026] Input: Image data sent to the server (JSON format)

[1027] Output: Food type and characteristics (e.g., sushi, pizza)

[1028] Specific behavior:

[1029] The server calls the Google Cloud Vision API, analyzes the characteristics of the food based on the received image data, and obtains the analysis results in JSON format to identify the type of food.

[1030] Step 3:

[1031] The device uses the user's smartphone camera and microphone to capture the user's facial expressions and tone of voice, and then analyzes emotions using an emotion recognition engine (Microsoft Azure Face API).

[1032] Input: User facial and voice data

[1033] Output: User's emotional state (e.g., relaxed, excited)

[1034] Specific behavior:

[1035] The user looks into the smartphone camera to capture their facial expressions and the tone of their voice with the microphone, which are then sent to the Azure Face API, which then sends the analysis results back to the server.

[1036] Step 4:

[1037] Based on the analyzed food information and emotional information, the server uses generative AI (OpenAI GPT-4) to generate restaurant and food suggestions suitable for the user.

[1038] Input: type and characteristics of food, user's emotional state

[1039] Output: A list of suggested restaurants and dishes

[1040] Specific behavior:

[1041] The server queries the AI ​​generator with a prompt based on the analysis information. The AI ​​generator generates a list of restaurants and dishes based on the prompt, and the server retrieves the results.

[1042] Step 5:

[1043] The server transmits the generated meal plan to the user's terminal, and the user confirms it.

[1044] Input: A list of suggested restaurants and dishes

[1045] Output: Meal plan displayed on user's device

[1046] Specific behavior:

[1047] The generated plan is sent to the user's device in JSON format. The application parses the data and displays it to the user as a list or detailed information. The user can then review the proposed information and place an order.

[1048] 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.

[1049] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1050] 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.

[1051] [Third embodiment]

[1052] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1053] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1064] To implement this invention, a system is constructed that allows users to input images and generate and provide travel plans based on them. The following is a detailed program process and its explanation.

[1065] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1066] 1. Receiving image input

[1067] The user launches the dedicated application and selects or takes a picture of an image that interests them.

[1068] The terminal acquires the image and converts it into a data format for transmission to the server.

[1069] 2. Image Analysis

[1070] The server receives the received image data and passes it to the image analysis engine.

[1071] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[1072] 3. Travel plan generation

[1073] Based on the information identified, the AI ​​generates a travel plan, including destination attractions, accommodations, transportation options, and a specific itinerary.

[1074] The generative AI creates a detailed schedule for each day and estimates the budget based on this plan.

[1075] 4. Bookmark creation and provision

[1076] The generative AI creates a "travel itinerary" based on the generated travel plan.

[1077] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[1078] Specific examples

[1079] Example 1: Planning a beach trip

[1080] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1081] 2. The server receives the image and instructs the generating AI to analyze it.

[1082] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[1083] 4. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[1084] 5. The AI ​​generator creates daily schedules for things like the Blue Lagoon, sightseeing in Valletta, and a tour of Gozo.

[1085] 6. The generation AI calculates the budget for the entire trip and presents the total amount.

[1086] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[1087] 8. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1088] Example 2: Mountain trip planning

[1089] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1090] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[1091] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[1092] 4. The generation AI collects information on tourist spots in Arashiyama and generates a 3-day, 2-night travel plan based on that information.

[1093] 5. The AI ​​generator assigns tourist spots such as Tenryu-ji Temple, the Bamboo Forest Path, and Arashiyama Park to each day.

[1094] 6. The generative AI calculates the budget for the entire trip and presents the final amount.

[1095] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[1096] 8. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[1097] In this way, this system can automatically generate and provide detailed travel plans to users simply by inputting images, allowing users to easily plan their trips and stay motivated to travel.

[1098] The processing flow will be explained below.

[1099] Step 1:

[1100] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1101] The user can either open the gallery from the app interface or launch the camera to capture an image.

[1102] Step 2:

[1103] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[1104] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[1105] Step 3:

[1106] The terminal transmits the image data to the server.

[1107] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[1108] Step 4:

[1109] The server prepares to analyze the received image data.

[1110] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[1111] Step 5:

[1112] The server instructs the generated AI to analyze the image.

[1113] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[1114] Step 6:

[1115] The generative AI analyzes the image features and extracts the information contained in the image.

[1116] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[1117] For example, "beach," "mountain," "city," etc. may be identified in this step.

[1118] Step 7:

[1119] Based on the analyzed information, the generative AI identifies the destination and surrounding tourist attractions.

[1120] The generative AI searches a database based on the recognized features and lists relevant tourist attractions and accommodations.

[1121] Step 8:

[1122] A server collects detailed tourist information via the Internet.

[1123] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[1124] Step 9:

[1125] The generative AI generates a travel plan based on the collected information.

[1126] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[1127] Step 10:

[1128] Generative AI calculates a budget based on your travel plans.

[1129] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[1130] Step 11:

[1131] The server generates a detailed "travel itinerary."

[1132] The server creates a "travel guide" as rich content including travel plans, details of tourist spots, budget information, maps, links, and more.

[1133] Step 12:

[1134] The server sends the generated "travel guide" to the user's terminal.

[1135] The server provides the bookmark data to the terminal in URL format or directly as data.

[1136] Step 13:

[1137] The terminal receives the "travel guide" and displays it to the user.

[1138] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[1139] Step 14:

[1140] The user checks the "Travel Itinerary" and customizes the content as needed.

[1141] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[1142] In this way, through the above steps, the system allows the user to easily receive a specific travel plan.

[1143] Example 1

[1144] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1145] Modern travelers face the challenge of finding the right travel destination and itinerary on their own from the vast amount of information available. Planning a trip, including selecting destinations and tourist spots, creating an itinerary, and estimating a budget, takes a lot of time and effort. Furthermore, customizing travel plans and meeting user needs is not easy.

[1146] 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.

[1147] In this invention, the server includes means for acquiring images input by a user, means for converting the acquired images into an optimal data format, means for transmitting the acquired images to the server, means for analyzing the images received by the server, means for identifying image features based on the analyzed information and using a generative AI model to determine destinations and tourist spots, means for generating a travel plan based on the identified information, means for estimating a budget based on the generated travel plan, and means for providing the generated travel plan to the user. This reduces the burden of travel planning because a user only needs to input an image to automatically generate a travel plan and be provided with a detailed itinerary and budget.

[1148] "User" means any person or entity that operates the System.

[1149] "Image" refers to visual data provided by a user through a dedicated application.

[1150] "Data format" refers to the particular format in which image data is processed by the server.

[1151] "Server" refers to a computing system for receiving, analyzing, processing data, and providing results.

[1152] "Image analysis" refers to technical methods used to identify features or content in image data.

[1153] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence technology to analyze images and data and automatically generate travel plans.

[1154] A "travel plan" refers to a detailed schedule including destinations, attractions, accommodations, and transportation methods that is generated based on the user's needs.

[1155] "Budgeting" refers to the process of calculating approximate costs based on travel plans.

[1156] A "travel itinerary" refers to a document or digital data that summarizes a generated travel plan.

[1157] "Analyzed information" refers to data such as destinations and tourist spots obtained through image analysis.

[1158] To implement this invention, we will build a system that allows users to input images and generate and provide travel plans based on them. This system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1159] Receiving image input

[1160] The user launches a dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for sending to the server. For example, the device converts the image into JPEG format and sends it to an API endpoint.

[1161] Image analysis

[1162] The server reviews the received image data and passes it to an image analysis engine. A generative AI model analyzes the image features and identifies the destination or tourist spot represented by the image. For example, a generative AI model (e.g., ResNet, YOLO) analyzes the visual features and generates a tag for "beach."

[1163] Identifying destinations and attractions

[1164] The generative AI model identifies the destination shown in the image based on the analysis results. For example, in the case of a beach, it identifies the "main island." The generative AI model then collects information about tourist attractions related to that destination from a database.

[1165] Generate a travel plan

[1166] The generative AI model creates a travel plan based on the collected information, including destination attractions, accommodation, transportation, and a specific schedule. For example, the generative AI model could automatically generate a four-day, three-night itinerary with a daily schedule that includes visiting the beach on the first day and sightseeing in the city on the second day.

[1167] Creating and sending bookmarks

[1168] The generative AI model generates a "travel itinerary" based on the travel plan. The server sends the generated travel itinerary to the user's device. The device displays a notification that the "travel itinerary" has been received, allowing the user to view it.

[1169] Hardware and software used

[1170] Device: Smartphone or tablet (e.g., iPhone, Android device)

[1171] Server: Cloud server environment (e.g. AWS, Google Cloud)

[1172] Generative AI models: Machine learning models (e.g., TensorFlow, PyTorch)

[1173] Prompt Sentence Examples

[1174] Here are some examples of prompts to input to a generative AI model:

[1175] "Analyze this beach image and generate the best itinerary."

[1176] "Generate a trip plan based on this mountain view image."

[1177] Specific examples

[1178] Planning a beach trip

[1179] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1180] 2. The server receives the image and instructs the generative AI model to analyze it.

[1181] 3. A generative AI model analyzes the image and recognizes the beach as a "prime location."

[1182] 4. The generative AI model collects tourist information about the destination and generates a 3-night, 4-day plan.

[1183] 5. The generative AI model creates a daily schedule and calculates the overall budget for the trip.

[1184] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[1185] 7. The user checks the "Travel Itinerary" and begins preparing for their trip.

[1186] Mountain Travel Planning

[1187] 1. A user finds a beautiful mountain scene with autumn leaves on social media and imports a screenshot into a dedicated app.

[1188] 2. The device sends the image to the server, which then requests the generative AI model to analyze it.

[1189] 3. A generative AI model analyzes the image and identifies the location as an "another prime location."

[1190] 4. The generative AI model collects tourist spot information and generates a 2-night, 3-day travel plan based on that information.

[1191] 5. The generative AI model allocates tourist attractions by day and calculates the overall budget for the trip.

[1192] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[1193] 7. The user checks the provided "Travel Itinerary" and begins preparing for their trip.

[1194] The above is an embodiment of this system.

[1195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1196] Step 1:

[1197] Image acquisition

[1198] The user launches the dedicated application and selects or takes a picture of an image they are interested in. As the user operates the device, it retrieves image data from the photo library. The input here is the image selected or taken by the user, and this image becomes the output displayed within the application.

[1199] Step 2:

[1200] Converting to data format

[1201] The image data acquired by the terminal from the user is converted into the optimal data format for sending to the server. For example, the image data is converted into JPEG format. The input is the image data provided by the user, and the output is the converted JPEG format image.

