System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a travel request input from a user; means for suggesting appropriate travel destinations, activities, lodging locations, and modes of transportation based on the travel request using generated AI; means for making reservations for the suggested travel destinations, activities, lodging locations, and modes of transportation; and means for transmitting the suggestions and reservation information to the user's device.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] In recent years, planning a trip has become extremely cumbersome, requiring users to gather a large amount of information and make reservations, placing a significant burden on young travelers in particular. Furthermore, there are concerns about the reliability of travel information available online, leading users to expend a great deal of time and effort when making their own plans. To address these issues, it is desirable to provide users with travel plans tailored to their preferences in a fast and reliable manner, and then to make the necessary reservations all at once based on those plans. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including: means for receiving a travel request input by a user; means for using a generating AI to suggest appropriate travel destinations, activities, accommodations, and means of transportation based on the travel request; means for booking the suggested travel destinations, activities, accommodations, and means of transportation; and means for transmitting the suggestion and reservation information to a user's terminal. This allows users to obtain optimal travel plans based on their preferences without unnecessary hassle, and frees them from the hassle of travel planning through reliable travel information and bulk booking arrangements.

[0006] "User" means any person or entity that submits a travel request using the System.

[0007] A "terminal" is a device that a user uses to access the system, send requests, and check received information, and specifically includes smartphones, tablets, PCs, etc.

[0008] A "travel request" is a travel request or desired condition that a user inputs into the system, and includes information such as destination, itinerary, budget, and activities.

[0009] "Generative AI" is an artificial intelligence that analyzes travel requests received from users and, based on that, suggests optimal travel destinations, activities, accommodations, and transportation methods.

[0010] "Suggestions" are candidates for travel destinations, activities, accommodations, transportation, etc. that the generative AI creates based on the user's travel request.

[0011] A "travel destination" is a place or area that a user plans to visit.

[0012] An "activity" is a sightseeing or experience that a user can have at their travel destination.

[0013] "Accommodation" refers to a facility such as a hotel or inn where a user stays during a trip.

[0014] "Transportation" refers to the means of transportation used by the user when traveling, and specifically includes trains, airplanes, buses, rental cars, etc.

[0015] "Reservation" refers to the procedure by which a user actually uses the proposed accommodations and transportation.

[0016] The term "system" refers to a series of processes included in the present invention and the hardware and software for executing the processes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] As an embodiment of this invention, we will explain a specific implementation method of a system for making holiday plans using generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0039] 1. User submits request

[0040] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0041] Users enter travel requests such as, "I'd like to go on a hot spring trip next weekend. My budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." in a form within the app or on a web page.

[0042] Once you have completed entering the information, click the "Submit" button to send the request data to the server.

[0043] 2. Server request reception and analysis

[0044] The server receives the request data from the user.

[0045] The server converts the received data into an appropriate format and prepares it for passing to the generation AI.

[0046] 3. Proposal creation using generative AI

[0047] The generation AI analyzes the user request data passed from the server, specifically extracting the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[0048] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0049] The generative AI creates a travel plan like this:

[0050] Travel destination: Hakone Onsen

[0051] Activities: Walking around the lake, visiting hot springs

[0052] Accommodation: XX Hot Springs Inn (2 nights, 15,000 yen per person per night)

[0053] Transportation: Round-trip Shinkansen ticket (total 15,000 yen)

[0054] The created proposal is returned to the server.

[0055] 4. Server Proposal Submission

[0056] The server converts the proposed data received from the generation AI into a format that can be shown to the user.

[0057] The server transmits the proposal data to the user's terminal so that the user can view it.

[0058] 5. User Verification and Authorization

[0059] The user checks the proposal sent from the server on the terminal.

[0060] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0061] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0062] 6. Reservation arrangements by the server

[0063] The server receives an "approval" request from the user.

[0064] The server makes online reservations for accommodation and transportation suggested by the AI.

[0065] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0066] Execute payment processing to complete Shinkansen ticket reservations.

[0067] Make sure your booking is confirmed.

[0068] 7. Notification of Final Travel Plans

[0069] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0070] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0071] Specific examples

[0072] For example, suppose a user wishes to "enjoy a hot spring trip next weekend." In this case, the user accesses the system using a terminal and enters a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests appropriate accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation and notifies the user of the final travel plan. This allows users to plan their trip with peace of mind without any complicated work.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user accesses the system using a terminal, inputs a travel request (e.g., "I want to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and clicks the "Submit" button.

[0076] Step 2:

[0077] The server receives the travel request sent by the user, converts the request data into an appropriate format, and prepares it for passing to the generation AI.

[0078] Step 3:

[0079] The server passes the user's travel request data to the generation AI, which then analyzes the request data and extracts the user's desired conditions.

[0080] Step 4:

[0081] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[0082] Step 5:

[0083] The generative AI creates a travel plan, providing specific suggestions such as: destination: Hakone Hot Springs, activities: lakeside walks and hot spring tours, accommodation: XX hot spring inn (two nights, 15,000 yen per person per night), transportation: round-trip Shinkansen ticket (total 15,000 yen).

[0084] Step 6:

[0085] The generation AI returns the created proposal to the server, which then converts the proposal data received from the generation AI into a format that can be shown to the user.

[0086] Step 7:

[0087] The server sends the proposal data to the user's device so that the user can view it. The user then checks the proposal on the device.

[0088] Step 8:

[0089] If the user is satisfied with the proposal, they click the "Approve" button. If they are not satisfied with the proposal, they can change the conditions and submit the request again.

[0090] Step 9:

[0091] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the AI.

[0092] Step 10:

[0093] The server accesses the reservation website of the hot spring inn and confirms the accommodation reservation. It also processes the payment to complete the Shinkansen ticket reservation.

[0094] Step 11:

[0095] The server confirms the reservation and generates a final itinerary including the reservation confirmation information (confirmation number, accommodation details, and transportation information).

[0096] Step 12:

[0097] The server transmits the final itinerary to the user's terminal, allowing the user to confirm the details of the trip. The user confirms the final itinerary on the terminal and proceeds with preparations for the trip.

[0098] This series of steps allows users to eliminate cumbersome tasks and smoothly plan their trip.

[0099] Example 1

[0100] 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."

[0101] In today's modern lifestyle, planning a trip in a busy daily life is cumbersome and time-consuming, and many users find it stressful to coordinate the details of their trip. An effective system to solve this problem is needed.

[0102] 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.

[0103] In this invention, the server includes means for receiving a travel request input from a user, means for converting the travel request data into JSON format and preparing for analysis, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation based on the travel request, means for booking the suggested travel destinations, activities, accommodations, and transportation, means for sending the suggestion and booking information to the user's device, means for confirming the booking with the user's approval and generating a final travel plan, and means for sending the final travel plan to the user's device and notifying the user of the trip details. This allows the user to smoothly plan their trip without having to make complicated adjustments.

[0104] "User" means any person or entity that accesses the System and enters and submits a Travel Request.

[0105] "Terminal" refers to an electronic device, such as a smartphone, tablet, or personal computer, that a user uses to access the system.

[0106] "Travel request" refers to data including travel wishes and conditions that a user inputs and transmits to the system.

[0107] "Generative AI" refers to an artificial intelligence model that analyzes incoming travel requests and suggests appropriate destinations, activities, accommodations and transportation options.

[0108] "Server" refers to an electronic computer that receives a travel request from a user, passes it to the generating AI, and transmits the proposed data to the user's terminal.

[0109] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for storing and exchanging structured data.

[0110] "Proposal" refers to a plan that compiles information on travel destinations, activities, accommodations, and transportation generated by the Generating AI based on a travel request.

[0111] "Confirming reservation" refers to the action of the user approving the proposal and then the server officially reserving accommodation and transportation.

[0112] The "final travel plan" refers to a plan including detailed travel information that is generated by the server after the reservation is confirmed and sent to the user.

[0113] "Internet information" refers to information about travel destinations and activities obtained from websites and online databases.

[0114] This invention relates to a travel planning support system that utilizes generative AI, and has the function of proposing optimal travel plans based on the user's travel requests, confirming the reservations, and notifying the user. This system is mainly composed of four main elements: the user, the terminal, the server, and generative AI.

[0115] Users access the system using devices such as smartphones, tablets, and personal computers. The access method is a dedicated app or a web browser. Users input travel requests in a form within the app or on a web page. For example, a user might input a request such as, "I'd like to take a trip to a hot spring next weekend. My budget should be under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." By sending this request, data is sent to the server.

[0116] The server receives the request data sent by the user. It converts the received data into an appropriate format, such as JSON, and prepares it for analysis. For example, it extracts conditions included in the travel request, such as "hot spring trip," "budget under 50,000 yen," and "place where you can enjoy nature."

[0117] Based on the data received from the server, the generation AI analyzes the travel request. It searches past travel databases and information on the Internet to identify the travel destinations, activities, accommodations, and transportation methods that best fit the user's requirements. For example, the generation AI might suggest Hakone Hot Springs as a travel destination and create a specific travel plan that includes activities such as lakeside strolls and hot spring tours. Accommodation fees and transportation costs are also included in the calculations.

[0118] The travel plan created by the generation AI is returned to the server. The server converts it into a format for display to the user and sends the proposed data to the user's device. The user then checks the proposed travel plan on their device. If satisfied, the user clicks the "Approve" button and sends the approval data to the server.

[0119] The server receives an "approval" request from the user and makes online reservations for accommodation and transportation suggested by the generation AI. For example, the user accesses the accommodation reservation website and confirms the reservation. Similarly, the Shinkansen ticket reservation is completed. Once the reservation is confirmed, the server generates a final travel plan. This final plan includes a reservation confirmation number, accommodation details, transportation information, etc.

[0120] The final itinerary is sent from the server to the user's device, where the user can check the details, eliminating the need for complicated adjustments and enabling users to smoothly create their travel plans.

[0121] Specific examples

[0122] For example, suppose a user wants to enjoy a hot spring trip next weekend. In this case, the user would enter the following:

[0123] "I'd like to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature."

[0124] The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests suitable accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation options and notifies the user of the final travel plan, allowing the user to easily plan their trip.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] The user accesses the system using a terminal and inputs a travel request. The request includes desired conditions such as "I would like to take a trip to a hot spring next weekend. The budget should be under 50,000 yen, and I would like it to be somewhere where I can enjoy nature." After inputting the information, the user clicks the "Submit" button to send the request data to the server. The input of this step is the request data from the user, and the output is the sent request data.

[0128] Step 2:

[0129] The server receives the request data sent by the user. It converts the received data into JSON format and formats the data for analysis. During this process, the specific conditions of the request ("hot spring trip," "budget under 50,000 yen," "place where you can enjoy nature") are extracted. The input is the sent request data, and the output is the formatted request data.

[0130] Step 3:

[0131] The server sends the formatted request data to the generation AI. The generation AI receives the request data and begins analyzing it. Specifically, it searches past travel databases and information on the Internet to select the optimal travel destinations, activities, accommodations, and transportation methods based on the user's criteria. The input is the formatted request data, and the output is the generated travel plan.

[0132] Step 4:

[0133] The generation AI creates a travel plan based on the analysis results and sends it back to the server. The travel plan includes specific information such as Hakone hot springs, a stroll along the lake, accommodation for two nights, and Shinkansen transportation. The input is the analysis results by the generation AI, and the output is a specific travel plan.

[0134] Step 5:

[0135] The server converts the received itinerary into a format suitable for display to the user. The converted itinerary is sent to the user's device, allowing the user to view the proposals. The input is the generated itinerary, and the output is the itinerary converted for display.

[0136] Step 6:

[0137] The user checks the travel plan sent from the server on the terminal. If the proposed content is satisfactory, the user clicks the "Approve" button. The input is the displayed travel plan, and the output is the user's approval data.

[0138] Step 7:

[0139] The server receives the user's approval data and makes reservations for the proposed accommodation and transportation. It uses an online reservation system to obtain confirmed reservation information. For example, a user accesses an accommodation reservation site, confirms a hotel reservation, and also completes a Shinkansen ticket reservation. The input is the user's approval data, and the output is the confirmed reservation information.

[0140] Step 8:

[0141] The server generates a final itinerary based on the confirmed reservation information. This final itinerary includes the reservation confirmation number, accommodation details, transportation information, etc. The generated final itinerary is sent to the user's device to notify them of the trip details. The input is the confirmed reservation information, and the output is the final itinerary.

[0142] (Application example 1)

[0143] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0144] In conventional factories, work schedule adjustment and resource allocation are often performed manually by dedicated managers, resulting in issues of human error and reduced efficiency. Furthermore, inefficient resource allocation and scheduling can lead to reduced productivity and additional costs. The present invention aims to solve these issues and provide a system that optimizes factory work schedules and resource allocation.

[0145] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0146] In this invention, the server includes a means for receiving work requests input by a user, a means for using a generation AI to propose an appropriate work schedule and resource allocation based on the work requests, a means for making actual arrangements based on the proposed work schedule and resource allocation, and a means for transmitting the proposal and arrangement information to a user's terminal. This eliminates the traditional manual management method and enables efficient, error-free optimal work schedules and resource allocation.

[0147] "User" refers to the entity that uses this system to schedule work and allocate resources, such as factory or facility managers and operators.

[0148] The term "work request" refers to information including the work content and necessary resources that a user wants to perform at a specific date and time.

[0149] "Generative AI" refers to an artificial intelligence system that generates optimal schedules and resource allocations that meet the conditions based on requests entered by users.

[0150] A "work schedule" refers to a plan that determines the date, time, and order in which work should be performed based on input work requests.

[0151] "Resource allocation" refers to the appropriate allocation of resources such as labor, machinery, and materials required to carry out work.

[0152] "Arrangements" refers to the actual execution of necessary instructions and procurement based on the proposed work schedule and resource allocation.