[1202] Step 3:

[1203] Sending images

[1204] The device sends the converted image data to the server. The image data is sent as a POST request to a specific API endpoint. The input is the image data in JPEG format, and the output is the data sent to the server. The device displays a success message.

[1205] Step 4:

[1206] Image Receipt and Verification

[1207] The server checks the image data received from the terminal and verifies that there is no corruption or format mismatch. The input is the image data that arrived at the server, and the output is the verification result (success or error).

[1208] Step 5:

[1209] Image analysis request

[1210] The server passes image data to the image analysis engine and requests an analysis. The generative AI model analyzes the image's features and identifies the elements represented by the image (e.g., ocean, mountain, city). The input is the image data stored on the server, and the output is the feature extraction results.

[1211] Step 6:

[1212] Determining specific destinations and attractions

[1213] The generative AI model determines destinations and tourist spots based on the identified features. For example, if an image of a beach is used, it will identify "specific islands" and "tourist spots." The input is the feature extraction results, and the output is information on the identified destinations and tourist spots.

[1214] Step 7:

[1215] Generate a travel plan

[1216] The generative AI model generates a travel plan based on the identified destinations and tourist attractions. Specifically, it creates a schedule including accommodations, tourist attractions, and transportation. The input is the identified tourist attractions, and the output is a detailed travel plan.

[1217] Step 8:

[1218] Budget Estimates

[1219] The generative AI model estimates a budget based on a travel plan, calculating the cost of each item (e.g., accommodation, transportation) and calculating the total amount. The input is the details of the travel plan, and the output is the estimated budget.

[1220] Step 9:

[1221] Creating bookmarks

[1222] A generative AI model creates a "travel itinerary" based on a travel plan, including itinerary, attractions to visit, budget, etc. The input is a travel plan and estimated budget, and the output is a detailed itinerary.

[1223] Step 10:

[1224] Send a bookmark

[1225] The server sends the generated "travel itinerary" to the user's device. The user receives the bookmark and is notified so that they can view it. The input is the generated "travel itinerary," and the output is the result of transmission to the user's device.

[1226] Step 11:

[1227] Check bookmarks

[1228] The user checks the "travel itinerary" on the terminal and makes a final check of the details of the travel plan. At this stage, the user begins preparing for the trip. The input is the bookmark sent to the terminal, and the output is the bookmark confirmation result.

[1229] The above is a description of the specific processing steps.

[1230] (Application example 1)

[1231] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1232] Conventional travel plan generation systems analyze images entered by users to suggest specific travel destinations and tourist spots, but they are unable to accommodate diverse user needs, such as meal delivery plans. For this reason, there has been a demand for a system that allows users to easily create both travel plans and meal delivery plans.

[1233] 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.

[1234] In this invention, the server includes means for acquiring images input by the user, means for analyzing the acquired images, means for generating a travel plan or meal provision plan based on the analyzed information, and means for providing the generated travel plan or meal provision plan to the user. This allows the user to easily acquire both the travel plan and the meal provision plan, greatly improving convenience.

[1235] "User" refers to the individual or end user who inputs images using a dedicated application.

[1236] "Means for acquiring images" refers to an interface for importing images input or taken by the user into the system.

[1237] "Means for analyzing images" refers to an algorithm or engine that analyzes acquired image data and identifies or classifies its contents.

[1238] "Travel Plan" refers to a plan that suggests a schedule and budget including specific travel destinations and tourist attractions based on a user's input image.

[1239] A "meal plan" refers to a plan based on an image of a dish entered by a user, including the location where the dish will be served and delivery arrangements.

[1240] "Provision means" refers to a mechanism for sending the generated travel plan or meal plan to the user's terminal so that the user can check and use it.

[1241] "Means for identification" refers to a mechanism for identifying details of a location or dish based on the captured image and extracting related information.

[1242] "Delivery arrangements" refers to the procedure for delivering food to a designated location based on a meal provision plan.

[1243] To implement this invention, it is necessary to build a system that allows a user to input an image and generates and provides a travel plan or meal plan based on that image. This system is composed of the following processes.

[1244] Program Generation

[1245] The system is programmed as follows:

[1246] 1. Receiving image input

[1247] The user launches the dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for transmission to the server.

[1248] 2. Image Analysis

[1249] The server receives the received image data and passes it to an image analysis engine (e.g., OpenAI's CLIP model). The generative AI analyzes the image features and identifies the destination and cuisine depicted in the image.

[1250] 3. Travel or meal plan generation

[1251] Based on the information it identifies, the AI ​​generates a travel plan or meal plan. The travel plan includes destination attractions, accommodations, transportation, and a specific schedule. The meal plan includes identifying food locations and arranging delivery.

[1252] 4. Providing a plan

[1253] The server creates a "travel guide" or a "meal plan" based on the generated travel plan or meal plan. The server sends this information to the user's terminal, allowing the user to view it.

[1254] Hardware and Software Used

[1255] Hardware: Smartphone (iOS / Android)

[1256] Software: OpenAI CLIP model, Google Places API, Uber Eats API, Python, Flask, cloud server such as AWS (Amazon Web Services)

[1257] Specific examples

[1258] Travel plan execution example

[1259] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1260] 2. The server receives the image and instructs the generating AI to analyze it.

[1261] 3. The generative AI analyzes the image and recognizes that the beach is a specific tourist destination.

[1262] 4. The AI ​​generator collects tourist information about the relevant tourist destination and generates a 3-night, 4-day plan.

[1263] 5. Generative AI creates a detailed schedule for each day and calculates the budget for the entire trip.

[1264] 6. The server creates a "travel itinerary" and sends it to the device.

[1265] 7. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1266] Example of meal provision plan implementation

[1267] 1. The user uses a dedicated app to upload an image of paella, and the device sends the image to the server.

[1268] 2. The server receives the image and requests the generating AI to analyze it.

[1269] 3. The generative AI analyzes the image and recognizes the dish as "paella."

[1270] 4. The generating AI uses the Google Places API to identify the locations to serve.

[1271] 5. The generative AI uses the Uber Eats API to arrange food delivery based on the search results.

[1272] 6. The server sends the meal plan to the terminal, and the user confirms and completes the order.

[1273] Prompt Sentence Examples

[1274] Prompt: "What is the dish in this picture?"

[1275] Input image: A food image taken or uploaded by the user

[1276] In this way, the system can automatically generate and provide detailed travel plans and meal plans to users simply by inputting images, allowing users to easily make travel and meal arrangements.

[1277] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1278] Step 1:

[1279] The user launches the dedicated application and selects or takes a picture. The user uploads the image they are interested in to the application, and the device acquires the image. In this step, the image entered by the user is directly saved on the device and converted into a data format for processing.

[1280] Input: An image taken or selected by the user

[1281] Output: Image data saved on the device (converted to a data format)

[1282] Step 2:

[1283] The device sends the acquired image data to the server. Specifically, the device executes a procedure to upload the image data to the server via the Internet. This allows the image data to be stored on the server for analysis.

[1284] Input: Image data stored on the device

[1285] Output: Image data sent to the server

[1286] Step 3:

[1287] The server passes the received image data to an image analysis engine for analysis. The server then calls the analysis engine (e.g., OpenAI's CLIP model), receives the image data as input, and analyzes its features. Specifically, it performs image recognition and feature extraction.

[1288] Input: Image data sent to the server

[1289] Output: Image features (analyzed data)

[1290] Step 4:

[1291] The server identifies specific destinations or dishes based on the analyzed image features. In the case of travel plans, the server retrieves relevant location information from a database. In the case of meal delivery plans, the server identifies the names of dishes and their locations. This provides the basis for generating the next plan.

[1292] Input: Image features (analyzed data)

[1293] Output: Identified destination or cuisine information

[1294] Step 5:

[1295] The generation AI generates a travel plan or meal plan based on the identified information. The travel plan includes a schedule of tourist spots and a budget calculation, while the meal plan includes meal locations and delivery arrangements. Specific operations include creating a schedule with detailed destinations and suggesting delivery options.

[1296] Input: Specific destination or cuisine information

[1297] Output: Generated travel or meal plan

[1298] Step 6:

[1299] The server creates a "travel itinerary" or "meal plan" based on the generated plan and sends it to the user's device. The server then converts the results into a format that is easy for the user to view (such as text or PDF) and sends the data to the device.

[1300] Input: Generated travel or meal plan

[1301] Output: "Travel guide" or "Meal plan" (sent to user device)

[1302] Step 7:

[1303] The user checks the provided "travel itinerary" or "meal plan" and proceeds with travel or meal arrangements. Specific operations include checking the plan contents and making the necessary reservations and arrangements.

[1304] Input: "Travel guide" or "Meal plan" (sent to user device)

[1305] Output: Travel or dining arrangements confirmed by the user

[1306] 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.

[1307] To implement this invention, a system for generating travel plans based on user-entered images is combined with an emotion engine that recognizes the user's emotions and customizes the travel plans based on those emotions. The following is a detailed program process and its explanation for this system.

[1308] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1309] 1. Receiving image input

[1310] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1311] The terminal acquires the image and converts it into a data format for transmission to the server.

[1312] 2. Image Analysis

[1313] The server receives the received image data and passes it to the image analysis engine.

[1314] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[1315] 3. Emotion recognition

[1316] The device uses a camera and microphone to capture facial expressions and tone of voice to recognize the user's emotions.

[1317] The emotion engine analyzes the captured data and recognizes the user's emotions, for example, determining whether the user is excited or relaxed.

[1318] 4. Information Collection

[1319] Based on the information identified by the generative AI, a list of tourist attractions at and around the destination is created.

[1320] The server collects detailed tourist information (e.g., travel guide reviews, transportation options, accommodation information, etc.) via the Internet.

[1321] 5. Travel Plan Generation

[1322] Based on the information collected, the generative AI generates a travel plan that corresponds to the user's emotions.

[1323] For example, if the user is feeling relaxed, a leisurely schedule will be suggested, whereas if the user is feeling excited, a schedule including many active activities will be suggested.

[1324] 6. Budget Estimates

[1325] Generative AI will estimate a budget based on your travel plans, including transportation, accommodation, meals, etc.

[1326] 7. Bookmark Creation and Provision

[1327] The generative AI creates a "travel itinerary" based on the generated travel plan.

[1328] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[1329] Specific examples

[1330] Example 1: Planning a beach trip

[1331] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1332] 2. The server receives the image and instructs the generating AI to analyze it.

[1333] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[1334] 4. The emotion engine identifies the user's emotions and recognizes that the user wants to relax.

[1335] 5. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[1336] 6. Generative AI suggests relaxing itineraries, creating plans that include hot spring resorts and relaxing activities, for example.

[1337] 7. The generation AI calculates the budget for the entire trip and presents the total amount.

[1338] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[1339] 9. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1340] Example 2: Mountain trip planning

[1341] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1342] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[1343] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[1344] 4. The emotion engine identifies the user's emotions and recognizes that the user is looking for an exciting activity.

[1345] 5. The generation AI collects information on tourist spots in Arashiyama and generates a 2-night, 3-day travel plan based on that information.

[1346] 6. Generative AI suggests active itineraries and sets out plans that include, for example, hikes or bike tours.

[1347] 7. The generative AI calculates the budget for the entire trip and presents the final amount.

[1348] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[1349] 9. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[1350] In this way, by combining the emotion engine, the system can provide more personalized travel plans according to the user's emotions, allowing users to have a more satisfying travel experience.

[1351] The processing flow will be explained below.

[1352] Step 1:

[1353] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1354] The user can either open the gallery from the app interface or launch the camera to capture an image.

[1355] Step 2:

[1356] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[1357] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[1358] Step 3:

[1359] The terminal transmits the image data to the server.

[1360] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[1361] Step 4:

[1362] The server prepares to analyze the received image data.

[1363] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[1364] Step 5:

[1365] The server instructs the generated AI to analyze the image.

[1366] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[1367] Step 6:

[1368] The generative AI analyzes the image features and extracts the information contained in the image.

[1369] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[1370] For example, "beach," "mountain," "city," etc. may be identified in this step.

[1371] Step 7:

[1372] The device uses a camera and microphone to capture the user's facial expressions and tone of voice to recognize their emotions.

[1373] The device will use facial recognition and voice analysis technologies to more accurately understand the user's current emotional state.

[1374] Step 8:

[1375] The emotion engine analyzes the captured data and recognizes the user's emotions.

[1376] The emotion engine determines whether the user is excited, relaxed, or in some other emotional state.

[1377] Step 9:

[1378] The generative AI identifies the destination and surrounding tourist attractions based on the analyzed image information and emotion data from the emotion engine.

[1379] The generative AI searches a database based on the recognized features and emotion data to list relevant tourist attractions and accommodations.

[1380] Step 10:

[1381] A server collects detailed tourist information via the Internet.

[1382] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[1383] Step 11:

[1384] The generative AI generates a travel plan based on the collected information.

[1385] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[1386] The generative AI selects activities and locations that match the user's emotions.

[1387] Step 12:

[1388] Generative AI calculates a budget based on your travel plans.

[1389] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[1390] Step 13:

[1391] Generative AI generates a detailed "travel itinerary."

[1392] The generative AI creates a "travel itinerary" as rich content including travel plans, details of tourist attractions, budget information, maps, links, etc.

[1393] Step 14:

[1394] The server sends the generated "travel guide" to the user's terminal.

[1395] The server provides the bookmark data to the terminal in URL format or directly as data.

[1396] Step 15:

[1397] The terminal receives the "travel guide" and displays it to the user.

[1398] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[1399] Step 16:

[1400] The user checks the "Travel Itinerary" and customizes the content as needed.

[1401] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[1402] Through these steps, the system allows users to easily receive specific travel plans. By combining it with an emotion engine, it is possible to provide more personalized travel plans that reflect the user's emotions.

[1403] Example 2

[1404] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1405] Conventional travel planning systems often generate uniform travel plans based on user-entered information, without taking into account the individual user's emotions or mood at the time. As a result, the travel plans may not meet the user's expectations, resulting in low satisfaction. Furthermore, they lack the ability to flexibly customize plans based on the user's emotions or provide specific travel budget estimates.

[1406] 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.

[1407] In this invention, the server includes means for acquiring an image input by a user, means for transmitting the acquired image to the server, means for the server to analyze the image, means for the terminal to recognize the user's emotion, means for customizing a travel plan based on the emotion, and means for providing the generated travel plan to the user, thereby enabling the generation of a more personalized travel plan based on the user's emotion.

[1408] "User" refers to an individual or organization that uses the system to make travel plans.

[1409] "Image" refers to digital data containing visual information captured or selected by a user, and is saved in formats such as JPEG or PNG.

[1410] "Terminal" refers to a computer device or mobile device operated by a user, which performs tasks such as acquiring images and collecting emotional data.

[1411] "Server" refers to the computing resources and software for receiving and analyzing image data, and generating and providing itineraries.

[1412] An "emotion engine" refers to a hardware and software system that analyzes and recognizes a user's emotional state from facial expressions, tone of voice, etc.

[1413] "Travel Itinerary" refers to a detailed plan including a user's travel destinations, schedule, activities, and estimated budget.

[1414] "Analysis" refers to the process of processing the acquired image data and emotion data, extracting features, and analyzing them.

[1415] "Features" refer to significant data patterns and attributes extracted from image and emotion data, and are elements that identify destinations, user emotions, etc.

[1416] "Tourist attractions" refer to places and facilities that tourists visit, including tourist destinations and activities.

[1417] "Customization" refers to the process of individually adjusting and modifying a travel plan based on the user's feelings and desires.

[1418] A "bookmark" is a document or digital file that details your ultimate travel plans, including schedules and important information.

[1419] MODE FOR CARRYING OUT THE INVENTION

[1420] To implement this invention, a system including a user terminal, a server, an image analysis engine, an emotion engine, and a generative AI model is required. This system generates a travel plan based on images entered by the user and analyzes the user's emotions to customize the travel plan.

[1421] 1. Receiving image input

[1422] The user launches a dedicated application and selects or takes a photo of the image that will trigger the trip. The device acquires the image and converts it into a data format (e.g., JPEG or PNG format) for transmission to the server.

[1423] 2. Sending and receiving images

[1424] The device sends image data to a server via the Internet, and the server receives the image data and temporarily stores it.

[1425] 3. Image Analysis

[1426] The image data stored on the server is input into an image analysis engine (e.g., TensorFlow or OpenCV). The generative AI extracts image features and identifies the destination or tourist spot indicated by the image. For example, it can recognize an image of a beach as a "famous tourist spot."

[1427] 4. Preparation for Emotion Recognition

[1428] The device activates the camera and microphone to recognize the user's emotions, capturing the user's facial expressions and tone of voice and collecting data in real time.

[1429] 5. Emotion analysis

[1430] The device sends the captured data to an emotion engine (e.g., Microsoft Azure Emotion API or Face API), which analyzes the data and recognizes the user's emotion. For example, it determines whether the user is relaxed or excited.

[1431] 6. Information Collection

[1432] Based on the destination specified, the AI ​​collects tourist attractions and nearby activities from the internet. For example, it uses travel guide review sites and web services. The server organizes the necessary information and passes it to the AI.

[1433] 7. Travel Plan Generation

[1434] The generative AI customizes travel plans based on the collected information and the user's emotional state. For example, if the user wants to relax, it will suggest a schedule that includes hot spring resorts and spas. If the user is excited, it will create a schedule that includes more active activities like hiking and surfing.

[1435] 8. Budget Estimates

[1436] The generation AI estimates costs based on the travel plan, including transportation, accommodation, and meals, and then sends the estimate to the server.

[1437] 9. Bookmark Creation and Provision

[1438] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. The itinerary includes the entire trip schedule, important contact information, and a checklist. The server then sends this "travel itinerary" to the user's device in PDF format or other format. The user can then view the "travel itinerary" through the application and begin preparing for their trip.

[1439] Specific examples

[1440] Example 1: Planning a beach trip

[1441] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device converts the image to JPEG format.

[1442] 2. The device sends a JPEG image to the server, which receives and saves the image.

[1443] 3. The server uses an image analysis engine (e.g., TensorFlow) to extract image features.

[1444] 4. The generative AI analyzes the image of the beach and identifies it as a "famous tourist destination."

[1445] 5. The device activates the camera and captures the user's facial expression.

[1446] 6. The emotion engine analyzes the user's facial expression data and recognizes that the user wants to relax.

[1447] 7. The generation AI collects tourist information from the internet and generates a 3-night, 4-day travel plan.

[1448] 8. Generative AI suggests a relaxing itinerary that includes hot spring resorts and spas.

[1449] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[1450] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[1451] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[1452] Example 2: Mountain trip planning

[1453] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1454] 2. The device sends the screenshot to the server, which receives and saves the image.

[1455] 3. The server uses an image analysis engine (e.g., OpenCV) to extract image features.

[1456] 4. The generative AI analyzes the image and identifies the location as a "famous tourist spot."

[1457] 5. The device activates the microphone and captures the tone of the user's voice.

[1458] 6. The emotion engine analyzes tone of voice data and recognizes that the user is in an excited mood.

[1459] 7. The generation AI collects tourist spot information from the internet and generates a 2-night, 3-day travel plan.

[1460] 8. Generative AI suggests active itineraries including hikes and bike tours.

[1461] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[1462] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[1463] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[1464] Prompt Sentence Examples

[1465] "I uploaded a beach image and want you to generate a relaxing itinerary based on it."

[1466] "I captured a mountain scene of autumn leaves from Instagram. Please suggest some exciting travel plans."

[1467] In this way, users can easily obtain a personalized travel plan that matches their emotions at the time. This system is expected to make users' travel experiences more satisfying.

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1:

[1470] The user launches the dedicated application and selects or takes a photo that will trigger the trip. The user's operation is the input, and an image file is obtained as the output. The specific operation is to launch the camera function within the application and support taking photos or selecting images from the gallery.

[1471] Step 2:

[1472] The image acquired by the device is converted to JPEG or PNG format and sent to the server. The input is an image selected or taken by the user, and the output is image data sent to the server. Specifically, the data format is converted using an image conversion library, and the data is uploaded to the server using the HTTP protocol.

[1473] Step 3:

[1474] The server temporarily stores the image data it receives. The input is image data sent from the terminal, and the output is a saved image file. Specifically, it saves the image in the file system and records the storage location in the database.

[1475] Step 4:

[1476] The server inputs the stored image data into the image analysis engine. The stored image data is the input, and image feature data is obtained as the output. Specifically, TensorFlow or OpenCV is used to extract image features and identify tourist spots and destinations.

[1477] Step 5:

[1478] The device activates the camera and microphone to recognize the user's emotions. The input is real-time data of the user's facial expressions and voice, and the output is captured emotional data. Specifically, the camera captures facial expressions and the microphone records the tone of voice.