[0153] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0154] As an embodiment of this invention, we will explain a specific implementation method of a factory work schedule optimization system that utilizes generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0155] System construction

[0156] 1. User submits work request

[0157] Users access the system using a device (e.g., a computer, tablet, or smartphone) through an app or web browser. They enter a work request in a form within the app or on a web page, such as "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." Once the input is complete, the user clicks the "Submit" button to send the request data to the server.

[0158] 2. Server request reception and analysis

[0159] The server receives work request data from the user. The server converts the received data into an appropriate format and prepares it for passing to the generation AI. Specifically, it generates a prompt statement based on the user's desired date and time, work content, number of personnel, and machine operating status.

[0160] Example prompt sentence:

[0161] Date: 2023-10-01

[0162] Personnel size: 3

[0163] Work: Maintenance of machine A, cleaning of line B

[0164] Machine availability: {'Machine A': 80, 'Machine B': 50}

[0165] 3. Proposal creation using generative AI

[0166] The generation AI analyzes the user's work request data passed from the server. Specifically, it extracts work content and resource information and generates an efficient schedule and resource allocation. Based on the past work database and information on the Internet, the AI ​​creates a work schedule like the one below:

[0167] Work date: 2023-10-01

[0168] Work: Maintenance of machine A (morning), cleaning of line B (afternoon)

[0169] Resource allocation: XX company's maintenance team (3 people)

[0170] 4. Server Proposal Submission

[0171] The server converts the proposed data received from the generation AI into a format that can be displayed to the user, and then sends the proposed data to the user's device so that the user can view it.

[0172] 5. User Verification and Authorization

[0173] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[0174] 6. Arrangements by the Server

[0175] The server then issues the necessary instructions based on the work schedule and resource allocation proposed by the generative AI, ensuring that the work is carried out efficiently and reliably.

[0176] Hardware and software used

[0177] Hardware

[0178] Server: Data center in the factory

[0179] Devices: Computers, tablets, and smartphones used by users

[0180] software

[0181] Python: used as a programming language

[0182] pandas: a library for data processing

[0183] scikit-learn: A library for machine learning models

[0184] transformers: A library for using HuggingFace's generative AI model

[0185] By combining these elements, the system can generate efficient work schedules and resource allocations based on input work requests, significantly reducing the burden on users.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] The user uses a terminal to access the system and input a work request. Specifically, the user inputs, "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." This input data includes the work date, work content, number of personnel, and machine operating status.

[0189] Step 2:

[0190] The device sends the user's input data to the server, which then analyzes it and converts it into a prompt format that the AI ​​uses.

[0191] Step 3:

[0192] The server generates a prompt sentence from the work request data to be passed to the generation AI. Specifically, it creates the following prompt sentence:

[0193] "Date and time: 2023-10-01 Staff size: 3 Work: Maintenance of machine A, cleaning of line B Machine operation status: {'machine A': 80, 'machine B': 50}"

[0194] Step 4:

[0195] The server sends a prompt to the generation AI, which analyzes the prompt and generates an efficient work schedule and resource allocation. The generation AI uses information from a database of past work and the internet to select the optimal schedule and resources.

[0196] Step 5:

[0197] The server receives the work schedule and resource allocation created by the generation AI. This information includes the work date and time, specific work content, and resource allocation. For example, "Work date and time: 2023-10-01 Work content: Maintenance of machine A (morning), cleaning of line B (afternoon) Resource allocation: Maintenance team (3 people)."

[0198] Step 6:

[0199] The server converts the proposed data received from the generation AI into a format that can be viewed by the user and sends it to the user's device.

[0200] Step 7:

[0201] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and send the request again.

[0202] Step 8:

[0203] Upon receiving the user's "approval" request, the server makes the actual arrangements based on the proposed work schedule and resource allocation. Specifically, it deploys the maintenance team at the specified date and time and sends the necessary instructions to each person in charge via the system.

[0204] 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.

[0205] As an embodiment of this invention, we will explain a specific implementation method of a system for planning holidays using generative AI and an emotion engine. This system consists of five main elements: a user, a terminal, a server, generative AI, and an emotion engine.

[0206] 1. User submits request

[0207] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0208] Users input their travel requests in a form within the app or on a web page, such as "I'd like to take a hot spring trip next weekend. The budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The app also simultaneously obtains real-time emotion data using the user's facial recognition camera.

[0209] Once the input is complete, the user clicks the "Send" button to send the travel request data and emotion data to the server.

[0210] 2. Server request reception and analysis

[0211] The server receives the travel request and emotion data sent by the user.

[0212] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[0213] 3. Proposal creation using generative AI and emotion engine

[0214] The generation AI analyzes the user's travel request data passed from the server and extracts the user's desired conditions.

[0215] The emotion engine analyzes the user's emotion data and evaluates the user's current mental state, for example, determining whether the user feels like relaxing or wants to be active.

[0216] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0217] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, highlighting hot springs for a user looking to relax, for example, and suggesting adventure activities for an active user.

[0218] The generated proposal is returned to the server.

[0219] 4. Server Proposal Submission

[0220] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[0221] The server transmits the proposal data to the user's terminal so that the user can view it.

[0222] 5. User Verification and Authorization

[0223] The user checks the proposal sent from the server on the terminal.

[0224] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0225] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0226] 6. Reservation arrangements by the server

[0227] The server receives an "approval" request from the user.

[0228] The server makes online reservations for accommodation and transportation suggested by the AI.

[0229] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0230] Execute payment processing to complete Shinkansen ticket reservations.

[0231] Make sure your booking is confirmed.

[0232] 7. Notification of Final Travel Plans

[0233] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0234] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0235] Specific examples

[0236] For example, a user might wish to "enjoy a hot spring trip next weekend," but is stressed and seeking relaxation. Using this system, the user accesses the system using a device and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine proposes a travel plan that emphasizes relaxation, and the generation AI selects Hakone Hot Springs as the destination and suggests appropriate accommodations and transportation options. The server returns this to the user, and if the user approves, the server makes the final reservations and notifies them of the confirmed travel plan. This allows users to smoothly plan a trip that suits their emotional state.

[0237] The processing flow will be explained below.

[0238] Step 1:

[0239] Users access the system using a terminal. The user inputs a travel request (e.g., "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and the device's camera captures the user's facial expressions in real time. The data (travel request and facial expression data) are sent to the server by clicking the "Send" button.

[0240] Step 2:

[0241] The server receives the travel request data and facial expression data sent by the user, analyzes the received data, converts it into an appropriate format, and prepares it for passing to the generative AI and emotion engine.

[0242] Step 3:

[0243] The server passes the user's travel request data and facial expression data to the generation AI and emotion engine. The generation AI analyzes the request data and extracts the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[0244] Step 4:

[0245] The emotion engine analyzes the facial expression data passed to it and assesses the user's emotional state, for example, the emotion engine may sense that the user is seeking relaxation.

[0246] Step 5:

[0247] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[0248] Step 6:

[0249] The emotion engine customizes the suggestions made by the generative AI based on the user's emotional state. For example, if the user is looking to relax, the generative AI will highlight hot springs and relaxation programs in its suggested plans.

[0250] Step 7:

[0251] The generative AI completes a customized itinerary and sends it back to the server, with suggestions including specific destinations, accommodations, activities, and transportation details.

[0252] Step 8:

[0253] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user, and then sends the proposed data to the user's device.

[0254] Step 9:

[0255] The user checks the proposal sent from the server on the device. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[0256] Step 10:

[0257] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the emotion engine and generative AI.

[0258] Step 11:

[0259] For example, the server accesses the reservation site of a certain hot spring inn and confirms the accommodation reservation.Similarly, the server executes a payment process to complete the Shinkansen ticket reservation.

[0260] Step 12:

[0261] The server confirms that the reservation is confirmed. The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device.

[0262] Step 13:

[0263] Users can check the final travel plan on their device and proceed with travel preparations, allowing them to smoothly plan a trip that suits their emotional state and desired conditions.

[0264] Example 2

[0265] 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."

[0266] Conventional travel planning systems often fail to meet user expectations because they propose travel plans without taking the user's emotional state into consideration. There is a need for a system that can efficiently book travel destinations and activities that suit individual user needs. Furthermore, there is a need for a system that can provide a more satisfying travel experience by acquiring users' emotional data in real time and reflecting it in the proposals.

[0267] 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.

[0268] In this invention, the server includes means for receiving a travel request input by a user, means for acquiring user emotion data, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation means based on the travel request, means for customizing the suggestions based on the emotion data using an emotion engine, means for booking the suggested travel destinations, activities, accommodations, and transportation means, and means for transmitting the suggestions and booking information to the user's terminal. This makes it possible to provide a personalized travel plan that takes the user's emotional state into consideration, thereby increasing user satisfaction.

[0269] 1. "User" refers to an individual who uses the System to enter a travel request and review and approve the proposed travel plan.

[0270] 2. "Device" refers to the electronic device (e.g., smartphone, tablet, PC) used by a User to enter and submit a Travel Request and receive suggested Travel Itineraries.

[0271] 3. "Server" refers to a computing device that receives requests from users, generates and provides travel plans in cooperation with the Generative AI and Emotion Engine, and processes reservations.

[0272] 4. “Generative AI” refers to artificial intelligence technology that suggests suitable travel destinations, activities, accommodations and transportation based on travel requests.

[0273] 5. "Emotion Engine" refers to a system that analyzes a user's emotional data and customizes suggestions based on the user's current emotional state.

[0274] 6. "Travel request" refers to the desired travel conditions (e.g., destination, budget, time, etc.) entered by a user into the system.

[0275] 7. “Emotional Data” means data that represents a user’s emotional state (e.g., facial expressions, voice tone, text analysis, etc.) and that is acquired by the system in real time.

[0276] 8. “Recommendations” refers to travel plans created by the Generative AI and Emotion Engine based on a user’s travel request and emotion data.

[0277] 9. "Booking" means the actual arrangement of the proposed travel destination, activity, accommodation, transportation, etc.

[0278] 10. "Travel Database" refers to a data storage facility that stores past travel information and travel-related information on the Internet.

[0279] As an embodiment of this invention, we will show a specific implementation method of a system that supports users' travel planning by utilizing generative AI and an emotion engine. This system is implemented using the following hardware and software.

[0280] Hardware used

[0281] Devices: smartphones, tablets, PCs, etc.

[0282] Server: a high-performance computing device

[0283] Software used

[0284] Generative AI models (e.g., GPT-3)

[0285] Emotion engine (e.g. Emotion API)

[0286] Frontend: App or web browser

[0287] Backend: A platform for receiving and analyzing user requests, creating and delivering proposals

[0288] Processing Description

[0289] 1. Fill out and submit your request

[0290] Users use their device to access the application or web browser and enter a travel request, such as "I'd like to take a hot spring trip next weekend, with a budget of less than 50,000 yen and somewhere where I can enjoy nature." Real-time emotional data is also provided using a facial recognition camera.

[0291] 2. Receiving and analyzing request and emotion data

[0292] The server receives the travel request and emotion data sent by the user, converts the data into an appropriate format, and passes it to the generative AI and emotion engine. For example, the server analyzes the travel request as text data and converts the emotion data into numerical data as a result of facial expression analysis.

[0293] 3. Proposal generation and customization

[0294] The generative AI analyzes the user's travel request data and extracts the user's desired conditions. The emotion engine analyzes the user's emotional data and evaluates their current mental state, for example, determining whether they want to relax or be active.

[0295] 4. Submitting and Viewing Proposals

[0296] The server converts the proposal data generated by the generative AI and emotion engine into a format that can be viewed by the user and sends it to the user's device, where the user can review the proposal and approve or change the conditions.

[0297] 5. Reservation Procedure

[0298] If the user approves the proposal, the server will make online reservations for accommodation and transportation, for example, accessing the website of a hot spring inn to confirm the reservation and reserving a Shinkansen ticket.

[0299] 6. Notification of Travel Plans

[0300] The server generates a final itinerary (including reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device, where the user can confirm the trip details.

[0301] Specific examples

[0302] For example, if a user wishes to "enjoy a hot spring trip next weekend" and is feeling stressed and seeking relaxation, they can use this system. The user accesses the system using a terminal and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, they transmit facial expression data via the camera indicating their stress level. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine creates a travel plan that emphasizes relaxation, and the generation AI suggests appropriate accommodations and transportation options with Hakone Hot Springs as the destination. The server returns this to the user, and if the user approves, it makes the final reservations and notifies them of the confirmed travel plan. An example of a prompt sentence might be, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature. I'm currently feeling stressed and want to relax."

[0303] In this way, the system allows users to smoothly plan their trip according to their emotional state.

[0304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0305] Step 1: User enters and submits request

[0306] Users use devices such as smartphones, tablets, or PCs to access an app or web browser and enter their travel request. For example, they might enter, "I'd like to take a trip to a hot spring next weekend. My budget is under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." At this time, real-time emotional data (facial expression data) is also acquired using the device's camera. Once the input data and emotional data are ready, the user clicks the "Send" button to send the data to the server.

[0307] Step 2: The server receives and formats the request

[0308] The server receives the travel request data and emotion data sent by the user. It analyzes the received data and converts it into an appropriate format to be passed to the generative AI and emotion engine. Specifically, it analyzes the travel request as text data and converts facial expression data into numerical data. The analyzed data is then passed to the next processing step.

[0309] Step 3: Generative AI analyzes the travel request

[0310] The generation AI extracts the user's specific desired conditions based on the travel request data passed from the server. For example, conditions such as destination, budget, and places to enjoy nature are extracted through text analysis. This provides the basic data for generating travel destination, activity, accommodation, and transportation options. The input is the travel request data, and the output is the extracted desired conditions.

[0311] Step 4: Analyze the emotional data with the emotion engine

[0312] The emotion engine analyzes the emotion data passed from the server and evaluates the user's current mental state. Specifically, it analyzes facial expression data to determine whether the user wants to relax or be active. The results of this analysis are used by the generative AI to customize suggestions. The input is emotion data, and the output is the analyzed emotional state.