[1479] Step 6:

[1480] The device sends the captured data to the emotion engine, which then analyzes the user's emotions. The input is captured facial expression data and voice data, and the output is analyzed emotional state data. Specific operations include recognizing emotions using the Microsoft Azure Emotion API or Face API.

[1481] Step 7:

[1482] Based on the identified destination, the generative AI collects information on tourist attractions and nearby activities from the internet. The input is the identified destination information and emotional state data, and the output is a list of tourist information. Specifically, it uses web services and APIs to collect information from tourist guide sites.

[1483] Step 8:

[1484] The generative AI customizes a travel plan based on the collected information and the user's emotional state. The input is a list of tourist information and emotional state data, and the output is a customized travel plan. Specifically, it generates a schedule based on the information and suggests activities according to the user's emotional state.

[1485] Step 9:

[1486] The generative AI estimates costs based on a travel plan. It takes a customized travel plan as input and provides a budget estimate as output. Specifically, it calculates transportation, accommodation, meal costs, etc., and calculates the total amount.

[1487] Step 10:

[1488] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. It takes a customized travel plan and budget estimate as input, and produces a completed "travel itinerary" as output. Specifically, it compiles the entire trip schedule, important contacts, checklists, and more into a document.

[1489] Step 11:

[1490] The server sends the "Travel Itinerary" to the user's device in PDF format or other format. The input is the completed "Travel Itinerary," and the output is the data sent to the user's device. Specific operations include sending the "Travel Itinerary" via email or in-app notifications.

[1491] Step 12:

[1492] The user checks the "travel itinerary" through the application and begins preparing for the trip. The "travel itinerary" is the input, and the trip preparations are completed as the output. The specific operation is to open the "travel itinerary" on the device and check the schedule and activities.

[1493] (Application example 2)

[1494] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1495] Conventional food delivery systems have the problem that it is difficult for users to easily find the appropriate meal based on their preferences and emotions. In addition, suggestions based on food images are limited, and personalized suggestions that take into account the user's individual emotions and moods are not possible. To solve this problem, a system is needed that can customize meal plans based on the images entered by the user and their current emotions, and suggest appropriate restaurants and food delivery services.

[1496] 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.

[1497] In this invention, the server includes means for acquiring an image input by a user, means for analyzing the acquired image, means for generating a meal provision plan based on the analyzed information, means for recognizing the user's emotions and customizing the meal provision plan based on the acquired emotion information, and means for providing the generated meal provision plan to the user. This makes it possible to easily generate and provide a personalized meal provision plan that meets the user's wishes and emotions.

[1498] "User" means an individual or organization that uses this system.

[1499] "Image" refers to visual data stored in digital form.

[1500] "Acquisition" refers to the process of gathering or receiving information or data.

[1501] "Analysis" refers to the process of unraveling the content of data or information and finding meaning.

[1502] "Meal Plan" means a schedule or plan that includes specific dish or meal suggestions.

[1503] "Emotion" refers to data that indicates a user's psychological state and mood.

[1504] "Customization" refers to the process of modifying or optimizing content based on the needs and desires of an individual user.

[1505] "Providing" refers to delivering information or services to users.

[1506] "System" refers to a technological device or application that includes multiple components and functions that work in conjunction with each other.

[1507] To put this invention into practice, it is necessary to build a system that allows users to input images of the food they want to eat, generates a meal plan based on those images, and customizes it according to the user's emotions. The detailed configuration and specific operating procedures of this system are described below.

[1508] Hardware and software used

[1509] Hardware: Smartphone (including camera and microphone)

[1510] Software: Image analysis engine (e.g., Google Cloud Vision API), emotion recognition engine (e.g., Microsoft Azure Face API), generative AI (OpenAI GPT-4), server for data processing and management

[1511] Detailed system configuration

[1512] 1. To receive image input, the user selects or takes a picture of the food they want to eat using the smartphone camera and inputs the image into the application. The device converts the image data into JSON format and sends it to the server.

[1513] 2. Image analysis uses the Google Cloud Vision API to analyze the image data received by the server, and the type and characteristics of the food are identified as a result of the analysis.

[1514] 3. Emotion recognition uses the camera and microphone on the user's smartphone to capture the user's facial expressions and tone of voice, and then uses the Microsoft Azure Face API to analyze the user's emotions, for example, to determine whether they are feeling relaxed or excited.

[1515] 4. Information gathering and suggestion generation are performed using generative AI (OpenAI GPT-4) based on the analysis results. For example, restaurant and menu suggestions are generated using the following prompt:

[1516] If the user wants to relax:

[1517] text

[1518] Suggest restaurants delivering sushi for someone feeling relaxed.

[1519] If the user is in an exciting mood:

[1520] text

[1521] Suggest restaurants delivering spicy Mexican food for someone feeling excited.

[1522] 5. The generation and provision of meal plan involves the generation AI generating an appropriate meal plan based on the proposed content, and the server sending this plan to the user's device and providing it in a format that is easy for the user to check.

[1523] Specific examples

[1524] For example, a user takes a picture of "sushi" with their smartphone and inputs it into a "Smart Food Delivery" app. The system analyzes the image with Google Cloud Vision API, identifying "sushi" as the result. It then uses Microsoft Azure Face API to recognize the user's emotions and determine whether they are in a relaxed mood. OpenAI GPT-4 suggests suitable restaurants using the following prompt:

[1525] text

[1526] Suggest restaurants delivering sushi for someone feeling relaxed.

[1527] This presents the user with a delivery service that offers home-style sushi.

[1528] The present invention can generate a personalized meal plan based on the user's emotions and meal images, providing a suitable dining experience.

[1529] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1530] Step 1:

[1531] A user launches the app on their smartphone and selects or takes a picture of the food they want to eat. The device converts this image data into an appropriate data format (e.g., JSON) and sends it to the server.

[1532] Input: Food image

[1533] Output: Image data sent to the server (JSON format)

[1534] Specific behavior:

[1535] The user takes a picture of the dish with the camera, selects the image on the app, and presses the upload button. The application converts the image data into JSON format and sends it to the server.

[1536] Step 2:

[1537] The server acquires the received image data and analyzes it using an image analysis engine (Google Cloud Vision API). As a result of the analysis, the type and characteristics of the food are identified from the image features.

[1538] Input: Image data sent to the server (JSON format)

[1539] Output: Food type and characteristics (e.g., sushi, pizza)

[1540] Specific behavior:

[1541] The server calls the Google Cloud Vision API, analyzes the characteristics of the food based on the received image data, and obtains the analysis results in JSON format to identify the type of food.

[1542] Step 3:

[1543] The device uses the user's smartphone camera and microphone to capture the user's facial expressions and tone of voice, and then analyzes emotions using an emotion recognition engine (Microsoft Azure Face API).

[1544] Input: User facial and voice data

[1545] Output: User's emotional state (e.g., relaxed, excited)

[1546] Specific behavior:

[1547] The user looks into the smartphone camera to capture their facial expressions and the tone of their voice with the microphone, which are then sent to the Azure Face API, which then sends the analysis results back to the server.

[1548] Step 4:

[1549] Based on the analyzed food information and emotional information, the server uses generative AI (OpenAI GPT-4) to generate restaurant and food suggestions suitable for the user.

[1550] Input: type and characteristics of food, user's emotional state

[1551] Output: A list of suggested restaurants and dishes

[1552] Specific behavior:

[1553] The server queries the AI ​​generator with a prompt based on the analysis information. The AI ​​generator generates a list of restaurants and dishes based on the prompt, and the server retrieves the results.

[1554] Step 5:

[1555] The server transmits the generated meal plan to the user's terminal, and the user confirms it.

[1556] Input: A list of suggested restaurants and dishes

[1557] Output: Meal plan displayed on user's device

[1558] Specific behavior:

[1559] The generated plan is sent to the user's device in JSON format. The application parses the data and displays it to the user as a list or detailed information. The user can then review the proposed information and place an order.

[1560] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1561] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1562] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1563] [Fourth embodiment]

[1564] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1565] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1566] 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).

[1567] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1568] 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.

[1569] 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).

[1570] 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.

[1571] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1572] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1573] 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.

[1574] 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.

[1575] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1576] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1577] To implement this invention, a system is constructed that allows users to input images and generate and provide travel plans based on them. The following is a detailed program process and its explanation.

[1578] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1579] 1. Receiving image input

[1580] The user launches the dedicated application and selects or takes a picture of an image that interests them.

[1581] The terminal acquires the image and converts it into a data format for transmission to the server.

[1582] 2. Image Analysis

[1583] The server receives the received image data and passes it to the image analysis engine.

[1584] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[1585] 3. Travel plan generation

[1586] Based on the information identified, the AI ​​generates a travel plan, including destination attractions, accommodations, transportation options, and a specific itinerary.

[1587] The generative AI creates a detailed schedule for each day and estimates the budget based on this plan.

[1588] 4. Bookmark creation and provision

[1589] The generative AI creates a "travel itinerary" based on the generated travel plan.

[1590] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[1591] Specific examples

[1592] Example 1: Planning a beach trip

[1593] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1594] 2. The server receives the image and instructs the generating AI to analyze it.

[1595] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[1596] 4. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[1597] 5. The AI ​​generator creates daily schedules for things like the Blue Lagoon, sightseeing in Valletta, and a tour of Gozo.

[1598] 6. The generation AI calculates the budget for the entire trip and presents the total amount.

[1599] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[1600] 8. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1601] Example 2: Mountain trip planning

[1602] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1603] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[1604] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[1605] 4. The generation AI collects information on tourist spots in Arashiyama and generates a 3-day, 2-night travel plan based on that information.

[1606] 5. The AI ​​generator assigns tourist spots such as Tenryu-ji Temple, the Bamboo Forest Path, and Arashiyama Park to each day.

[1607] 6. The generative AI calculates the budget for the entire trip and presents the final amount.

[1608] 7. The server creates a detailed "travel itinerary" and sends it to the device.

[1609] 8. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[1610] In this way, this system can automatically generate and provide detailed travel plans to users simply by inputting images, allowing users to easily plan their trips and stay motivated to travel.

[1611] The processing flow will be explained below.

[1612] Step 1:

[1613] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1614] The user can either open the gallery from the app interface or launch the camera to capture an image.

[1615] Step 2:

[1616] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[1617] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[1618] Step 3:

[1619] The terminal transmits the image data to the server.

[1620] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[1621] Step 4:

[1622] The server prepares to analyze the received image data.

[1623] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[1624] Step 5:

[1625] The server instructs the generated AI to analyze the image.

[1626] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[1627] Step 6:

[1628] The generative AI analyzes the image features and extracts the information contained in the image.