[0313] Step 5: Generative AI creates proposals

[0314] The generation AI proposes specific travel destinations, activities, accommodations, and transportation methods based on the extracted user's desired conditions and the analysis results of the emotion engine. For example, it generates a plan that includes "Hakone Hot Springs as the destination, relaxing accommodation A, and scenic hiking course B." The input is the user's desired conditions and emotional state, and the output is a specific travel proposal.

[0315] Step 6: Server Sends Proposal

[0316] The server converts the generated travel proposals into a format that can be viewed by the user and sends them to the user's device. Specifically, it converts them into HTML or JSON format so that the proposals can be displayed on the device screen. The input is the travel proposal data, and the output is in a format that can be displayed on the user's screen.

[0317] Step 7: User Review and Authorization

[0318] The user reviews the submitted travel proposal on their device. If they are satisfied with the proposal, they click the "Accept" button. If they are not satisfied, they can change the conditions and submit the request again. The input is the travel proposal data, and the output is the user's approval or a new request.

[0319] Step 8: Reservations are made by the server

[0320] The server receives the user's approval request and makes online reservations for the accommodation and transportation suggested by the generation AI. For example, the user accesses a reservation website for a hot spring inn, confirms the reservation, and executes payment processing for the Shinkansen ticket. The input is the user's approval request, and the output is the reservation confirmation information.

[0321] Step 9: Finalize your travel plans

[0322] The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device. The user can then confirm the trip details. The input is the reservation confirmation information, and the output is the final itinerary.

[0323] These steps allow users to seamlessly create personalized travel plans based on their emotional state.

[0324] (Application example 2)

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

[0326] Conventional video streaming services suggest content based on a user's viewing history and general preferences, but they cannot suggest content that takes into account the user's real-time emotional state, resulting in low user satisfaction. In particular, there is a demand for services that can suggest relaxing content to users who are feeling stressed, and stimulating content to users who are full of energy. It is also important that the suggested content can be viewed immediately.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a viewing request input by a user, means for using a generation AI to suggest appropriate video content based on the viewing request, means for analyzing a facial image of the user and acquiring emotional data, means for customizing the suggested video content based on the emotional data, and means for transmitting information about the suggested video content to the user's terminal. This makes it possible to suggest video content that takes into account the user's real-time emotional state, thereby increasing user satisfaction.

[0328] "User" refers to a person who utilizes the system to enter a viewing request and view suggested video content.

[0329] A "viewing request" is data about a video viewing request that a user inputs into the system, and includes the type and theme of the content they wish to view.

[0330] "Generative AI" is an artificial intelligence that uses machine learning algorithms to suggest appropriate video content based on viewing requests.

[0331] "Video content" refers to videos that are proposed to and viewed by users, and there are a variety of types, including relaxing and stimulating videos.

[0332] "Facial image analysis" refers to the process of capturing emotional data using a user's facial image and using algorithms to determine emotions.

[0333] "Emotion data" is data that represents the user's real-time emotional state and is obtained through facial image analysis.

[0334] "Customization" refers to tailoring content suggestions to a user's specific preferences and state based on captured emotional data.

[0335] A "terminal" is a device used by a user to input a viewing request or view suggested video content, and includes a smartphone, tablet, personal computer, etc.

[0336] "Suggestion" refers to the act and content of the generative AI selecting the most suitable video content based on the user's viewing request and emotional data and showing it to the user.

[0337] As an embodiment of this invention, we will explain a specific implementation method of a system that combines emotion data and viewing requests to propose optimal video content. This system consists of five main elements: a user, a terminal, a server, a generation AI, and an emotion engine.

[0338] 1. User submits request

[0339] Users use a device (e.g., a smartphone, tablet, or personal computer) to access the system through an app or web browser.

[0340] Users enter viewing requests such as "I want to watch a relaxing video" into an in-app form or on a web page.

[0341] In addition, facial images are captured in real time using the device's camera, and emotional data is simultaneously obtained.

[0342] Once you have completed the input, click the "Send" button to send the viewing request data and emotion data to the server.

[0343] 2. Server request reception and analysis

[0344] The server receives the viewing request and emotion data sent from the user.

[0345] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[0346] 3. Proposal creation using generative AI and emotion engine

[0347] The generation AI analyzes the user's viewing request data passed from the server and extracts the user's desired conditions.

[0348] The emotion engine analyzes the user's facial images to obtain emotional data and evaluate their real-time emotional state, for example, determining whether they are in a relaxed mood or wanting to engage in energetic activities.

[0349] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[0350] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, for example highlighting calming content for a user looking to relax, or suggesting active content for an energetic user.

[0351] The generated proposal is returned to the server.

[0352] 4. Server Proposal Submission

[0353] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[0354] The server transmits the proposal data to the user's terminal so that the user can view it.

[0355] 5. User Verification and Viewing

[0356] The user checks the proposal sent from the server on the terminal.

[0357] If the user is satisfied with the suggestions, they can click the "Watch" button to immediately begin watching the suggested video content.

[0358] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0359] Examples:

[0360] For example, if a user wishes to "watch a relaxing video" but is stressed and seeking relaxation, they can use this system. The user uses their device to access the system and input a viewing request such as "I want to watch a relaxing video," and at the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine suggests video content that emphasizes relaxation, and the generation AI presents an appropriate video. The server returns this to the user, who then begins watching.

[0361] Example prompt sentence:

[0362] "Please suggest video content that will help users relax."

[0363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0364] Step 1:

[0365] A user accesses the system through a device, an app, or a web browser. The user inputs a viewing request and simultaneously captures a facial image with the device's camera. This viewing request data and facial image data are then sent to the server.

[0366] Input: Viewing request data, facial image data

[0367] Output: Viewing request data and facial image data sent to the server

[0368] Specific operation: The user types "I want to watch a relaxing video" in the app, and the camera simultaneously captures an image of the user's face.

[0369] Step 2:

[0370] The server receives the viewing request data and facial image data sent by the user and converts the data into an appropriate format. The viewing request data is passed to the generation AI, and the facial image data is passed to the emotion engine.

[0371] Input: Viewing request data, facial image data

[0372] Output: Data formatted to feed generative AI and emotion engines

[0373] Specific operation: The server prepares to pass the viewing request data to the generation AI and the facial image data to the emotion engine.

[0374] Step 3:

[0375] The emotion engine analyzes facial image data to assess the user's real-time emotional state, for example, whether the user is stressed or wants to relax.

[0376] Input: Facial image data

[0377] Output: User's emotional state data

[0378] How it works: The emotion engine uses facial image analysis algorithms to infer emotional states (e.g., stress) from captured facial images.

[0379] Step 4:

[0380] The generation AI analyzes the viewing request data and extracts the user's desired conditions, which clarifies the content of the viewing request.

[0381] Input: Viewing request data

[0382] Output: Extracted user preference data

[0383] Specific operation: The generation AI extracts the desired conditions of the user who is looking for a "relaxing video."

[0384] Step 5:

[0385] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[0386] Input: User's desired conditions data

[0387] Output: Optimal video content data

[0388] How it works: The generative AI searches a database for relaxing videos and identifies several candidates.

[0389] Step 6:

[0390] The emotion engine customizes suggestions based on the user's emotional state data and the video content identified by the generative AI. For example, if the user prioritizes relaxation, the generative AI will select particularly calming videos from the suggested content.

[0391] Input: User emotional state data, video content data generated by generative AI

[0392] Output: Customized video suggestion data

[0393] Specific operation: The emotion engine prioritizes video content suitable for relaxation.

[0394] Step 7:

[0395] The server sends the customized video suggestion data received from the generative AI and emotion engine to the user's device so that the user can view it.

[0396] Input: Customized video suggestion data

[0397] Output: Video suggestion data sent to the user's device

[0398] Specific operation: The server sends video suggestion data to the user's device and displays it on the app or browser.

[0399] Step 8:

[0400] The user can review the suggested video content on their device and click the "Watch" button to begin watching. If they are not satisfied with the suggested content, they can change the conditions and submit the request again.

[0401] Input: User confirmation action

[0402] Output: Start watching or re-request

[0403] Specific Action: User clicks the "Watch" button to play the video or resubmits the request.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Second embodiment]

[0408] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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."

[0420] As an embodiment of this invention, we will explain a specific implementation method of a system for making holiday plans using generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0421] 1. User submits request

[0422] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0423] Users enter travel requests such as, "I'd like to go on a hot spring trip next weekend. My budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." in a form within the app or on a web page.

[0424] Once you have completed entering the information, click the "Submit" button to send the request data to the server.

[0425] 2. Server request reception and analysis

[0426] The server receives the request data from the user.

[0427] The server converts the received data into an appropriate format and prepares it for passing to the generation AI.

[0428] 3. Proposal creation using generative AI

[0429] The generation AI analyzes the user request data passed from the server, specifically extracting the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[0430] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0431] The generative AI creates a travel plan like this:

[0432] Travel destination: Hakone Onsen

[0433] Activities: Walking around the lake, visiting hot springs

[0434] Accommodation: XX Hot Springs Inn (2 nights, 15,000 yen per person per night)

[0435] Transportation: Round-trip Shinkansen ticket (total 15,000 yen)

[0436] The created proposal is returned to the server.

[0437] 4. Server Proposal Submission

[0438] The server converts the proposed data received from the generation AI into a format that can be shown to the user.

[0439] The server transmits the proposal data to the user's terminal so that the user can view it.

[0440] 5. User Verification and Authorization

[0441] The user checks the proposal sent from the server on the terminal.

[0442] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0443] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0444] 6. Reservation arrangements by the server

[0445] The server receives an "approval" request from the user.

[0446] The server makes online reservations for accommodation and transportation suggested by the AI.

[0447] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0448] Execute payment processing to complete Shinkansen ticket reservations.

[0449] Make sure your booking is confirmed.

[0450] 7. Notification of Final Travel Plans

[0451] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0452] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0453] Specific examples

[0454] For example, suppose a user wishes to "enjoy a hot spring trip next weekend." In this case, the user accesses the system using a terminal and enters a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests appropriate accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation and notifies the user of the final travel plan. This allows users to plan their trip with peace of mind without any complicated work.

[0455] The processing flow will be explained below.

[0456] Step 1:

[0457] The user accesses the system using a terminal, inputs a travel request (e.g., "I want to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and clicks the "Submit" button.

[0458] Step 2:

[0459] The server receives the travel request sent by the user, converts the request data into an appropriate format, and prepares it for passing to the generation AI.

[0460] Step 3:

[0461] The server passes the user's travel request data to the generation AI, which then analyzes the request data and extracts the user's desired conditions.

[0462] Step 4:

[0463] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[0464] Step 5:

[0465] The generative AI creates a travel plan, providing specific suggestions such as: destination: Hakone Hot Springs, activities: lakeside walks and hot spring tours, accommodation: XX hot spring inn (two nights, 15,000 yen per person per night), transportation: round-trip Shinkansen ticket (total 15,000 yen).

[0466] Step 6:

[0467] The generation AI returns the created proposal to the server, which then converts the proposal data received from the generation AI into a format that can be shown to the user.

[0468] Step 7:

[0469] The server sends the proposal data to the user's device so that the user can view it. The user then checks the proposal on the device.

[0470] Step 8:

[0471] If the user is satisfied with the proposal, they click the "Approve" button. If they are not satisfied with the proposal, they can change the conditions and submit the request again.

[0472] Step 9:

[0473] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the AI.

[0474] Step 10:

[0475] The server accesses the reservation website of the hot spring inn and confirms the accommodation reservation. It also processes the payment to complete the Shinkansen ticket reservation.

[0476] Step 11:

[0477] The server confirms the reservation and generates a final itinerary including the reservation confirmation information (confirmation number, accommodation details, and transportation information).

[0478] Step 12:

[0479] The server transmits the final itinerary to the user's terminal, allowing the user to confirm the details of the trip. The user confirms the final itinerary on the terminal and proceeds with preparations for the trip.

[0480] This series of steps allows users to eliminate cumbersome tasks and smoothly plan their trip.

[0481] Example 1

[0482] 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."

[0483] In today's modern lifestyle, planning a trip in a busy daily life is cumbersome and time-consuming, and many users find it stressful to coordinate the details of their trip. An effective system to solve this problem is needed.

[0484] 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.

[0485] In this invention, the server includes means for receiving a travel request input from a user, means for converting the travel request data into JSON format and preparing for analysis, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation based on the travel request, means for booking the suggested travel destinations, activities, accommodations, and transportation, means for sending the suggestion and booking information to the user's device, means for confirming the booking with the user's approval and generating a final travel plan, and means for sending the final travel plan to the user's device and notifying the user of the trip details. This allows the user to smoothly plan their trip without having to make complicated adjustments.

[0486] "User" means any person or entity that accesses the System and enters and submits a Travel Request.

[0487] "Terminal" refers to an electronic device, such as a smartphone, tablet, or personal computer, that a user uses to access the system.

[0488] "Travel request" refers to data including travel wishes and conditions that a user inputs and transmits to the system.

[0489] "Generative AI" refers to an artificial intelligence model that analyzes incoming travel requests and suggests appropriate destinations, activities, accommodations and transportation options.

[0490] "Server" refers to an electronic computer that receives a travel request from a user, passes it to the generating AI, and transmits the proposed data to the user's terminal.

[0491] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for storing and exchanging structured data.

[0492] "Proposal" refers to a plan that compiles information on travel destinations, activities, accommodations, and transportation generated by the Generating AI based on a travel request.

[0493] "Confirming reservation" refers to the action of the user approving the proposal and then the server officially reserving accommodation and transportation.

[0494] The "final travel plan" refers to a plan including detailed travel information that is generated by the server after the reservation is confirmed and sent to the user.

[0495] "Internet information" refers to information about travel destinations and activities obtained from websites and online databases.

[0496] This invention relates to a travel planning support system that utilizes generative AI, and has the function of proposing optimal travel plans based on the user's travel requests, confirming the reservations, and notifying the user. This system is mainly composed of four main elements: the user, the terminal, the server, and generative AI.