[1629] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[1630] For example, "beach," "mountain," "city," etc. may be identified in this step.

[1631] Step 7:

[1632] Based on the analyzed information, the generative AI identifies the destination and surrounding tourist attractions.

[1633] The generative AI searches a database based on the recognized features and lists relevant tourist attractions and accommodations.

[1634] Step 8:

[1635] A server collects detailed tourist information via the Internet.

[1636] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[1637] Step 9:

[1638] The generative AI generates a travel plan based on the collected information.

[1639] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[1640] Step 10:

[1641] Generative AI calculates a budget based on your travel plans.

[1642] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[1643] Step 11:

[1644] The server generates a detailed "travel itinerary."

[1645] The server creates a "travel guide" as rich content including travel plans, details of tourist spots, budget information, maps, links, and more.

[1646] Step 12:

[1647] The server sends the generated "travel guide" to the user's terminal.

[1648] The server provides the bookmark data to the terminal in URL format or directly as data.

[1649] Step 13:

[1650] The terminal receives the "travel guide" and displays it to the user.

[1651] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[1652] Step 14:

[1653] The user checks the "Travel Itinerary" and customizes the content as needed.

[1654] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[1655] In this way, through the above steps, the system allows the user to easily receive a specific travel plan.

[1656] Example 1

[1657] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1658] Modern travelers face the challenge of finding the right travel destination and itinerary on their own from the vast amount of information available. Planning a trip, including selecting destinations and tourist spots, creating an itinerary, and estimating a budget, takes a lot of time and effort. Furthermore, customizing travel plans and meeting user needs is not easy.

[1659] 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.

[1660] In this invention, the server includes means for acquiring images input by a user, means for converting the acquired images into an optimal data format, means for transmitting the acquired images to the server, means for analyzing the images received by the server, means for identifying image features based on the analyzed information and using a generative AI model to determine destinations and tourist spots, means for generating a travel plan based on the identified information, means for estimating a budget based on the generated travel plan, and means for providing the generated travel plan to the user. This reduces the burden of travel planning because a user only needs to input an image to automatically generate a travel plan and be provided with a detailed itinerary and budget.

[1661] "User" means any person or entity that operates the System.

[1662] "Image" refers to visual data provided by a user through a dedicated application.

[1663] "Data format" refers to the particular format in which image data is processed by the server.

[1664] "Server" refers to a computing system for receiving, analyzing, processing data, and providing results.

[1665] "Image analysis" refers to technical methods used to identify features or content in image data.

[1666] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence technology to analyze images and data and automatically generate travel plans.

[1667] A "travel plan" refers to a detailed schedule including destinations, attractions, accommodations, and transportation methods that is generated based on the user's needs.

[1668] "Budgeting" refers to the process of calculating approximate costs based on travel plans.

[1669] A "travel itinerary" refers to a document or digital data that summarizes a generated travel plan.

[1670] "Analyzed information" refers to data such as destinations and tourist spots obtained through image analysis.

[1671] To implement this invention, we will build a system that allows users to input images and generate and provide travel plans based on them. This system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1672] Receiving image input

[1673] The user launches a dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for sending to the server. For example, the device converts the image into JPEG format and sends it to an API endpoint.

[1674] Image analysis

[1675] The server reviews the received image data and passes it to an image analysis engine. A generative AI model analyzes the image features and identifies the destination or tourist spot represented by the image. For example, a generative AI model (e.g., ResNet, YOLO) analyzes the visual features and generates a tag for "beach."

[1676] Identifying destinations and attractions

[1677] The generative AI model identifies the destination shown in the image based on the analysis results. For example, in the case of a beach, it identifies the "main island." The generative AI model then collects information about tourist attractions related to that destination from a database.

[1678] Generate a travel plan

[1679] The generative AI model creates a travel plan based on the collected information, including destination attractions, accommodation, transportation, and a specific schedule. For example, the generative AI model could automatically generate a four-day, three-night itinerary with a daily schedule that includes visiting the beach on the first day and sightseeing in the city on the second day.

[1680] Creating and sending bookmarks

[1681] The generative AI model generates a "travel itinerary" based on the travel plan. The server sends the generated travel itinerary to the user's device. The device displays a notification that the "travel itinerary" has been received, allowing the user to view it.

[1682] Hardware and software used

[1683] Device: Smartphone or tablet (e.g., iPhone, Android device)

[1684] Server: Cloud server environment (e.g. AWS, Google Cloud)

[1685] Generative AI models: Machine learning models (e.g., TensorFlow, PyTorch)

[1686] Prompt Sentence Examples

[1687] Here are some examples of prompts to input to a generative AI model:

[1688] "Analyze this beach image and generate the best itinerary."

[1689] "Generate a trip plan based on this mountain view image."

[1690] Specific examples

[1691] Planning a beach trip

[1692] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1693] 2. The server receives the image and instructs the generative AI model to analyze it.

[1694] 3. A generative AI model analyzes the image and recognizes the beach as a "prime location."

[1695] 4. The generative AI model collects tourist information about the destination and generates a 3-night, 4-day plan.

[1696] 5. The generative AI model creates a daily schedule and calculates the overall budget for the trip.

[1697] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[1698] 7. The user checks the "Travel Itinerary" and begins preparing for their trip.

[1699] Mountain Travel Planning

[1700] 1. A user finds a beautiful mountain scene with autumn leaves on social media and imports a screenshot into a dedicated app.

[1701] 2. The device sends the image to the server, which then requests the generative AI model to analyze it.

[1702] 3. A generative AI model analyzes the image and identifies the location as an "another prime location."

[1703] 4. The generative AI model collects tourist spot information and generates a 2-night, 3-day travel plan based on that information.

[1704] 5. The generative AI model allocates tourist attractions by day and calculates the overall budget for the trip.

[1705] 6. The server creates a detailed "travel itinerary" and sends it to the device.

[1706] 7. The user checks the provided "Travel Itinerary" and begins preparing for their trip.

[1707] The above is an embodiment of this system.

[1708] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1709] Step 1:

[1710] Image acquisition

[1711] The user launches the dedicated application and selects or takes a picture of an image they are interested in. As the user operates the device, it retrieves image data from the photo library. The input here is the image selected or taken by the user, and this image becomes the output displayed within the application.

[1712] Step 2:

[1713] Converting to data format

[1714] The image data acquired by the terminal from the user is converted into the optimal data format for sending to the server. For example, the image data is converted into JPEG format. The input is the image data provided by the user, and the output is the converted JPEG format image.

[1715] Step 3:

[1716] Sending images

[1717] The device sends the converted image data to the server. The image data is sent as a POST request to a specific API endpoint. The input is the image data in JPEG format, and the output is the data sent to the server. The device displays a success message.

[1718] Step 4:

[1719] Image Receipt and Verification

[1720] The server checks the image data received from the terminal and verifies that there is no corruption or format mismatch. The input is the image data that arrived at the server, and the output is the verification result (success or error).

[1721] Step 5:

[1722] Image analysis request

[1723] The server passes image data to the image analysis engine and requests an analysis. The generative AI model analyzes the image's features and identifies the elements represented by the image (e.g., ocean, mountain, city). The input is the image data stored on the server, and the output is the feature extraction results.

[1724] Step 6:

[1725] Determining specific destinations and attractions

[1726] The generative AI model determines destinations and tourist spots based on the identified features. For example, if an image of a beach is used, it will identify "specific islands" and "tourist spots." The input is the feature extraction results, and the output is information on the identified destinations and tourist spots.

[1727] Step 7:

[1728] Generate a travel plan

[1729] The generative AI model generates a travel plan based on the identified destinations and tourist attractions. Specifically, it creates a schedule including accommodations, tourist attractions, and transportation. The input is the identified tourist attractions, and the output is a detailed travel plan.

[1730] Step 8:

[1731] Budget Estimates

[1732] The generative AI model estimates a budget based on a travel plan, calculating the cost of each item (e.g., accommodation, transportation) and calculating the total amount. The input is the details of the travel plan, and the output is the estimated budget.

[1733] Step 9:

[1734] Creating bookmarks

[1735] A generative AI model creates a "travel itinerary" based on a travel plan, including itinerary, attractions to visit, budget, etc. The input is a travel plan and estimated budget, and the output is a detailed itinerary.

[1736] Step 10:

[1737] Send a bookmark

[1738] The server sends the generated "travel itinerary" to the user's device. The user receives the bookmark and is notified so that they can view it. The input is the generated "travel itinerary," and the output is the result of transmission to the user's device.

[1739] Step 11:

[1740] Check bookmarks

[1741] The user checks the "travel itinerary" on the terminal and makes a final check of the details of the travel plan. At this stage, the user begins preparing for the trip. The input is the bookmark sent to the terminal, and the output is the bookmark confirmation result.

[1742] The above is a description of the specific processing steps.

[1743] (Application example 1)

[1744] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1745] Conventional travel plan generation systems analyze images entered by users to suggest specific travel destinations and tourist spots, but they are unable to accommodate diverse user needs, such as meal delivery plans. For this reason, there has been a demand for a system that allows users to easily create both travel plans and meal delivery plans.

[1746] 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.

[1747] In this invention, the server includes means for acquiring images input by the user, means for analyzing the acquired images, means for generating a travel plan or meal provision plan based on the analyzed information, and means for providing the generated travel plan or meal provision plan to the user. This allows the user to easily acquire both the travel plan and the meal provision plan, greatly improving convenience.

[1748] "User" refers to the individual or end user who inputs images using a dedicated application.

[1749] "Means for acquiring images" refers to an interface for importing images input or taken by the user into the system.

[1750] "Means for analyzing images" refers to an algorithm or engine that analyzes acquired image data and identifies or classifies its contents.

[1751] "Travel Plan" refers to a plan that suggests a schedule and budget including specific travel destinations and tourist attractions based on a user's input image.

[1752] A "meal plan" refers to a plan based on an image of a dish entered by a user, including the location where the dish will be served and delivery arrangements.

[1753] "Provision means" refers to a mechanism for sending the generated travel plan or meal plan to the user's terminal so that the user can check and use it.

[1754] "Means for identification" refers to a mechanism for identifying details of a location or dish based on the captured image and extracting related information.

[1755] "Delivery arrangements" refers to the procedure for delivering food to a designated location based on a meal provision plan.

[1756] To implement this invention, it is necessary to build a system that allows a user to input an image and generates and provides a travel plan or meal plan based on that image. This system is composed of the following processes.

[1757] Program Generation

[1758] The system is programmed as follows:

[1759] 1. Receiving image input

[1760] The user launches the dedicated application and selects or takes a picture of an image they are interested in. The device acquires the image and converts it into a data format for transmission to the server.

[1761] 2. Image Analysis

[1762] The server receives the received image data and passes it to an image analysis engine (e.g., OpenAI's CLIP model). The generative AI analyzes the image features and identifies the destination and cuisine depicted in the image.

[1763] 3. Travel or meal plan generation

[1764] Based on the information it identifies, the AI ​​generates a travel plan or meal plan. The travel plan includes destination attractions, accommodations, transportation, and a specific schedule. The meal plan includes identifying food locations and arranging delivery.

[1765] 4. Providing a plan

[1766] The server creates a "travel guide" or a "meal plan" based on the generated travel plan or meal plan. The server sends this information to the user's terminal, allowing the user to view it.

[1767] Hardware and Software Used

[1768] Hardware: Smartphone (iOS / Android)

[1769] Software: OpenAI CLIP model, Google Places API, Uber Eats API, Python, Flask, cloud server such as AWS (Amazon Web Services)

[1770] Specific examples

[1771] Travel plan execution example

[1772] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1773] 2. The server receives the image and instructs the generating AI to analyze it.

[1774] 3. The generative AI analyzes the image and recognizes that the beach is a specific tourist destination.

[1775] 4. The AI ​​generator collects tourist information about the relevant tourist destination and generates a 3-night, 4-day plan.

[1776] 5. Generative AI creates a detailed schedule for each day and calculates the budget for the entire trip.

[1777] 6. The server creates a "travel itinerary" and sends it to the device.

[1778] 7. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1779] Example of meal provision plan implementation

[1780] 1. The user uses a dedicated app to upload an image of paella, and the device sends the image to the server.

[1781] 2. The server receives the image and requests the generating AI to analyze it.

[1782] 3. The generative AI analyzes the image and recognizes the dish as "paella."

[1783] 4. The generating AI uses the Google Places API to identify the locations to serve.

[1784] 5. The generative AI uses the Uber Eats API to arrange food delivery based on the search results.

[1785] 6. The server sends the meal plan to the terminal, and the user confirms and completes the order.

[1786] Prompt Sentence Examples

[1787] Prompt: "What is the dish in this picture?"

[1788] Input image: A food image taken or uploaded by the user

[1789] In this way, the system can automatically generate and provide detailed travel plans and meal plans to users simply by inputting images, allowing users to easily make travel and meal arrangements.

[1790] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1791] Step 1:

[1792] The user launches the dedicated application and selects or takes a picture. The user uploads the image they are interested in to the application, and the device acquires the image. In this step, the image entered by the user is directly saved on the device and converted into a data format for processing.

[1793] Input: An image taken or selected by the user

[1794] Output: Image data saved on the device (converted to a data format)

[1795] Step 2:

[1796] The device sends the acquired image data to the server. Specifically, the device executes a procedure to upload the image data to the server via the Internet. This allows the image data to be stored on the server for analysis.

[1797] Input: Image data stored on the device

[1798] Output: Image data sent to the server

[1799] Step 3:

[1800] The server passes the received image data to an image analysis engine for analysis. The server then calls the analysis engine (e.g., OpenAI's CLIP model), receives the image data as input, and analyzes its features. Specifically, it performs image recognition and feature extraction.

[1801] Input: Image data sent to the server

[1802] Output: Image features (analyzed data)

[1803] Step 4:

[1804] The server identifies specific destinations or dishes based on the analyzed image features. In the case of travel plans, the server retrieves relevant location information from a database. In the case of meal delivery plans, the server identifies the names of dishes and their locations. This provides the basis for generating the next plan.

[1805] Input: Image features (analyzed data)

[1806] Output: Identified destination or cuisine information

[1807] Step 5:

[1808] The generation AI generates a travel plan or meal plan based on the identified information. The travel plan includes a schedule of tourist spots and a budget calculation, while the meal plan includes meal locations and delivery arrangements. Specific operations include creating a schedule with detailed destinations and suggesting delivery options.

[1809] Input: Specific destination or cuisine information

[1810] Output: Generated travel or meal plan

[1811] Step 6:

[1812] The server creates a "travel itinerary" or "meal plan" based on the generated plan and sends it to the user's device. The server then converts the results into a format that is easy for the user to view (such as text or PDF) and sends the data to the device.

[1813] Input: Generated travel or meal plan

[1814] Output: "Travel guide" or "Meal plan" (sent to user device)

[1815] Step 7:

[1816] The user checks the provided "travel itinerary" or "meal plan" and proceeds with travel or meal arrangements. Specific operations include checking the plan contents and making the necessary reservations and arrangements.

[1817] Input: "Travel guide" or "Meal plan" (sent to user device)

[1818] Output: Travel or dining arrangements confirmed by the user

[1819] 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.

[1820] To implement this invention, a system for generating travel plans based on user-entered images is combined with an emotion engine that recognizes the user's emotions and customizes the travel plans based on those emotions. The following is a detailed program process and its explanation for this system.

[1821] The system starts when a user takes or selects an image using a dedicated application and sends the image to a server.

[1822] 1. Receiving image input

[1823] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1824] The terminal acquires the image and converts it into a data format for transmission to the server.

[1825] 2. Image Analysis

[1826] The server receives the received image data and passes it to the image analysis engine.

[1827] The generative AI analyzes the features of the image and identifies the destination or tourist spot indicated in the image.

[1828] 3. Emotion recognition

[1829] The device uses a camera and microphone to capture facial expressions and tone of voice to recognize the user's emotions.

[1830] The emotion engine analyzes the captured data and recognizes the user's emotions, for example, determining whether the user is excited or relaxed.

[1831] 4. Information Collection

[1832] Based on the information identified by the generative AI, a list of tourist attractions at and around the destination is created.

[1833] The server collects detailed tourist information (e.g., travel guide reviews, transportation options, accommodation information, etc.) via the Internet.

[1834] 5. Travel Plan Generation

[1835] Based on the information collected, the generative AI generates a travel plan that corresponds to the user's emotions.

[1836] For example, if the user is feeling relaxed, a leisurely schedule will be suggested, whereas if the user is feeling excited, a schedule including many active activities will be suggested.

[1837] 6. Budget Estimates

[1838] Generative AI will estimate a budget based on your travel plans, including transportation, accommodation, meals, etc.

[1839] 7. Bookmark Creation and Provision

[1840] The generative AI creates a "travel itinerary" based on the generated travel plan.

[1841] The server sends this "travel guide" to the user's terminal, allowing the user to view it.

[1842] Specific examples

[1843] Example 1: Planning a beach trip

[1844] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device sends the image to the server.

[1845] 2. The server receives the image and instructs the generating AI to analyze it.

[1846] 3. The generative AI analyzes the image and recognizes the beach as the "Blue Lagoon in Malta."

[1847] 4. The emotion engine identifies the user's emotions and recognizes that the user wants to relax.

[1848] 5. The AI ​​generator collects tourist information about Malta and generates a 3-night, 4-day itinerary.

[1849] 6. Generative AI suggests relaxing itineraries, creating plans that include hot spring resorts and relaxing activities, for example.

[1850] 7. The generation AI calculates the budget for the entire trip and presents the total amount.

[1851] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[1852] 9. The user can check the "Travel Itinerary" and begin preparations to carry out their travel plans.

[1853] Example 2: Mountain trip planning

[1854] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1855] 2. The device sends the image to the server, which then requests the generating AI to analyze it.

[1856] 3. The generative AI analyzes the image and identifies the location as "Arashiyama, Kyoto."

[1857] 4. The emotion engine identifies the user's emotions and recognizes that the user is looking for an exciting activity.

[1858] 5. The generation AI collects information on tourist spots in Arashiyama and generates a 2-night, 3-day travel plan based on that information.

[1859] 6. Generative AI suggests active itineraries and sets out plans that include, for example, hikes or bike tours.

[1860] 7. The generative AI calculates the budget for the entire trip and presents the final amount.

[1861] 8. The server creates a detailed "travel itinerary" and sends it to the device.

[1862] 9. The user can check the provided "Travel Itinerary" and start preparing for their trip.

[1863] In this way, by combining the emotion engine, the system can provide more personalized travel plans according to the user's emotions, allowing users to have a more satisfying travel experience.

[1864] The processing flow will be explained below.

[1865] Step 1:

[1866] The user launches a dedicated application and selects or takes a picture that will trigger the trip.

[1867] The user can either open the gallery from the app interface or launch the camera to capture an image.

[1868] Step 2:

[1869] The terminal saves the selected or captured image in the application and prepares to send the image data to the server.

[1870] The device converts the image file into the appropriate format (e.g., JPEG, PNG) and prepares it for transmission to the server.

[1871] Step 3:

[1872] The terminal transmits the image data to the server.

[1873] The terminal uploads the image data to a specific endpoint on the server via an internet connection.

[1874] Step 4:

[1875] The server prepares to analyze the received image data.

[1876] The server temporarily stores the image data and performs pre-processing before passing it to the generation AI.

[1877] Step 5:

[1878] The server instructs the generated AI to analyze the image.

[1879] The server passes the image data to the image analysis engine and commands it to start the analysis process.

[1880] Step 6:

[1881] The generative AI analyzes the image features and extracts the information contained in the image.

[1882] Generative AI uses image recognition algorithms to identify specific elements in an image, such as landmarks, landscapes, and architecture.

[1883] For example, "beach," "mountain," "city," etc. may be identified in this step.

[1884] Step 7:

[1885] The device uses a camera and microphone to capture the user's facial expressions and tone of voice to recognize their emotions.

[1886] The device will use facial recognition and voice analysis technologies to more accurately understand the user's current emotional state.

[1887] Step 8:

[1888] The emotion engine analyzes the captured data and recognizes the user's emotions.

[1889] The emotion engine determines whether the user is excited, relaxed, or in some other emotional state.

[1890] Step 9:

[1891] The generative AI identifies the destination and surrounding tourist attractions based on the analyzed image information and emotion data from the emotion engine.

[1892] The generative AI searches a database based on the recognized features and emotion data to list relevant tourist attractions and accommodations.

[1893] Step 10:

[1894] A server collects detailed tourist information via the Internet.

[1895] The server calls various APIs (e.g., Google Maps API, Travel Guide API) to obtain detailed information, reviews, images, prices, etc. about tourist spots.

[1896] Step 11:

[1897] The generative AI generates a travel plan based on the collected information.

[1898] The generative AI creates an optimal itinerary for the user and allocates tourist attractions, restaurants, and accommodations to the schedule.