[0497] Users access the system using devices such as smartphones, tablets, and personal computers. The access method is a dedicated app or a web browser. Users input travel requests in a form within the app or on a web page. For example, a user might input a request such as, "I'd like to take a trip to a hot spring next weekend. My budget should be under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." By sending this request, data is sent to the server.

[0498] The server receives the request data sent by the user. It converts the received data into an appropriate format, such as JSON, and prepares it for analysis. For example, it extracts conditions included in the travel request, such as "hot spring trip," "budget under 50,000 yen," and "place where you can enjoy nature."

[0499] Based on the data received from the server, the generation AI analyzes the travel request. It searches past travel databases and information on the Internet to identify the travel destinations, activities, accommodations, and transportation methods that best fit the user's requirements. For example, the generation AI might suggest Hakone Hot Springs as a travel destination and create a specific travel plan that includes activities such as lakeside strolls and hot spring tours. Accommodation fees and transportation costs are also included in the calculations.

[0500] The travel plan created by the generation AI is returned to the server. The server converts it into a format for display to the user and sends the proposed data to the user's device. The user then checks the proposed travel plan on their device. If satisfied, the user clicks the "Approve" button and sends the approval data to the server.

[0501] The server receives an "approval" request from the user and makes online reservations for accommodation and transportation suggested by the generation AI. For example, the user accesses the accommodation reservation website and confirms the reservation. Similarly, the Shinkansen ticket reservation is completed. Once the reservation is confirmed, the server generates a final travel plan. This final plan includes a reservation confirmation number, accommodation details, transportation information, etc.

[0502] The final itinerary is sent from the server to the user's device, where the user can check the details, eliminating the need for complicated adjustments and enabling users to smoothly create their travel plans.

[0503] Specific examples

[0504] For example, suppose a user wants to enjoy a hot spring trip next weekend. In this case, the user would enter the following:

[0505] "I'd like to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature."

[0506] The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests suitable accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation options and notifies the user of the final travel plan, allowing the user to easily plan their trip.

[0507] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0508] Step 1:

[0509] The user accesses the system using a terminal and inputs a travel request. The request includes desired conditions such as "I would like to take a trip to a hot spring next weekend. The budget should be under 50,000 yen, and I would like it to be somewhere where I can enjoy nature." After inputting the information, the user clicks the "Submit" button to send the request data to the server. The input of this step is the request data from the user, and the output is the sent request data.

[0510] Step 2:

[0511] The server receives the request data sent by the user. It converts the received data into JSON format and formats the data for analysis. During this process, the specific conditions of the request ("hot spring trip," "budget under 50,000 yen," "place where you can enjoy nature") are extracted. The input is the sent request data, and the output is the formatted request data.

[0512] Step 3:

[0513] The server sends the formatted request data to the generation AI. The generation AI receives the request data and begins analyzing it. Specifically, it searches past travel databases and information on the Internet to select the optimal travel destinations, activities, accommodations, and transportation methods based on the user's criteria. The input is the formatted request data, and the output is the generated travel plan.

[0514] Step 4:

[0515] The generation AI creates a travel plan based on the analysis results and sends it back to the server. The travel plan includes specific information such as Hakone hot springs, a stroll along the lake, accommodation for two nights, and Shinkansen transportation. The input is the analysis results by the generation AI, and the output is a specific travel plan.

[0516] Step 5:

[0517] The server converts the received itinerary into a format suitable for display to the user. The converted itinerary is sent to the user's device, allowing the user to view the proposals. The input is the generated itinerary, and the output is the itinerary converted for display.

[0518] Step 6:

[0519] The user checks the travel plan sent from the server on the terminal. If the proposed content is satisfactory, the user clicks the "Approve" button. The input is the displayed travel plan, and the output is the user's approval data.

[0520] Step 7:

[0521] The server receives the user's approval data and makes reservations for the proposed accommodation and transportation. It uses an online reservation system to obtain confirmed reservation information. For example, a user accesses an accommodation reservation site, confirms a hotel reservation, and also completes a Shinkansen ticket reservation. The input is the user's approval data, and the output is the confirmed reservation information.

[0522] Step 8:

[0523] The server generates a final itinerary based on the confirmed reservation information. This final itinerary includes the reservation confirmation number, accommodation details, transportation information, etc. The generated final itinerary is sent to the user's device to notify them of the trip details. The input is the confirmed reservation information, and the output is the final itinerary.

[0524] (Application example 1)

[0525] 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."

[0526] In conventional factories, work schedule adjustment and resource allocation are often performed manually by dedicated managers, resulting in issues of human error and reduced efficiency. Furthermore, inefficient resource allocation and scheduling can lead to reduced productivity and additional costs. The present invention aims to solve these issues and provide a system that optimizes factory work schedules and resource allocation.

[0527] 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.

[0528] In this invention, the server includes a means for receiving work requests input by a user, a means for using a generation AI to propose an appropriate work schedule and resource allocation based on the work requests, a means for making actual arrangements based on the proposed work schedule and resource allocation, and a means for transmitting the proposal and arrangement information to a user's terminal. This eliminates the traditional manual management method and enables efficient, error-free optimal work schedules and resource allocation.

[0529] "User" refers to the entity that uses this system to schedule work and allocate resources, such as factory or facility managers and operators.

[0530] The term "work request" refers to information including the work content and necessary resources that a user wants to perform at a specific date and time.

[0531] "Generative AI" refers to an artificial intelligence system that generates optimal schedules and resource allocations that meet the conditions based on requests entered by users.

[0532] A "work schedule" refers to a plan that determines the date, time, and order in which work should be performed based on input work requests.

[0533] "Resource allocation" refers to the appropriate allocation of resources such as labor, machinery, and materials required to carry out work.

[0534] "Arrangements" refers to the actual execution of necessary instructions and procurement based on the proposed work schedule and resource allocation.

[0535] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0536] As an embodiment of this invention, we will explain a specific implementation method of a factory work schedule optimization system that utilizes generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0537] System construction

[0538] 1. User submits work request

[0539] Users access the system using a device (e.g., a computer, tablet, or smartphone) through an app or web browser. They enter a work request in a form within the app or on a web page, such as "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." Once the input is complete, the user clicks the "Submit" button to send the request data to the server.

[0540] 2. Server request reception and analysis

[0541] The server receives work request data from the user. The server converts the received data into an appropriate format and prepares it for passing to the generation AI. Specifically, it generates a prompt statement based on the user's desired date and time, work content, number of personnel, and machine operating status.

[0542] Example prompt sentence:

[0543] Date: 2023-10-01

[0544] Personnel size: 3

[0545] Work: Maintenance of machine A, cleaning of line B

[0546] Machine availability: {'Machine A': 80, 'Machine B': 50}

[0547] 3. Proposal creation using generative AI

[0548] The generation AI analyzes the user's work request data passed from the server. Specifically, it extracts work content and resource information and generates an efficient schedule and resource allocation. Based on the past work database and information on the Internet, the AI ​​creates a work schedule like the one below:

[0549] Work date: 2023-10-01

[0550] Work: Maintenance of machine A (morning), cleaning of line B (afternoon)

[0551] Resource allocation: XX company's maintenance team (3 people)

[0552] 4. Server Proposal Submission

[0553] The server converts the proposed data received from the generation AI into a format that can be displayed to the user, and then sends the proposed data to the user's device so that the user can view it.

[0554] 5. User Verification and Authorization

[0555] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[0556] 6. Arrangements by the Server

[0557] The server then issues the necessary instructions based on the work schedule and resource allocation proposed by the generative AI, ensuring that the work is carried out efficiently and reliably.

[0558] Hardware and software used

[0559] Hardware

[0560] Server: Data center in the factory

[0561] Devices: Computers, tablets, and smartphones used by users

[0562] software

[0563] Python: used as a programming language

[0564] pandas: a library for data processing

[0565] scikit-learn: A library for machine learning models

[0566] transformers: A library for using HuggingFace's generative AI model

[0567] By combining these elements, the system can generate efficient work schedules and resource allocations based on input work requests, significantly reducing the burden on users.

[0568] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0569] Step 1:

[0570] The user uses a terminal to access the system and input a work request. Specifically, the user inputs, "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." This input data includes the work date, work content, number of personnel, and machine operating status.

[0571] Step 2:

[0572] The device sends the user's input data to the server, which then analyzes it and converts it into a prompt format that the AI ​​uses.

[0573] Step 3:

[0574] The server generates a prompt sentence from the work request data to be passed to the generation AI. Specifically, it creates the following prompt sentence:

[0575] "Date and time: 2023-10-01 Staff size: 3 Work: Maintenance of machine A, cleaning of line B Machine operation status: {'machine A': 80, 'machine B': 50}"

[0576] Step 4:

[0577] The server sends a prompt to the generation AI, which analyzes the prompt and generates an efficient work schedule and resource allocation. The generation AI uses information from a database of past work and the internet to select the optimal schedule and resources.

[0578] Step 5:

[0579] The server receives the work schedule and resource allocation created by the generation AI. This information includes the work date and time, specific work content, and resource allocation. For example, "Work date and time: 2023-10-01 Work content: Maintenance of machine A (morning), cleaning of line B (afternoon) Resource allocation: Maintenance team (3 people)."

[0580] Step 6:

[0581] The server converts the proposed data received from the generation AI into a format that can be viewed by the user and sends it to the user's device.

[0582] Step 7:

[0583] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and send the request again.

[0584] Step 8:

[0585] Upon receiving the user's "approval" request, the server makes the actual arrangements based on the proposed work schedule and resource allocation. Specifically, it deploys the maintenance team at the specified date and time and sends the necessary instructions to each person in charge via the system.

[0586] 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.

[0587] As an embodiment of this invention, we will explain a specific implementation method of a system for planning holidays using generative AI and an emotion engine. This system consists of five main elements: a user, a terminal, a server, generative AI, and an emotion engine.

[0588] 1. User submits request

[0589] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0590] Users input their travel requests in a form within the app or on a web page, such as "I'd like to take a hot spring trip next weekend. The budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The app also simultaneously obtains real-time emotion data using the user's facial recognition camera.

[0591] Once the input is complete, the user clicks the "Send" button to send the travel request data and emotion data to the server.

[0592] 2. Server request reception and analysis

[0593] The server receives the travel request and emotion data sent by the user.

[0594] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[0595] 3. Proposal creation using generative AI and emotion engine

[0596] The generation AI analyzes the user's travel request data passed from the server and extracts the user's desired conditions.

[0597] The emotion engine analyzes the user's emotion data and evaluates the user's current mental state, for example, determining whether the user feels like relaxing or wants to be active.

[0598] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0599] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, highlighting hot springs for a user looking to relax, for example, and suggesting adventure activities for an active user.

[0600] The generated proposal is returned to the server.

[0601] 4. Server Proposal Submission

[0602] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[0603] The server transmits the proposal data to the user's terminal so that the user can view it.

[0604] 5. User Verification and Authorization

[0605] The user checks the proposal sent from the server on the terminal.

[0606] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0607] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0608] 6. Reservation arrangements by the server

[0609] The server receives an "approval" request from the user.

[0610] The server makes online reservations for accommodation and transportation suggested by the AI.

[0611] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0612] Execute payment processing to complete Shinkansen ticket reservations.

[0613] Make sure your booking is confirmed.

[0614] 7. Notification of Final Travel Plans

[0615] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0616] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0617] Specific examples

[0618] For example, a user might wish to "enjoy a hot spring trip next weekend," but is stressed and seeking relaxation. Using this system, the user accesses the system using a device and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine proposes a travel plan that emphasizes relaxation, and the generation AI selects Hakone Hot Springs as the destination and suggests appropriate accommodations and transportation options. The server returns this to the user, and if the user approves, the server makes the final reservations and notifies them of the confirmed travel plan. This allows users to smoothly plan a trip that suits their emotional state.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] Users access the system using a terminal. The user inputs a travel request (e.g., "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and the device's camera captures the user's facial expressions in real time. The data (travel request and facial expression data) are sent to the server by clicking the "Send" button.

[0622] Step 2:

[0623] The server receives the travel request data and facial expression data sent by the user, analyzes the received data, converts it into an appropriate format, and prepares it for passing to the generative AI and emotion engine.

[0624] Step 3:

[0625] The server passes the user's travel request data and facial expression data to the generation AI and emotion engine. The generation AI analyzes the request data and extracts the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[0626] Step 4:

[0627] The emotion engine analyzes the facial expression data passed to it and assesses the user's emotional state, for example, the emotion engine may sense that the user is seeking relaxation.

[0628] Step 5:

[0629] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[0630] Step 6:

[0631] The emotion engine customizes the suggestions made by the generative AI based on the user's emotional state. For example, if the user is looking to relax, the generative AI will highlight hot springs and relaxation programs in its suggested plans.

[0632] Step 7:

[0633] The generative AI completes a customized itinerary and sends it back to the server, with suggestions including specific destinations, accommodations, activities, and transportation details.

[0634] Step 8:

[0635] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user, and then sends the proposed data to the user's device.

[0636] Step 9:

[0637] The user checks the proposal sent from the server on the device. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[0638] Step 10:

[0639] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the emotion engine and generative AI.

[0640] Step 11:

[0641] For example, the server accesses the reservation site of a certain hot spring inn and confirms the accommodation reservation.Similarly, the server executes a payment process to complete the Shinkansen ticket reservation.

[0642] Step 12:

[0643] The server confirms that the reservation is confirmed. The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device.

[0644] Step 13:

[0645] Users can check the final travel plan on their device and proceed with travel preparations, allowing them to smoothly plan a trip that suits their emotional state and desired conditions.

[0646] Example 2

[0647] 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."

[0648] Conventional travel planning systems often fail to meet user expectations because they propose travel plans without taking the user's emotional state into consideration. There is a need for a system that can efficiently book travel destinations and activities that suit individual user needs. Furthermore, there is a need for a system that can provide a more satisfying travel experience by acquiring users' emotional data in real time and reflecting it in the proposals.