[1899] The generative AI selects activities and locations that match the user's emotions.

[1900] Step 12:

[1901] Generative AI calculates a budget based on your travel plans.

[1902] The generation AI tally up various expenses such as travel, accommodation, meals, and admission fees, and estimates the total budget.

[1903] Step 13:

[1904] Generative AI generates a detailed "travel itinerary."

[1905] The generative AI creates a "travel itinerary" as rich content including travel plans, details of tourist attractions, budget information, maps, links, etc.

[1906] Step 14:

[1907] The server sends the generated "travel guide" to the user's terminal.

[1908] The server provides the bookmark data to the terminal in URL format or directly as data.

[1909] Step 15:

[1910] The terminal receives the "travel guide" and displays it to the user.

[1911] The device displays the received bookmark data in a dedicated application, allowing the user to view and edit it.

[1912] Step 16:

[1913] The user checks the "Travel Itinerary" and customizes the content as needed.

[1914] The user can finalize the trip based on the provided plan and, if necessary, make individual changes or additions.

[1915] Through these steps, the system allows users to easily receive specific travel plans. By combining it with an emotion engine, it is possible to provide more personalized travel plans that reflect the user's emotions.

[1916] Example 2

[1917] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1918] Conventional travel planning systems often generate uniform travel plans based on user-entered information, without taking into account the individual user's emotions or mood at the time. As a result, the travel plans may not meet the user's expectations, resulting in low satisfaction. Furthermore, they lack the ability to flexibly customize plans based on the user's emotions or provide specific travel budget estimates.

[1919] 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.

[1920] In this invention, the server includes means for acquiring an image input by a user, means for transmitting the acquired image to the server, means for the server to analyze the image, means for the terminal to recognize the user's emotion, means for customizing a travel plan based on the emotion, and means for providing the generated travel plan to the user, thereby enabling the generation of a more personalized travel plan based on the user's emotion.

[1921] "User" refers to an individual or organization that uses the system to make travel plans.

[1922] "Image" refers to digital data containing visual information captured or selected by a user, and is saved in formats such as JPEG or PNG.

[1923] "Terminal" refers to a computer device or mobile device operated by a user, which performs tasks such as acquiring images and collecting emotional data.

[1924] "Server" refers to the computing resources and software for receiving and analyzing image data, and generating and providing itineraries.

[1925] An "emotion engine" refers to a hardware and software system that analyzes and recognizes a user's emotional state from facial expressions, tone of voice, etc.

[1926] "Travel Itinerary" refers to a detailed plan including a user's travel destinations, schedule, activities, and estimated budget.

[1927] "Analysis" refers to the process of processing the acquired image data and emotion data, extracting features, and analyzing them.

[1928] "Features" refer to significant data patterns and attributes extracted from image and emotion data, and are elements that identify destinations, user emotions, etc.

[1929] "Tourist attractions" refer to places and facilities that tourists visit, including tourist destinations and activities.

[1930] "Customization" refers to the process of individually adjusting and modifying a travel plan based on the user's feelings and desires.

[1931] A "bookmark" is a document or digital file that details your ultimate travel plans, including schedules and important information.

[1932] MODE FOR CARRYING OUT THE INVENTION

[1933] To implement this invention, a system including a user terminal, a server, an image analysis engine, an emotion engine, and a generative AI model is required. This system generates a travel plan based on images entered by the user and analyzes the user's emotions to customize the travel plan.

[1934] 1. Receiving image input

[1935] The user launches a dedicated application and selects or takes a photo of the image that will trigger the trip. The device acquires the image and converts it into a data format (e.g., JPEG or PNG format) for transmission to the server.

[1936] 2. Sending and receiving images

[1937] The device sends image data to a server via the Internet, and the server receives the image data and temporarily stores it.

[1938] 3. Image Analysis

[1939] The image data stored on the server is input into an image analysis engine (e.g., TensorFlow or OpenCV). The generative AI extracts image features and identifies the destination or tourist spot indicated by the image. For example, it can recognize an image of a beach as a "famous tourist spot."

[1940] 4. Preparation for Emotion Recognition

[1941] The device activates the camera and microphone to recognize the user's emotions, capturing the user's facial expressions and tone of voice and collecting data in real time.

[1942] 5. Emotion analysis

[1943] The device sends the captured data to an emotion engine (e.g., Microsoft Azure Emotion API or Face API), which analyzes the data and recognizes the user's emotion. For example, it determines whether the user is relaxed or excited.

[1944] 6. Information Collection

[1945] Based on the destination specified, the AI ​​collects tourist attractions and nearby activities from the internet. For example, it uses travel guide review sites and web services. The server organizes the necessary information and passes it to the AI.

[1946] 7. Travel Plan Generation

[1947] The generative AI customizes travel plans based on the collected information and the user's emotional state. For example, if the user wants to relax, it will suggest a schedule that includes hot spring resorts and spas. If the user is excited, it will create a schedule that includes more active activities like hiking and surfing.

[1948] 8. Budget Estimates

[1949] The generation AI estimates costs based on the travel plan, including transportation, accommodation, and meals, and then sends the estimate to the server.

[1950] 9. Bookmark Creation and Provision

[1951] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. The itinerary includes the entire trip schedule, important contact information, and a checklist. The server then sends this "travel itinerary" to the user's device in PDF format or other format. The user can then view the "travel itinerary" through the application and begin preparing for their trip.

[1952] Specific examples

[1953] Example 1: Planning a beach trip

[1954] 1. Using a dedicated app, the user selects an image of a beautiful beach, and the device converts the image to JPEG format.

[1955] 2. The device sends a JPEG image to the server, which receives and saves the image.

[1956] 3. The server uses an image analysis engine (e.g., TensorFlow) to extract image features.

[1957] 4. The generative AI analyzes the image of the beach and identifies it as a "famous tourist destination."

[1958] 5. The device activates the camera and captures the user's facial expression.

[1959] 6. The emotion engine analyzes the user's facial expression data and recognizes that the user wants to relax.

[1960] 7. The generation AI collects tourist information from the internet and generates a 3-night, 4-day travel plan.

[1961] 8. Generative AI suggests a relaxing itinerary that includes hot spring resorts and spas.

[1962] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[1963] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[1964] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[1965] Example 2: Mountain trip planning

[1966] 1. A user finds a beautiful mountain scene with autumn leaves on Instagram and imports a screenshot into a dedicated app.

[1967] 2. The device sends the screenshot to the server, which receives and saves the image.

[1968] 3. The server uses an image analysis engine (e.g., OpenCV) to extract image features.

[1969] 4. The generative AI analyzes the image and identifies the location as a "famous tourist spot."

[1970] 5. The device activates the microphone and captures the tone of the user's voice.

[1971] 6. The emotion engine analyzes tone of voice data and recognizes that the user is in an excited mood.

[1972] 7. The generation AI collects tourist spot information from the internet and generates a 2-night, 3-day travel plan.

[1973] 8. Generative AI suggests active itineraries including hikes and bike tours.

[1974] 9. The generation AI calculates the budget for the entire trip (e.g., transportation, accommodation, meals, etc.) and calculates the total amount.

[1975] 10. The server creates a detailed "travel itinerary" in PDF format and sends it to the device.

[1976] 11. The user checks the travel itinerary through the application and begins preparing for their trip.

[1977] Prompt Sentence Examples

[1978] "I uploaded a beach image and want you to generate a relaxing itinerary based on it."

[1979] "I captured a mountain scene of autumn leaves from Instagram. Please suggest some exciting travel plans."

[1980] In this way, users can easily obtain a personalized travel plan that matches their emotions at the time. This system is expected to make users' travel experiences more satisfying.

[1981] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1982] Step 1:

[1983] The user launches the dedicated application and selects or takes a photo that will trigger the trip. The user's operation is the input, and an image file is obtained as the output. The specific operation is to launch the camera function within the application and support taking photos or selecting images from the gallery.

[1984] Step 2:

[1985] The image acquired by the device is converted to JPEG or PNG format and sent to the server. The input is an image selected or taken by the user, and the output is image data sent to the server. Specifically, the data format is converted using an image conversion library, and the data is uploaded to the server using the HTTP protocol.

[1986] Step 3:

[1987] The server temporarily stores the image data it receives. The input is image data sent from the terminal, and the output is a saved image file. Specifically, it saves the image in the file system and records the storage location in the database.

[1988] Step 4:

[1989] The server inputs the stored image data into the image analysis engine. The stored image data is the input, and image feature data is obtained as the output. Specifically, TensorFlow or OpenCV is used to extract image features and identify tourist spots and destinations.

[1990] Step 5:

[1991] The device activates the camera and microphone to recognize the user's emotions. The input is real-time data of the user's facial expressions and voice, and the output is captured emotional data. Specifically, the camera captures facial expressions and the microphone records the tone of voice.

[1992] Step 6:

[1993] The device sends the captured data to the emotion engine, which then analyzes the user's emotions. The input is captured facial expression data and voice data, and the output is analyzed emotional state data. Specific operations include recognizing emotions using the Microsoft Azure Emotion API or Face API.

[1994] Step 7:

[1995] Based on the identified destination, the generative AI collects information on tourist attractions and nearby activities from the internet. The input is the identified destination information and emotional state data, and the output is a list of tourist information. Specifically, it uses web services and APIs to collect information from tourist guide sites.

[1996] Step 8:

[1997] The generative AI customizes a travel plan based on the collected information and the user's emotional state. The input is a list of tourist information and emotional state data, and the output is a customized travel plan. Specifically, it generates a schedule based on the information and suggests activities according to the user's emotional state.

[1998] Step 9:

[1999] The generative AI estimates costs based on a travel plan. It takes a customized travel plan as input and provides a budget estimate as output. Specifically, it calculates transportation, accommodation, meal costs, etc., and calculates the total amount.

[2000] Step 10:

[2001] The generative AI creates a "travel itinerary" based on the final travel plan and budget estimate. It takes a customized travel plan and budget estimate as input, and produces a completed "travel itinerary" as output. Specifically, it compiles the entire trip schedule, important contacts, checklists, and more into a document.

[2002] Step 11:

[2003] The server sends the "Travel Itinerary" to the user's device in PDF format or other format. The input is the completed "Travel Itinerary," and the output is the data sent to the user's device. Specific operations include sending the "Travel Itinerary" via email or in-app notifications.

[2004] Step 12:

[2005] The user checks the "travel itinerary" through the application and begins preparing for the trip. The "travel itinerary" is the input, and the trip preparations are completed as the output. The specific operation is to open the "travel itinerary" on the device and check the schedule and activities.