[0649] 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.

[0650] In this invention, the server includes means for receiving a travel request input by a user, means for acquiring user emotion data, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation means based on the travel request, means for customizing the suggestions based on the emotion data using an emotion engine, means for booking the suggested travel destinations, activities, accommodations, and transportation means, and means for transmitting the suggestions and booking information to the user's terminal. This makes it possible to provide a personalized travel plan that takes the user's emotional state into consideration, thereby increasing user satisfaction.

[0651] 1. "User" refers to an individual who uses the System to enter a travel request and review and approve the proposed travel plan.

[0652] 2. "Device" refers to the electronic device (e.g., smartphone, tablet, PC) used by a User to enter and submit a Travel Request and receive suggested Travel Itineraries.

[0653] 3. "Server" refers to a computing device that receives requests from users, generates and provides travel plans in cooperation with the Generative AI and Emotion Engine, and processes reservations.

[0654] 4. “Generative AI” refers to artificial intelligence technology that suggests suitable travel destinations, activities, accommodations and transportation based on travel requests.

[0655] 5. "Emotion Engine" refers to a system that analyzes a user's emotional data and customizes suggestions based on the user's current emotional state.

[0656] 6. "Travel request" refers to the desired travel conditions (e.g., destination, budget, time, etc.) entered by a user into the system.

[0657] 7. “Emotional Data” means data that represents a user’s emotional state (e.g., facial expressions, voice tone, text analysis, etc.) and that is acquired by the system in real time.

[0658] 8. “Recommendations” refers to travel plans created by the Generative AI and Emotion Engine based on a user’s travel request and emotion data.

[0659] 9. "Booking" means the actual arrangement of the proposed travel destination, activity, accommodation, transportation, etc.

[0660] 10. "Travel Database" refers to a data storage facility that stores past travel information and travel-related information on the Internet.

[0661] As an embodiment of this invention, we will show a specific implementation method of a system that supports users' travel planning by utilizing generative AI and an emotion engine. This system is implemented using the following hardware and software.

[0662] Hardware used

[0663] Devices: smartphones, tablets, PCs, etc.

[0664] Server: a high-performance computing device

[0665] Software used

[0666] Generative AI models (e.g., GPT-3)

[0667] Emotion engine (e.g. Emotion API)

[0668] Frontend: App or web browser

[0669] Backend: A platform for receiving and analyzing user requests, creating and delivering proposals

[0670] Processing Description

[0671] 1. Fill out and submit your request

[0672] Users use their device to access the application or web browser and enter a travel request, such as "I'd like to take a hot spring trip next weekend, with a budget of less than 50,000 yen and somewhere where I can enjoy nature." Real-time emotional data is also provided using a facial recognition camera.

[0673] 2. Receiving and analyzing request and emotion data

[0674] The server receives the travel request and emotion data sent by the user, converts the data into an appropriate format, and passes it to the generative AI and emotion engine. For example, the server analyzes the travel request as text data and converts the emotion data into numerical data as a result of facial expression analysis.

[0675] 3. Proposal generation and customization

[0676] The generative AI analyzes the user's travel request data and extracts the user's desired conditions. The emotion engine analyzes the user's emotional data and evaluates their current mental state, for example, determining whether they want to relax or be active.

[0677] 4. Submitting and Viewing Proposals

[0678] The server converts the proposal data generated by the generative AI and emotion engine into a format that can be viewed by the user and sends it to the user's device, where the user can review the proposal and approve or change the conditions.

[0679] 5. Reservation Procedure

[0680] If the user approves the proposal, the server will make online reservations for accommodation and transportation, for example, accessing the website of a hot spring inn to confirm the reservation and reserving a Shinkansen ticket.

[0681] 6. Notification of Travel Plans

[0682] The server generates a final itinerary (including reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device, where the user can confirm the trip details.

[0683] Specific examples

[0684] For example, if a user wishes to "enjoy a hot spring trip next weekend" and is feeling stressed and seeking relaxation, they can use this system. The user accesses the system using a terminal and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, they transmit facial expression data via the camera indicating their stress level. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine creates a travel plan that emphasizes relaxation, and the generation AI suggests appropriate accommodations and transportation options with Hakone Hot Springs as the destination. The server returns this to the user, and if the user approves, it makes the final reservations and notifies them of the confirmed travel plan. An example of a prompt sentence might be, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature. I'm currently feeling stressed and want to relax."

[0685] In this way, the system allows users to smoothly plan their trip according to their emotional state.

[0686] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0687] Step 1: User enters and submits request

[0688] Users use devices such as smartphones, tablets, or PCs to access an app or web browser and enter their travel request. For example, they might enter, "I'd like to take a trip to a hot spring next weekend. My budget is under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." At this time, real-time emotional data (facial expression data) is also acquired using the device's camera. Once the input data and emotional data are ready, the user clicks the "Send" button to send the data to the server.

[0689] Step 2: The server receives and formats the request

[0690] The server receives the travel request data and emotion data sent by the user. It analyzes the received data and converts it into an appropriate format to be passed to the generative AI and emotion engine. Specifically, it analyzes the travel request as text data and converts facial expression data into numerical data. The analyzed data is then passed to the next processing step.

[0691] Step 3: Generative AI analyzes the travel request

[0692] The generation AI extracts the user's specific desired conditions based on the travel request data passed from the server. For example, conditions such as destination, budget, and places to enjoy nature are extracted through text analysis. This provides the basic data for generating travel destination, activity, accommodation, and transportation options. The input is the travel request data, and the output is the extracted desired conditions.

[0693] Step 4: Analyze the emotional data with the emotion engine

[0694] The emotion engine analyzes the emotion data passed from the server and evaluates the user's current mental state. Specifically, it analyzes facial expression data to determine whether the user wants to relax or be active. The results of this analysis are used by the generative AI to customize suggestions. The input is emotion data, and the output is the analyzed emotional state.

[0695] Step 5: Generative AI creates proposals

[0696] The generation AI proposes specific travel destinations, activities, accommodations, and transportation methods based on the extracted user's desired conditions and the analysis results of the emotion engine. For example, it generates a plan that includes "Hakone Hot Springs as the destination, relaxing accommodation A, and scenic hiking course B." The input is the user's desired conditions and emotional state, and the output is a specific travel proposal.

[0697] Step 6: Server Sends Proposal

[0698] The server converts the generated travel proposals into a format that can be viewed by the user and sends them to the user's device. Specifically, it converts them into HTML or JSON format so that the proposals can be displayed on the device screen. The input is the travel proposal data, and the output is in a format that can be displayed on the user's screen.

[0699] Step 7: User Review and Authorization

[0700] The user reviews the submitted travel proposal on their device. If they are satisfied with the proposal, they click the "Accept" button. If they are not satisfied, they can change the conditions and submit the request again. The input is the travel proposal data, and the output is the user's approval or a new request.

[0701] Step 8: Reservations are made by the server

[0702] The server receives the user's approval request and makes online reservations for the accommodation and transportation suggested by the generation AI. For example, the user accesses a reservation website for a hot spring inn, confirms the reservation, and executes payment processing for the Shinkansen ticket. The input is the user's approval request, and the output is the reservation confirmation information.

[0703] Step 9: Finalize your travel plans

[0704] The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device. The user can then confirm the trip details. The input is the reservation confirmation information, and the output is the final itinerary.

[0705] These steps allow users to seamlessly create personalized travel plans based on their emotional state.

[0706] (Application example 2)

[0707] 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."

[0708] Conventional video streaming services suggest content based on a user's viewing history and general preferences, but they cannot suggest content that takes into account the user's real-time emotional state, resulting in low user satisfaction. In particular, there is a demand for services that can suggest relaxing content to users who are feeling stressed, and stimulating content to users who are full of energy. It is also important that the suggested content can be viewed immediately.

[0709] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a viewing request input by a user, means for using a generation AI to suggest appropriate video content based on the viewing request, means for analyzing a facial image of the user and acquiring emotional data, means for customizing the suggested video content based on the emotional data, and means for transmitting information about the suggested video content to the user's terminal. This makes it possible to suggest video content that takes into account the user's real-time emotional state, thereby increasing user satisfaction.

[0710] "User" refers to a person who utilizes the system to enter a viewing request and view suggested video content.

[0711] A "viewing request" is data about a video viewing request that a user inputs into the system, and includes the type and theme of the content they wish to view.

[0712] "Generative AI" is an artificial intelligence that uses machine learning algorithms to suggest appropriate video content based on viewing requests.

[0713] "Video content" refers to videos that are proposed to and viewed by users, and there are a variety of types, including relaxing and stimulating videos.

[0714] "Facial image analysis" refers to the process of capturing emotional data using a user's facial image and using algorithms to determine emotions.

[0715] "Emotion data" is data that represents the user's real-time emotional state and is obtained through facial image analysis.

[0716] "Customization" refers to tailoring content suggestions to a user's specific preferences and state based on captured emotional data.

[0717] A "terminal" is a device used by a user to input a viewing request or view suggested video content, and includes a smartphone, tablet, personal computer, etc.

[0718] "Suggestion" refers to the act and content of the generative AI selecting the most suitable video content based on the user's viewing request and emotional data and showing it to the user.

[0719] As an embodiment of this invention, we will explain a specific implementation method of a system that combines emotion data and viewing requests to propose optimal video content. This system consists of five main elements: a user, a terminal, a server, a generation AI, and an emotion engine.

[0720] 1. User submits request

[0721] Users use a device (e.g., a smartphone, tablet, or personal computer) to access the system through an app or web browser.

[0722] Users enter viewing requests such as "I want to watch a relaxing video" into an in-app form or on a web page.

[0723] In addition, facial images are captured in real time using the device's camera, and emotional data is simultaneously obtained.

[0724] Once you have completed the input, click the "Send" button to send the viewing request data and emotion data to the server.

[0725] 2. Server request reception and analysis

[0726] The server receives the viewing request and emotion data sent from the user.

[0727] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[0728] 3. Proposal creation using generative AI and emotion engine

[0729] The generation AI analyzes the user's viewing request data passed from the server and extracts the user's desired conditions.

[0730] The emotion engine analyzes the user's facial images to obtain emotional data and evaluate their real-time emotional state, for example, determining whether they are in a relaxed mood or wanting to engage in energetic activities.

[0731] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[0732] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, for example highlighting calming content for a user looking to relax, or suggesting active content for an energetic user.

[0733] The generated proposal is returned to the server.

[0734] 4. Server Proposal Submission

[0735] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[0736] The server transmits the proposal data to the user's terminal so that the user can view it.

[0737] 5. User Verification and Viewing

[0738] The user checks the proposal sent from the server on the terminal.

[0739] If the user is satisfied with the suggestions, they can click the "Watch" button to immediately begin watching the suggested video content.

[0740] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0741] Examples:

[0742] For example, if a user wishes to "watch a relaxing video" but is stressed and seeking relaxation, they can use this system. The user uses their device to access the system and input a viewing request such as "I want to watch a relaxing video," and at the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine suggests video content that emphasizes relaxation, and the generation AI presents an appropriate video. The server returns this to the user, who then begins watching.

[0743] Example prompt sentence:

[0744] "Please suggest video content that will help users relax."

[0745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0746] Step 1:

[0747] A user accesses the system through a device, an app, or a web browser. The user inputs a viewing request and simultaneously captures a facial image with the device's camera. This viewing request data and facial image data are then sent to the server.

[0748] Input: Viewing request data, facial image data

[0749] Output: Viewing request data and facial image data sent to the server

[0750] Specific operation: The user types "I want to watch a relaxing video" in the app, and the camera simultaneously captures an image of the user's face.

[0751] Step 2:

[0752] The server receives the viewing request data and facial image data sent by the user and converts the data into an appropriate format. The viewing request data is passed to the generation AI, and the facial image data is passed to the emotion engine.

[0753] Input: Viewing request data, facial image data

[0754] Output: Data formatted to feed generative AI and emotion engines

[0755] Specific operation: The server prepares to pass the viewing request data to the generation AI and the facial image data to the emotion engine.

[0756] Step 3:

[0757] The emotion engine analyzes facial image data to assess the user's real-time emotional state, for example, whether the user is stressed or wants to relax.

[0758] Input: Facial image data

[0759] Output: User's emotional state data

[0760] How it works: The emotion engine uses facial image analysis algorithms to infer emotional states (e.g., stress) from captured facial images.

[0761] Step 4:

[0762] The generation AI analyzes the viewing request data and extracts the user's desired conditions, which clarifies the content of the viewing request.

[0763] Input: Viewing request data

[0764] Output: Extracted user preference data

[0765] Specific operation: The generation AI extracts the desired conditions of the user who is looking for a "relaxing video."

[0766] Step 5:

[0767] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[0768] Input: User's desired conditions data

[0769] Output: Optimal video content data

[0770] How it works: The generative AI searches a database for relaxing videos and identifies several candidates.

[0771] Step 6:

[0772] The emotion engine customizes suggestions based on the user's emotional state data and the video content identified by the generative AI. For example, if the user prioritizes relaxation, the generative AI will select particularly calming videos from the suggested content.

[0773] Input: User emotional state data, video content data generated by generative AI

[0774] Output: Customized video suggestion data

[0775] Specific operation: The emotion engine prioritizes video content suitable for relaxation.

[0776] Step 7:

[0777] The server sends the customized video suggestion data received from the generative AI and emotion engine to the user's device so that the user can view it.

[0778] Input: Customized video suggestion data

[0779] Output: Video suggestion data sent to the user's device

[0780] Specific operation: The server sends video suggestion data to the user's device and displays it on the app or browser.

[0781] Step 8:

[0782] The user can review the suggested video content on their device and click the "Watch" button to begin watching. If they are not satisfied with the suggested content, they can change the conditions and submit the request again.

[0783] Input: User confirmation action

[0784] Output: Start watching or re-request

[0785] Specific Action: User clicks the "Watch" button to play the video or resubmits the request.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] [Third embodiment]

[0790] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0791] 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.

[0792] 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).