[2006] (Application example 2)

[2007] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2008] Conventional food delivery systems have the problem that it is difficult for users to easily find the appropriate meal based on their preferences and emotions. In addition, suggestions based on food images are limited, and personalized suggestions that take into account the user's individual emotions and moods are not possible. To solve this problem, a system is needed that can customize meal plans based on the images entered by the user and their current emotions, and suggest appropriate restaurants and food delivery services.

[2009] 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.

[2010] In this invention, the server includes means for acquiring an image input by a user, means for analyzing the acquired image, means for generating a meal provision plan based on the analyzed information, means for recognizing the user's emotions and customizing the meal provision plan based on the acquired emotion information, and means for providing the generated meal provision plan to the user. This makes it possible to easily generate and provide a personalized meal provision plan that meets the user's wishes and emotions.

[2011] "User" means an individual or organization that uses this system.

[2012] "Image" refers to visual data stored in digital form.

[2013] "Acquisition" refers to the process of gathering or receiving information or data.

[2014] "Analysis" refers to the process of unraveling the content of data or information and finding meaning.

[2015] "Meal Plan" means a schedule or plan that includes specific dish or meal suggestions.

[2016] "Emotion" refers to data that indicates a user's psychological state and mood.

[2017] "Customization" refers to the process of modifying or optimizing content based on the needs and desires of an individual user.

[2018] "Providing" refers to delivering information or services to users.

[2019] "System" refers to a technological device or application that includes multiple components and functions that work in conjunction with each other.

[2020] To put this invention into practice, it is necessary to build a system that allows users to input images of the food they want to eat, generates a meal plan based on those images, and customizes it according to the user's emotions. The detailed configuration and specific operating procedures of this system are described below.

[2021] Hardware and software used

[2022] Hardware: Smartphone (including camera and microphone)

[2023] Software: Image analysis engine (e.g., Google Cloud Vision API), emotion recognition engine (e.g., Microsoft Azure Face API), generative AI (OpenAI GPT-4), server for data processing and management

[2024] Detailed system configuration

[2025] 1. To receive image input, the user selects or takes a picture of the food they want to eat using the smartphone camera and inputs the image into the application. The device converts the image data into JSON format and sends it to the server.

[2026] 2. Image analysis uses the Google Cloud Vision API to analyze the image data received by the server, and the type and characteristics of the food are identified as a result of the analysis.

[2027] 3. Emotion recognition uses the camera and microphone on the user's smartphone to capture the user's facial expressions and tone of voice, and then uses the Microsoft Azure Face API to analyze the user's emotions, for example, to determine whether they are feeling relaxed or excited.

[2028] 4. Information gathering and suggestion generation are performed using generative AI (OpenAI GPT-4) based on the analysis results. For example, restaurant and menu suggestions are generated using the following prompt:

[2029] If the user wants to relax:

[2030] text

[2031] Suggest restaurants delivering sushi for someone feeling relaxed.

[2032] If the user is in an exciting mood:

[2033] text

[2034] Suggest restaurants delivering spicy Mexican food for someone feeling excited.

[2035] 5. The generation and provision of meal plan involves the generation AI generating an appropriate meal plan based on the proposed content, and the server sending this plan to the user's device and providing it in a format that is easy for the user to check.

[2036] Specific examples

[2037] For example, a user takes a picture of "sushi" with their smartphone and inputs it into a "Smart Food Delivery" app. The system analyzes the image with Google Cloud Vision API, identifying "sushi" as the result. It then uses Microsoft Azure Face API to recognize the user's emotions and determine whether they are in a relaxed mood. OpenAI GPT-4 suggests suitable restaurants using the following prompt:

[2038] text

[2039] Suggest restaurants delivering sushi for someone feeling relaxed.

[2040] This presents the user with a delivery service that offers home-style sushi.

[2041] The present invention can generate a personalized meal plan based on the user's emotions and meal images, providing a suitable dining experience.

[2042] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2043] Step 1:

[2044] A user launches the app on their smartphone and selects or takes a picture of the food they want to eat. The device converts this image data into an appropriate data format (e.g., JSON) and sends it to the server.

[2045] Input: Food image

[2046] Output: Image data sent to the server (JSON format)

[2047] Specific behavior:

[2048] The user takes a picture of the dish with the camera, selects the image on the app, and presses the upload button. The application converts the image data into JSON format and sends it to the server.

[2049] Step 2:

[2050] The server acquires the received image data and analyzes it using an image analysis engine (Google Cloud Vision API). As a result of the analysis, the type and characteristics of the food are identified from the image features.

[2051] Input: Image data sent to the server (JSON format)

[2052] Output: Food type and characteristics (e.g., sushi, pizza)

[2053] Specific behavior:

[2054] The server calls the Google Cloud Vision API, analyzes the characteristics of the food based on the received image data, and obtains the analysis results in JSON format to identify the type of food.

[2055] Step 3:

[2056] The device uses the user's smartphone camera and microphone to capture the user's facial expressions and tone of voice, and then analyzes emotions using an emotion recognition engine (Microsoft Azure Face API).

[2057] Input: User facial and voice data

[2058] Output: User's emotional state (e.g., relaxed, excited)

[2059] Specific behavior:

[2060] The user looks into the smartphone camera to capture their facial expressions and the tone of their voice with the microphone, which are then sent to the Azure Face API, which then sends the analysis results back to the server.

[2061] Step 4:

[2062] Based on the analyzed food information and emotional information, the server uses generative AI (OpenAI GPT-4) to generate restaurant and food suggestions suitable for the user.

[2063] Input: type and characteristics of food, user's emotional state

[2064] Output: A list of suggested restaurants and dishes

[2065] Specific behavior:

[2066] The server queries the AI ​​generator with a prompt based on the analysis information. The AI ​​generator generates a list of restaurants and dishes based on the prompt, and the server retrieves the results.

[2067] Step 5:

[2068] The server transmits the generated meal plan to the user's terminal, and the user confirms it.

[2069] Input: A list of suggested restaurants and dishes

[2070] Output: Meal plan displayed on user's device

[2071] Specific behavior:

[2072] The generated plan is sent to the user's device in JSON format. The application parses the data and displays it to the user as a list or detailed information. The user can then review the proposed information and place an order.

[2073] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2075] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2076] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2077] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2078] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2079] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2080] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2081] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2082] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2083] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2084] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2085] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2086] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2087] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2088] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2089] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2090] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2091] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2092] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2093] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2094] The following is further disclosed regarding the above embodiment.

[2095] (Claim 1)

[2096] means for acquiring an image input by a user;

[2097] means for analyzing the acquired images;

[2098] means for generating a travel plan based on the analyzed information;

[2099] means for providing the generated travel plan to a user;

[2100] A system including:

[2101] (Claim 2)

[2102] The system of claim 1 , further comprising means for identifying a destination and associated tourist attractions based on the captured image.

[2103] (Claim 3)

[2104] 10. The system of claim 1, further comprising means for estimating a budget based on the travel plan.

[2105] (Claim 4)

[2106] 10. The system of claim 1, further comprising means for generating a travel itinerary for the user through the provided travel plan.

[2107] (Claim 5)

[2108] 2. The system according to claim 1, further comprising means for collecting detailed tourist information via the Internet based on the information extracted by the analysis means.

[2109] "Example 1"

[2110] (Claim 1)

[2111] means for acquiring an image input by a user;

[2112] A means for converting the acquired image into an optimal data format;

[2113] means for transmitting the acquired image to a server;

[2114] means for analyzing the image received by the server;

[2115] Using generative AI models to identify image features and determine destinations and tourist attractions based on the analyzed information;

[2116] means for generating a travel plan based on the identified information;

[2117] A means for estimating a budget based on the generated travel plan;

[2118] means for providing the generated travel plan to a user;

[2119] A system including:

[2120] (Claim 2)

[2121] The system of claim 1 , further comprising means for identifying a destination and associated tourist attractions based on the captured image.

[2122] (Claim 3)

[2123] 10. The system of claim 1, further comprising means for identifying image features using a generative AI model to determine destinations and tourist attractions.

[2124] "Application Example 1"

[2125] (Claim 1)

[2126] means for acquiring an image input by a user;

[2127] means for analyzing the acquired images;

[2128] means for generating a travel plan or a meal plan based on the analyzed information;

[2129] means for providing the generated travel plan or meal plan to a user;

[2130] A system including:

[2131] (Claim 2)

[2132] The system of claim 1 , further comprising means for identifying a destination and associated tourist attractions or cuisine and serving locations based on the captured image.

[2133] (Claim 3)

[2134] 10. The system of claim 1, further comprising means for estimating a budget based on a travel plan or means for making delivery arrangements based on a meal plan.

[2135] "Example 2: Combining Emotion Engines"

[2136] (Claim 1)

[2137] means for acquiring an image input by a user;

[2138] means for transmitting the acquired image to a server;

[2139] a means for the server to analyze the image;

[2140] A means for the terminal to recognize the user's emotion;

[2141] A means to customize travel plans based on emotions;

[2142] means for providing the generated travel plan to a user;

[2143] A system including:

[2144] (Claim 2)

[2145] The system of claim 1 , further comprising means for identifying a destination and associated tourist attractions based on the captured image.

[2146] (Claim 3)

[2147] 10. The system of claim 1, further comprising means for estimating a budget based on the travel plan.

[2148] "Application example 2 when combining emotion engines"

[2149] (Claim 1)

[2150] means for acquiring an image input by a user;

[2151] means for analyzing the acquired images;

[2152] means for generating a meal plan based on the analyzed information;

[2153] means for recognizing a user's emotion and customizing a meal plan based on the acquired emotion information;

[2154] means for providing the generated meal plan to a user;

[2155] A system including:

[2156] (Claim 2)

[2157] 10. The system of claim 1, further comprising means for identifying a type of food and an associated source based on the captured image.

[2158] (Claim 3)

[2159] 10. The system of claim 1, further comprising means for estimating costs based on the meal plan. [Explanation of symbols]

[2160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring an image input by a user; means for analyzing the acquired images; means for generating a travel plan based on the analyzed information; means for providing the generated travel plan to a user; A system including:

2. The system of claim 1 , further comprising means for identifying a destination and associated tourist attractions based on the captured image.

3. The system of claim 1 further comprising means for estimating a budget based on the travel plan.

4. The system of claim 1 further comprising means for generating a travel itinerary for the user through the provided travel plan.

5. 2. The system according to claim 1, further comprising means for collecting detailed tourist information via the Internet based on the information extracted by the analysis means.

Images