[0793] 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.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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."

[0802] As an embodiment of this invention, we will explain a specific implementation method of a system for making holiday plans using generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0803] 1. User submits request

[0804] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0805] Users enter travel requests such as, "I'd like to go on a hot spring trip next weekend. My budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." in a form within the app or on a web page.

[0806] Once you have completed entering the information, click the "Submit" button to send the request data to the server.

[0807] 2. Server request reception and analysis

[0808] The server receives the request data from the user.

[0809] The server converts the received data into an appropriate format and prepares it for passing to the generation AI.

[0810] 3. Proposal creation using generative AI

[0811] The generation AI analyzes the user request data passed from the server, specifically extracting the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[0812] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0813] The generative AI creates a travel plan like this:

[0814] Travel destination: Hakone Onsen

[0815] Activities: Walking around the lake, visiting hot springs

[0816] Accommodation: XX Hot Springs Inn (2 nights, 15,000 yen per person per night)

[0817] Transportation: Round-trip Shinkansen ticket (total 15,000 yen)

[0818] The created proposal is returned to the server.

[0819] 4. Server Proposal Submission

[0820] The server converts the proposed data received from the generation AI into a format that can be shown to the user.

[0821] The server transmits the proposal data to the user's terminal so that the user can view it.

[0822] 5. User Verification and Authorization

[0823] The user checks the proposal sent from the server on the terminal.

[0824] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0825] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0826] 6. Reservation arrangements by the server

[0827] The server receives an "approval" request from the user.

[0828] The server makes online reservations for accommodation and transportation suggested by the AI.

[0829] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0830] Execute payment processing to complete Shinkansen ticket reservations.

[0831] Make sure your booking is confirmed.

[0832] 7. Notification of Final Travel Plans

[0833] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0834] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0835] Specific examples

[0836] For example, suppose a user wishes to "enjoy a hot spring trip next weekend." In this case, the user accesses the system using a terminal and enters a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests appropriate accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation and notifies the user of the final travel plan. This allows users to plan their trip with peace of mind without any complicated work.

[0837] The processing flow will be explained below.

[0838] Step 1:

[0839] The user accesses the system using a terminal, inputs a travel request (e.g., "I want to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and clicks the "Submit" button.

[0840] Step 2:

[0841] The server receives the travel request sent by the user, converts the request data into an appropriate format, and prepares it for passing to the generation AI.

[0842] Step 3:

[0843] The server passes the user's travel request data to the generation AI, which then analyzes the request data and extracts the user's desired conditions.

[0844] Step 4:

[0845] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[0846] Step 5:

[0847] The generative AI creates a travel plan, providing specific suggestions such as: destination: Hakone Hot Springs, activities: lakeside walks and hot spring tours, accommodation: XX hot spring inn (two nights, 15,000 yen per person per night), transportation: round-trip Shinkansen ticket (total 15,000 yen).

[0848] Step 6:

[0849] The generation AI returns the created proposal to the server, which then converts the proposal data received from the generation AI into a format that can be shown to the user.

[0850] Step 7:

[0851] The server sends the proposal data to the user's device so that the user can view it. The user then checks the proposal on the device.

[0852] Step 8:

[0853] If the user is satisfied with the proposal, they click the "Approve" button. If they are not satisfied with the proposal, they can change the conditions and submit the request again.

[0854] Step 9:

[0855] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the AI.

[0856] Step 10:

[0857] The server accesses the reservation website of the hot spring inn and confirms the accommodation reservation. It also processes the payment to complete the Shinkansen ticket reservation.

[0858] Step 11:

[0859] The server confirms the reservation and generates a final itinerary including the reservation confirmation information (confirmation number, accommodation details, and transportation information).

[0860] Step 12:

[0861] The server transmits the final itinerary to the user's terminal, allowing the user to confirm the details of the trip. The user confirms the final itinerary on the terminal and proceeds with preparations for the trip.

[0862] This series of steps allows users to eliminate cumbersome tasks and smoothly plan their trip.

[0863] Example 1

[0864] 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."

[0865] In today's modern lifestyle, planning a trip in a busy daily life is cumbersome and time-consuming, and many users find it stressful to coordinate the details of their trip. An effective system to solve this problem is needed.

[0866] 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.

[0867] In this invention, the server includes means for receiving a travel request input from a user, means for converting the travel request data into JSON format and preparing for analysis, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation based on the travel request, means for booking the suggested travel destinations, activities, accommodations, and transportation, means for sending the suggestion and booking information to the user's device, means for confirming the booking with the user's approval and generating a final travel plan, and means for sending the final travel plan to the user's device and notifying the user of the trip details. This allows the user to smoothly plan their trip without having to make complicated adjustments.

[0868] "User" means any person or entity that accesses the System and enters and submits a Travel Request.

[0869] "Terminal" refers to an electronic device, such as a smartphone, tablet, or personal computer, that a user uses to access the system.

[0870] "Travel request" refers to data including travel wishes and conditions that a user inputs and transmits to the system.

[0871] "Generative AI" refers to an artificial intelligence model that analyzes incoming travel requests and suggests appropriate destinations, activities, accommodations and transportation options.

[0872] "Server" refers to an electronic computer that receives a travel request from a user, passes it to the generating AI, and transmits the proposed data to the user's terminal.

[0873] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for storing and exchanging structured data.

[0874] "Proposal" refers to a plan that compiles information on travel destinations, activities, accommodations, and transportation generated by the Generating AI based on a travel request.

[0875] "Confirming reservation" refers to the action of the user approving the proposal and then the server officially reserving accommodation and transportation.

[0876] The "final travel plan" refers to a plan including detailed travel information that is generated by the server after the reservation is confirmed and sent to the user.

[0877] "Internet information" refers to information about travel destinations and activities obtained from websites and online databases.

[0878] This invention relates to a travel planning support system that utilizes generative AI, and has the function of proposing optimal travel plans based on the user's travel requests, confirming the reservations, and notifying the user. This system is mainly composed of four main elements: the user, the terminal, the server, and generative AI.

[0879] Users access the system using devices such as smartphones, tablets, and personal computers. The access method is a dedicated app or a web browser. Users input travel requests in a form within the app or on a web page. For example, a user might input a request such as, "I'd like to take a trip to a hot spring next weekend. My budget should be under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." By sending this request, data is sent to the server.

[0880] The server receives the request data sent by the user. It converts the received data into an appropriate format, such as JSON, and prepares it for analysis. For example, it extracts conditions included in the travel request, such as "hot spring trip," "budget under 50,000 yen," and "place where you can enjoy nature."

[0881] Based on the data received from the server, the generation AI analyzes the travel request. It searches past travel databases and information on the Internet to identify the travel destinations, activities, accommodations, and transportation methods that best fit the user's requirements. For example, the generation AI might suggest Hakone Hot Springs as a travel destination and create a specific travel plan that includes activities such as lakeside strolls and hot spring tours. Accommodation fees and transportation costs are also included in the calculations.

[0882] The travel plan created by the generation AI is returned to the server. The server converts it into a format for display to the user and sends the proposed data to the user's device. The user then checks the proposed travel plan on their device. If satisfied, the user clicks the "Approve" button and sends the approval data to the server.

[0883] The server receives an "approval" request from the user and makes online reservations for accommodation and transportation suggested by the generation AI. For example, the user accesses the accommodation reservation website and confirms the reservation. Similarly, the Shinkansen ticket reservation is completed. Once the reservation is confirmed, the server generates a final travel plan. This final plan includes a reservation confirmation number, accommodation details, transportation information, etc.

[0884] The final itinerary is sent from the server to the user's device, where the user can check the details, eliminating the need for complicated adjustments and enabling users to smoothly create their travel plans.

[0885] Specific examples

[0886] For example, suppose a user wants to enjoy a hot spring trip next weekend. In this case, the user would enter the following:

[0887] "I'd like to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature."

[0888] The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests suitable accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation options and notifies the user of the final travel plan, allowing the user to easily plan their trip.

[0889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0890] Step 1:

[0891] The user accesses the system using a terminal and inputs a travel request. The request includes desired conditions such as "I would like to take a trip to a hot spring next weekend. The budget should be under 50,000 yen, and I would like it to be somewhere where I can enjoy nature." After inputting the information, the user clicks the "Submit" button to send the request data to the server. The input of this step is the request data from the user, and the output is the sent request data.

[0892] Step 2:

[0893] The server receives the request data sent by the user. It converts the received data into JSON format and formats the data for analysis. During this process, the specific conditions of the request ("hot spring trip," "budget under 50,000 yen," "place where you can enjoy nature") are extracted. The input is the sent request data, and the output is the formatted request data.

[0894] Step 3:

[0895] The server sends the formatted request data to the generation AI. The generation AI receives the request data and begins analyzing it. Specifically, it searches past travel databases and information on the Internet to select the optimal travel destinations, activities, accommodations, and transportation methods based on the user's criteria. The input is the formatted request data, and the output is the generated travel plan.

[0896] Step 4:

[0897] The generation AI creates a travel plan based on the analysis results and sends it back to the server. The travel plan includes specific information such as Hakone hot springs, a stroll along the lake, accommodation for two nights, and Shinkansen transportation. The input is the analysis results by the generation AI, and the output is a specific travel plan.

[0898] Step 5:

[0899] The server converts the received itinerary into a format suitable for display to the user. The converted itinerary is sent to the user's device, allowing the user to view the proposals. The input is the generated itinerary, and the output is the itinerary converted for display.

[0900] Step 6:

[0901] The user checks the travel plan sent from the server on the terminal. If the proposed content is satisfactory, the user clicks the "Approve" button. The input is the displayed travel plan, and the output is the user's approval data.

[0902] Step 7:

[0903] The server receives the user's approval data and makes reservations for the proposed accommodation and transportation. It uses an online reservation system to obtain confirmed reservation information. For example, a user accesses an accommodation reservation site, confirms a hotel reservation, and also completes a Shinkansen ticket reservation. The input is the user's approval data, and the output is the confirmed reservation information.

[0904] Step 8:

[0905] The server generates a final itinerary based on the confirmed reservation information. This final itinerary includes the reservation confirmation number, accommodation details, transportation information, etc. The generated final itinerary is sent to the user's device to notify them of the trip details. The input is the confirmed reservation information, and the output is the final itinerary.

[0906] (Application example 1)

[0907] 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."

[0908] In conventional factories, work schedule adjustment and resource allocation are often performed manually by dedicated managers, resulting in issues of human error and reduced efficiency. Furthermore, inefficient resource allocation and scheduling can lead to reduced productivity and additional costs. The present invention aims to solve these issues and provide a system that optimizes factory work schedules and resource allocation.

[0909] 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.

[0910] In this invention, the server includes a means for receiving work requests input by a user, a means for using a generation AI to propose an appropriate work schedule and resource allocation based on the work requests, a means for making actual arrangements based on the proposed work schedule and resource allocation, and a means for transmitting the proposal and arrangement information to a user's terminal. This eliminates the traditional manual management method and enables efficient, error-free optimal work schedules and resource allocation.

[0911] "User" refers to the entity that uses this system to schedule work and allocate resources, such as factory or facility managers and operators.

[0912] The term "work request" refers to information including the work content and necessary resources that a user wants to perform at a specific date and time.

[0913] "Generative AI" refers to an artificial intelligence system that generates optimal schedules and resource allocations that meet the conditions based on requests entered by users.

[0914] A "work schedule" refers to a plan that determines the date, time, and order in which work should be performed based on input work requests.

[0915] "Resource allocation" refers to the appropriate allocation of resources such as labor, machinery, and materials required to carry out work.

[0916] "Arrangements" refers to the actual execution of necessary instructions and procurement based on the proposed work schedule and resource allocation.

[0917] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0918] As an embodiment of this invention, we will explain a specific implementation method of a factory work schedule optimization system that utilizes generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[0919] System construction

[0920] 1. User submits work request

[0921] Users access the system using a device (e.g., a computer, tablet, or smartphone) through an app or web browser. They enter a work request in a form within the app or on a web page, such as "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." Once the input is complete, the user clicks the "Submit" button to send the request data to the server.

[0922] 2. Server request reception and analysis

[0923] The server receives work request data from the user. The server converts the received data into an appropriate format and prepares it for passing to the generation AI. Specifically, it generates a prompt statement based on the user's desired date and time, work content, number of personnel, and machine operating status.

[0924] Example prompt sentence:

[0925] Date: 2023-10-01

[0926] Personnel size: 3

[0927] Work: Maintenance of machine A, cleaning of line B

[0928] Machine availability: {'Machine A': 80, 'Machine B': 50}

[0929] 3. Proposal creation using generative AI

[0930] The generation AI analyzes the user's work request data passed from the server. Specifically, it extracts work content and resource information and generates an efficient schedule and resource allocation. Based on the past work database and information on the Internet, the AI ​​creates a work schedule like the one below:

[0931] Work date: 2023-10-01

[0932] Work: Maintenance of machine A (morning), cleaning of line B (afternoon)

[0933] Resource allocation: XX company's maintenance team (3 people)

[0934] 4. Server Proposal Submission

[0935] The server converts the proposed data received from the generation AI into a format that can be displayed to the user, and then sends the proposed data to the user's device so that the user can view it.

[0936] 5. User Verification and Authorization

[0937] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[0938] 6. Arrangements by the Server

[0939] The server then issues the necessary instructions based on the work schedule and resource allocation proposed by the generative AI, ensuring that the work is carried out efficiently and reliably.

[0940] Hardware and software used

[0941] Hardware

[0942] Server: Data center in the factory

[0943] Devices: Computers, tablets, and smartphones used by users

[0944] software

[0945] Python: used as a programming language

[0946] pandas: a library for data processing

[0947] scikit-learn: A library for machine learning models

[0948] transformers: A library for using HuggingFace's generative AI model

[0949] By combining these elements, the system can generate efficient work schedules and resource allocations based on input work requests, significantly reducing the burden on users.

[0950] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0951] Step 1:

[0952] The user uses a terminal to access the system and input a work request. Specifically, the user inputs, "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." This input data includes the work date, work content, number of personnel, and machine operating status.

[0953] Step 2:

[0954] The device sends the user's input data to the server, which then analyzes it and converts it into a prompt format that the AI ​​uses.

[0955] Step 3:

[0956] The server generates a prompt sentence from the work request data to be passed to the generation AI. Specifically, it creates the following prompt sentence:

[0957] "Date and time: 2023-10-01 Staff size: 3 Work: Maintenance of machine A, cleaning of line B Machine operation status: {'machine A': 80, 'machine B': 50}"

[0958] Step 4:

[0959] The server sends a prompt to the generation AI, which analyzes the prompt and generates an efficient work schedule and resource allocation. The generation AI uses information from a database of past work and the internet to select the optimal schedule and resources.

[0960] Step 5:

[0961] The server receives the work schedule and resource allocation created by the generation AI. This information includes the work date and time, specific work content, and resource allocation. For example, "Work date and time: 2023-10-01 Work content: Maintenance of machine A (morning), cleaning of line B (afternoon) Resource allocation: Maintenance team (3 people)."

[0962] Step 6:

[0963] The server converts the proposed data received from the generation AI into a format that can be viewed by the user and sends it to the user's device.

[0964] Step 7:

[0965] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and send the request again.

[0966] Step 8:

[0967] Upon receiving the user's "approval" request, the server makes the actual arrangements based on the proposed work schedule and resource allocation. Specifically, it deploys the maintenance team at the specified date and time and sends the necessary instructions to each person in charge via the system.

[0968] 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.

[0969] As an embodiment of this invention, we will explain a specific implementation method of a system for planning holidays using generative AI and an emotion engine. This system consists of five main elements: a user, a terminal, a server, generative AI, and an emotion engine.

[0970] 1. User submits request

[0971] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[0972] Users input their travel requests in a form within the app or on a web page, such as "I'd like to take a hot spring trip next weekend. The budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The app also simultaneously obtains real-time emotion data using the user's facial recognition camera.

[0973] Once the input is complete, the user clicks the "Send" button to send the travel request data and emotion data to the server.

[0974] 2. Server request reception and analysis

[0975] The server receives the travel request and emotion data sent by the user.

[0976] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[0977] 3. Proposal creation using generative AI and emotion engine

[0978] The generation AI analyzes the user's travel request data passed from the server and extracts the user's desired conditions.

[0979] The emotion engine analyzes the user's emotion data and evaluates the user's current mental state, for example, determining whether the user feels like relaxing or wants to be active.

[0980] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[0981] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, highlighting hot springs for a user looking to relax, for example, and suggesting adventure activities for an active user.

[0982] The generated proposal is returned to the server.

[0983] 4. Server Proposal Submission

[0984] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[0985] The server transmits the proposal data to the user's terminal so that the user can view it.

[0986] 5. User Verification and Authorization

[0987] The user checks the proposal sent from the server on the terminal.

[0988] If the user is satisfied with the proposal, he clicks the "Approve" button.

[0989] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[0990] 6. Reservation arrangements by the server

[0991] The server receives an "approval" request from the user.

[0992] The server makes online reservations for accommodation and transportation suggested by the AI.

[0993] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[0994] Execute payment processing to complete Shinkansen ticket reservations.

[0995] Make sure your booking is confirmed.

[0996] 7. Notification of Final Travel Plans

[0997] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[0998] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[0999] Specific examples

[1000] For example, a user might wish to "enjoy a hot spring trip next weekend," but is stressed and seeking relaxation. Using this system, the user accesses the system using a device and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine proposes a travel plan that emphasizes relaxation, and the generation AI selects Hakone Hot Springs as the destination and suggests appropriate accommodations and transportation options. The server returns this to the user, and if the user approves, the server makes the final reservations and notifies them of the confirmed travel plan. This allows users to smoothly plan a trip that suits their emotional state.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] Users access the system using a terminal. The user inputs a travel request (e.g., "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and the device's camera captures the user's facial expressions in real time. The data (travel request and facial expression data) are sent to the server by clicking the "Send" button.

[1004] Step 2:

[1005] The server receives the travel request data and facial expression data sent by the user, analyzes the received data, converts it into an appropriate format, and prepares it for passing to the generative AI and emotion engine.

[1006] Step 3:

[1007] The server passes the user's travel request data and facial expression data to the generation AI and emotion engine. The generation AI analyzes the request data and extracts the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[1008] Step 4:

[1009] The emotion engine analyzes the facial expression data passed to it and assesses the user's emotional state, for example, the emotion engine may sense that the user is seeking relaxation.

[1010] Step 5:

[1011] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[1012] Step 6:

[1013] The emotion engine customizes the suggestions made by the generative AI based on the user's emotional state. For example, if the user is looking to relax, the generative AI will highlight hot springs and relaxation programs in its suggested plans.

[1014] Step 7:

[1015] The generative AI completes a customized itinerary and sends it back to the server, with suggestions including specific destinations, accommodations, activities, and transportation details.

[1016] Step 8:

[1017] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user, and then sends the proposed data to the user's device.

[1018] Step 9:

[1019] The user checks the proposal sent from the server on the device. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[1020] Step 10:

[1021] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the emotion engine and generative AI.

[1022] Step 11:

[1023] For example, the server accesses the reservation site of a certain hot spring inn and confirms the accommodation reservation.Similarly, the server executes a payment process to complete the Shinkansen ticket reservation.

[1024] Step 12:

[1025] The server confirms that the reservation is confirmed. The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device.

[1026] Step 13:

[1027] Users can check the final travel plan on their device and proceed with travel preparations, allowing them to smoothly plan a trip that suits their emotional state and desired conditions.

[1028] Example 2

[1029] 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."

[1030] Conventional travel planning systems often fail to meet user expectations because they propose travel plans without taking the user's emotional state into consideration. There is a need for a system that can efficiently book travel destinations and activities that suit individual user needs. Furthermore, there is a need for a system that can provide a more satisfying travel experience by acquiring users' emotional data in real time and reflecting it in the proposals.

[1031] 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.

[1032] In this invention, the server includes means for receiving a travel request input by a user, means for acquiring user emotion data, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation means based on the travel request, means for customizing the suggestions based on the emotion data using an emotion engine, means for booking the suggested travel destinations, activities, accommodations, and transportation means, and means for transmitting the suggestions and booking information to the user's terminal. This makes it possible to provide a personalized travel plan that takes the user's emotional state into consideration, thereby increasing user satisfaction.

[1033] 1. "User" refers to an individual who uses the System to enter a travel request and review and approve the proposed travel plan.

[1034] 2. "Device" refers to the electronic device (e.g., smartphone, tablet, PC) used by a User to enter and submit a Travel Request and receive suggested Travel Itineraries.

[1035] 3. "Server" refers to a computing device that receives requests from users, generates and provides travel plans in cooperation with the Generative AI and Emotion Engine, and processes reservations.

[1036] 4. “Generative AI” refers to artificial intelligence technology that suggests suitable travel destinations, activities, accommodations and transportation based on travel requests.

[1037] 5. "Emotion Engine" refers to a system that analyzes a user's emotional data and customizes suggestions based on the user's current emotional state.

[1038] 6. "Travel request" refers to the desired travel conditions (e.g., destination, budget, time, etc.) entered by a user into the system.

[1039] 7. “Emotional Data” means data that represents a user’s emotional state (e.g., facial expressions, voice tone, text analysis, etc.) and that is acquired by the system in real time.

[1040] 8. “Recommendations” refers to travel plans created by the Generative AI and Emotion Engine based on a user’s travel request and emotion data.

[1041] 9. "Booking" means the actual arrangement of the proposed travel destination, activity, accommodation, transportation, etc.

[1042] 10. "Travel Database" refers to a data storage facility that stores past travel information and travel-related information on the Internet.

[1043] As an embodiment of this invention, we will show a specific implementation method of a system that supports users' travel planning by utilizing generative AI and an emotion engine. This system is implemented using the following hardware and software.

[1044] Hardware used

[1045] Devices: smartphones, tablets, PCs, etc.

[1046] Server: a high-performance computing device

[1047] Software used

[1048] Generative AI models (e.g., GPT-3)

[1049] Emotion engine (e.g. Emotion API)

[1050] Frontend: App or web browser

[1051] Backend: A platform for receiving and analyzing user requests, creating and delivering proposals

[1052] Processing Description

[1053] 1. Fill out and submit your request

[1054] Users use their device to access the application or web browser and enter a travel request, such as "I'd like to take a hot spring trip next weekend, with a budget of less than 50,000 yen and somewhere where I can enjoy nature." Real-time emotional data is also provided using a facial recognition camera.

[1055] 2. Receiving and analyzing request and emotion data

[1056] The server receives the travel request and emotion data sent by the user, converts the data into an appropriate format, and passes it to the generative AI and emotion engine. For example, the server analyzes the travel request as text data and converts the emotion data into numerical data as a result of facial expression analysis.

[1057] 3. Proposal generation and customization

[1058] The generative AI analyzes the user's travel request data and extracts the user's desired conditions. The emotion engine analyzes the user's emotional data and evaluates their current mental state, for example, determining whether they want to relax or be active.

[1059] 4. Submitting and Viewing Proposals

[1060] The server converts the proposal data generated by the generative AI and emotion engine into a format that can be viewed by the user and sends it to the user's device, where the user can review the proposal and approve or change the conditions.

[1061] 5. Reservation Procedure

[1062] If the user approves the proposal, the server will make online reservations for accommodation and transportation, for example, accessing the website of a hot spring inn to confirm the reservation and reserving a Shinkansen ticket.

[1063] 6. Notification of Travel Plans

[1064] The server generates a final itinerary (including reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device, where the user can confirm the trip details.

[1065] Specific examples

[1066] For example, if a user wishes to "enjoy a hot spring trip next weekend" and is feeling stressed and seeking relaxation, they can use this system. The user accesses the system using a terminal and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, they transmit facial expression data via the camera indicating their stress level. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine creates a travel plan that emphasizes relaxation, and the generation AI suggests appropriate accommodations and transportation options with Hakone Hot Springs as the destination. The server returns this to the user, and if the user approves, it makes the final reservations and notifies them of the confirmed travel plan. An example of a prompt sentence might be, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature. I'm currently feeling stressed and want to relax."

[1067] In this way, the system allows users to smoothly plan their trip according to their emotional state.

[1068] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1069] Step 1: User enters and submits request

[1070] Users use devices such as smartphones, tablets, or PCs to access an app or web browser and enter their travel request. For example, they might enter, "I'd like to take a trip to a hot spring next weekend. My budget is under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." At this time, real-time emotional data (facial expression data) is also acquired using the device's camera. Once the input data and emotional data are ready, the user clicks the "Send" button to send the data to the server.

[1071] Step 2: The server receives and formats the request

[1072] The server receives the travel request data and emotion data sent by the user. It analyzes the received data and converts it into an appropriate format to be passed to the generative AI and emotion engine. Specifically, it analyzes the travel request as text data and converts facial expression data into numerical data. The analyzed data is then passed to the next processing step.

[1073] Step 3: Generative AI analyzes the travel request

[1074] The generation AI extracts the user's specific desired conditions based on the travel request data passed from the server. For example, conditions such as destination, budget, and places to enjoy nature are extracted through text analysis. This provides the basic data for generating travel destination, activity, accommodation, and transportation options. The input is the travel request data, and the output is the extracted desired conditions.

[1075] Step 4: Analyze the emotional data with the emotion engine

[1076] The emotion engine analyzes the emotion data passed from the server and evaluates the user's current mental state. Specifically, it analyzes facial expression data to determine whether the user wants to relax or be active. The results of this analysis are used by the generative AI to customize suggestions. The input is emotion data, and the output is the analyzed emotional state.

[1077] Step 5: Generative AI creates proposals

[1078] The generation AI proposes specific travel destinations, activities, accommodations, and transportation methods based on the extracted user's desired conditions and the analysis results of the emotion engine. For example, it generates a plan that includes "Hakone Hot Springs as the destination, relaxing accommodation A, and scenic hiking course B." The input is the user's desired conditions and emotional state, and the output is a specific travel proposal.

[1079] Step 6: Server Sends Proposal

[1080] The server converts the generated travel proposals into a format that can be viewed by the user and sends them to the user's device. Specifically, it converts them into HTML or JSON format so that the proposals can be displayed on the device screen. The input is the travel proposal data, and the output is in a format that can be displayed on the user's screen.

[1081] Step 7: User Review and Authorization

[1082] The user reviews the submitted travel proposal on their device. If they are satisfied with the proposal, they click the "Accept" button. If they are not satisfied, they can change the conditions and submit the request again. The input is the travel proposal data, and the output is the user's approval or a new request.

[1083] Step 8: Reservations are made by the server

[1084] The server receives the user's approval request and makes online reservations for the accommodation and transportation suggested by the generation AI. For example, the user accesses a reservation website for a hot spring inn, confirms the reservation, and executes payment processing for the Shinkansen ticket. The input is the user's approval request, and the output is the reservation confirmation information.

[1085] Step 9: Finalize your travel plans

[1086] The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device. The user can then confirm the trip details. The input is the reservation confirmation information, and the output is the final itinerary.

[1087] These steps allow users to seamlessly create personalized travel plans based on their emotional state.

[1088] (Application example 2)

[1089] 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."

[1090] Conventional video streaming services suggest content based on a user's viewing history and general preferences, but they cannot suggest content that takes into account the user's real-time emotional state, resulting in low user satisfaction. In particular, there is a demand for services that can suggest relaxing content to users who are feeling stressed, and stimulating content to users who are full of energy. It is also important that the suggested content can be viewed immediately.

[1091] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a viewing request input by a user, means for using a generation AI to suggest appropriate video content based on the viewing request, means for analyzing a facial image of the user and acquiring emotional data, means for customizing the suggested video content based on the emotional data, and means for transmitting information about the suggested video content to the user's terminal. This makes it possible to suggest video content that takes into account the user's real-time emotional state, thereby increasing user satisfaction.

[1092] "User" refers to a person who utilizes the system to enter a viewing request and view suggested video content.

[1093] A "viewing request" is data about a video viewing request that a user inputs into the system, and includes the type and theme of the content they wish to view.

[1094] "Generative AI" is an artificial intelligence that uses machine learning algorithms to suggest appropriate video content based on viewing requests.

[1095] "Video content" refers to videos that are proposed to and viewed by users, and there are a variety of types, including relaxing and stimulating videos.

[1096] "Facial image analysis" refers to the process of capturing emotional data using a user's facial image and using algorithms to determine emotions.

[1097] "Emotion data" is data that represents the user's real-time emotional state and is obtained through facial image analysis.

[1098] "Customization" refers to tailoring content suggestions to a user's specific preferences and state based on captured emotional data.

[1099] A "terminal" is a device used by a user to input a viewing request or view suggested video content, and includes a smartphone, tablet, personal computer, etc.

[1100] "Suggestion" refers to the act and content of the generative AI selecting the most suitable video content based on the user's viewing request and emotional data and showing it to the user.

[1101] As an embodiment of this invention, we will explain a specific implementation method of a system that combines emotion data and viewing requests to propose optimal video content. This system consists of five main elements: a user, a terminal, a server, a generation AI, and an emotion engine.

[1102] 1. User submits request

[1103] Users use a device (e.g., a smartphone, tablet, or personal computer) to access the system through an app or web browser.

[1104] Users enter viewing requests such as "I want to watch a relaxing video" into an in-app form or on a web page.

[1105] In addition, facial images are captured in real time using the device's camera, and emotional data is simultaneously obtained.

[1106] Once you have completed the input, click the "Send" button to send the viewing request data and emotion data to the server.

[1107] 2. Server request reception and analysis

[1108] The server receives the viewing request and emotion data sent from the user.

[1109] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[1110] 3. Proposal creation using generative AI and emotion engine

[1111] The generation AI analyzes the user's viewing request data passed from the server and extracts the user's desired conditions.

[1112] The emotion engine analyzes the user's facial images to obtain emotional data and evaluate their real-time emotional state, for example, determining whether they are in a relaxed mood or wanting to engage in energetic activities.

[1113] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[1114] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, for example highlighting calming content for a user looking to relax, or suggesting active content for an energetic user.

[1115] The generated proposal is returned to the server.

[1116] 4. Server Proposal Submission

[1117] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[1118] The server transmits the proposal data to the user's terminal so that the user can view it.

[1119] 5. User Verification and Viewing

[1120] The user checks the proposal sent from the server on the terminal.

[1121] If the user is satisfied with the suggestions, they can click the "Watch" button to immediately begin watching the suggested video content.

[1122] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[1123] Examples:

[1124] For example, if a user wishes to "watch a relaxing video" but is stressed and seeking relaxation, they can use this system. The user uses their device to access the system and input a viewing request such as "I want to watch a relaxing video," and at the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine suggests video content that emphasizes relaxation, and the generation AI presents an appropriate video. The server returns this to the user, who then begins watching.

[1125] Example prompt sentence:

[1126] "Please suggest video content that will help users relax."

[1127] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1128] Step 1:

[1129] A user accesses the system through a device, an app, or a web browser. The user inputs a viewing request and simultaneously captures a facial image with the device's camera. This viewing request data and facial image data are then sent to the server.

[1130] Input: Viewing request data, facial image data

[1131] Output: Viewing request data and facial image data sent to the server

[1132] Specific operation: The user types "I want to watch a relaxing video" in the app, and the camera simultaneously captures an image of the user's face.

[1133] Step 2:

[1134] The server receives the viewing request data and facial image data sent by the user and converts the data into an appropriate format. The viewing request data is passed to the generation AI, and the facial image data is passed to the emotion engine.

[1135] Input: Viewing request data, facial image data

[1136] Output: Data formatted to feed generative AI and emotion engines

[1137] Specific operation: The server prepares to pass the viewing request data to the generation AI and the facial image data to the emotion engine.

[1138] Step 3:

[1139] The emotion engine analyzes facial image data to assess the user's real-time emotional state, for example, whether the user is stressed or wants to relax.

[1140] Input: Facial image data

[1141] Output: User's emotional state data

[1142] How it works: The emotion engine uses facial image analysis algorithms to infer emotional states (e.g., stress) from captured facial images.

[1143] Step 4:

[1144] The generation AI analyzes the viewing request data and extracts the user's desired conditions, which clarifies the content of the viewing request.

[1145] Input: Viewing request data

[1146] Output: Extracted user preference data

[1147] Specific operation: The generation AI extracts the desired conditions of the user who is looking for a "relaxing video."

[1148] Step 5:

[1149] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[1150] Input: User's desired conditions data

[1151] Output: Optimal video content data

[1152] How it works: The generative AI searches a database for relaxing videos and identifies several candidates.

[1153] Step 6:

[1154] The emotion engine customizes suggestions based on the user's emotional state data and the video content identified by the generative AI. For example, if the user prioritizes relaxation, the generative AI will select particularly calming videos from the suggested content.

[1155] Input: User emotional state data, video content data generated by generative AI

[1156] Output: Customized video suggestion data

[1157] Specific operation: The emotion engine prioritizes video content suitable for relaxation.

[1158] Step 7:

[1159] The server sends the customized video suggestion data received from the generative AI and emotion engine to the user's device so that the user can view it.

[1160] Input: Customized video suggestion data

[1161] Output: Video suggestion data sent to the user's device

[1162] Specific operation: The server sends video suggestion data to the user's device and displays it on the app or browser.

[1163] Step 8:

[1164] The user can review the suggested video content on their device and click the "Watch" button to begin watching. If they are not satisfied with the suggested content, they can change the conditions and submit the request again.

[1165] Input: User confirmation action

[1166] Output: Start watching or re-request

[1167] Specific Action: User clicks the "Watch" button to play the video or resubmits the request.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] [Fourth embodiment]

[1172] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1173] 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.

[1174] 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).

[1175] 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.

[1176] 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.

[1177] 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).

[1178] 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.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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."

[1185] As an embodiment of this invention, we will explain a specific implementation method of a system for making holiday plans using generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[1186] 1. User submits request

[1187] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[1188] Users enter travel requests such as, "I'd like to go on a hot spring trip next weekend. My budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." in a form within the app or on a web page.

[1189] Once you have completed entering the information, click the "Submit" button to send the request data to the server.

[1190] 2. Server request reception and analysis

[1191] The server receives the request data from the user.

[1192] The server converts the received data into an appropriate format and prepares it for passing to the generation AI.

[1193] 3. Proposal creation using generative AI

[1194] The generation AI analyzes the user request data passed from the server, specifically extracting the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[1195] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[1196] The generative AI creates a travel plan like this:

[1197] Travel destination: Hakone Onsen

[1198] Activities: Walking around the lake, visiting hot springs

[1199] Accommodation: XX Hot Springs Inn (2 nights, 15,000 yen per person per night)

[1200] Transportation: Round-trip Shinkansen ticket (total 15,000 yen)

[1201] The created proposal is returned to the server.

[1202] 4. Server Proposal Submission

[1203] The server converts the proposed data received from the generation AI into a format that can be shown to the user.

[1204] The server transmits the proposal data to the user's terminal so that the user can view it.

[1205] 5. User Verification and Authorization

[1206] The user checks the proposal sent from the server on the terminal.

[1207] If the user is satisfied with the proposal, he clicks the "Approve" button.

[1208] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[1209] 6. Reservation arrangements by the server

[1210] The server receives an "approval" request from the user.

[1211] The server makes online reservations for accommodation and transportation suggested by the AI.

[1212] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[1213] Execute payment processing to complete Shinkansen ticket reservations.

[1214] Make sure your booking is confirmed.

[1215] 7. Notification of Final Travel Plans

[1216] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[1217] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[1218] Specific examples

[1219] For example, suppose a user wishes to "enjoy a hot spring trip next weekend." In this case, the user accesses the system using a terminal and enters a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests appropriate accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation and notifies the user of the final travel plan. This allows users to plan their trip with peace of mind without any complicated work.

[1220] The processing flow will be explained below.

[1221] Step 1:

[1222] The user accesses the system using a terminal, inputs a travel request (e.g., "I want to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and clicks the "Submit" button.

[1223] Step 2:

[1224] The server receives the travel request sent by the user, converts the request data into an appropriate format, and prepares it for passing to the generation AI.

[1225] Step 3:

[1226] The server passes the user's travel request data to the generation AI, which then analyzes the request data and extracts the user's desired conditions.

[1227] Step 4:

[1228] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[1229] Step 5:

[1230] The generative AI creates a travel plan, providing specific suggestions such as: destination: Hakone Hot Springs, activities: lakeside walks and hot spring tours, accommodation: XX hot spring inn (two nights, 15,000 yen per person per night), transportation: round-trip Shinkansen ticket (total 15,000 yen).

[1231] Step 6:

[1232] The generation AI returns the created proposal to the server, which then converts the proposal data received from the generation AI into a format that can be shown to the user.

[1233] Step 7:

[1234] The server sends the proposal data to the user's device so that the user can view it. The user then checks the proposal on the device.

[1235] Step 8:

[1236] If the user is satisfied with the proposal, they click the "Approve" button. If they are not satisfied with the proposal, they can change the conditions and submit the request again.

[1237] Step 9:

[1238] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the AI.

[1239] Step 10:

[1240] The server accesses the reservation website of the hot spring inn and confirms the accommodation reservation. It also processes the payment to complete the Shinkansen ticket reservation.

[1241] Step 11:

[1242] The server confirms the reservation and generates a final itinerary including the reservation confirmation information (confirmation number, accommodation details, and transportation information).

[1243] Step 12:

[1244] The server transmits the final itinerary to the user's terminal, allowing the user to confirm the details of the trip. The user confirms the final itinerary on the terminal and proceeds with preparations for the trip.

[1245] This series of steps allows users to eliminate cumbersome tasks and smoothly plan their trip.

[1246] Example 1

[1247] 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."

[1248] In today's modern lifestyle, planning a trip in a busy daily life is cumbersome and time-consuming, and many users find it stressful to coordinate the details of their trip. An effective system to solve this problem is needed.

[1249] 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.

[1250] In this invention, the server includes means for receiving a travel request input from a user, means for converting the travel request data into JSON format and preparing for analysis, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation based on the travel request, means for booking the suggested travel destinations, activities, accommodations, and transportation, means for sending the suggestion and booking information to the user's device, means for confirming the booking with the user's approval and generating a final travel plan, and means for sending the final travel plan to the user's device and notifying the user of the trip details. This allows the user to smoothly plan their trip without having to make complicated adjustments.

[1251] "User" means any person or entity that accesses the System and enters and submits a Travel Request.

[1252] "Terminal" refers to an electronic device, such as a smartphone, tablet, or personal computer, that a user uses to access the system.

[1253] "Travel request" refers to data including travel wishes and conditions that a user inputs and transmits to the system.

[1254] "Generative AI" refers to an artificial intelligence model that analyzes incoming travel requests and suggests appropriate destinations, activities, accommodations and transportation options.

[1255] "Server" refers to an electronic computer that receives a travel request from a user, passes it to the generating AI, and transmits the proposed data to the user's terminal.

[1256] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for storing and exchanging structured data.

[1257] "Proposal" refers to a plan that compiles information on travel destinations, activities, accommodations, and transportation generated by the Generating AI based on a travel request.

[1258] "Confirming reservation" refers to the action of the user approving the proposal and then the server officially reserving accommodation and transportation.

[1259] The "final travel plan" refers to a plan including detailed travel information that is generated by the server after the reservation is confirmed and sent to the user.

[1260] "Internet information" refers to information about travel destinations and activities obtained from websites and online databases.

[1261] This invention relates to a travel planning support system that utilizes generative AI, and has the function of proposing optimal travel plans based on the user's travel requests, confirming the reservations, and notifying the user. This system is mainly composed of four main elements: the user, the terminal, the server, and generative AI.

[1262] Users access the system using devices such as smartphones, tablets, and personal computers. The access method is a dedicated app or a web browser. Users input travel requests in a form within the app or on a web page. For example, a user might input a request such as, "I'd like to take a trip to a hot spring next weekend. My budget should be under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." By sending this request, data is sent to the server.

[1263] The server receives the request data sent by the user. It converts the received data into an appropriate format, such as JSON, and prepares it for analysis. For example, it extracts conditions included in the travel request, such as "hot spring trip," "budget under 50,000 yen," and "place where you can enjoy nature."

[1264] Based on the data received from the server, the generation AI analyzes the travel request. It searches past travel databases and information on the Internet to identify the travel destinations, activities, accommodations, and transportation methods that best fit the user's requirements. For example, the generation AI might suggest Hakone Hot Springs as a travel destination and create a specific travel plan that includes activities such as lakeside strolls and hot spring tours. Accommodation fees and transportation costs are also included in the calculations.

[1265] The travel plan created by the generation AI is returned to the server. The server converts it into a format for display to the user and sends the proposed data to the user's device. The user then checks the proposed travel plan on their device. If satisfied, the user clicks the "Approve" button and sends the approval data to the server.

[1266] The server receives an "approval" request from the user and makes online reservations for accommodation and transportation suggested by the generation AI. For example, the user accesses the accommodation reservation website and confirms the reservation. Similarly, the Shinkansen ticket reservation is completed. Once the reservation is confirmed, the server generates a final travel plan. This final plan includes a reservation confirmation number, accommodation details, transportation information, etc.

[1267] The final itinerary is sent from the server to the user's device, where the user can check the details, eliminating the need for complicated adjustments and enabling users to smoothly create their travel plans.

[1268] Specific examples

[1269] For example, suppose a user wants to enjoy a hot spring trip next weekend. In this case, the user would enter the following:

[1270] "I'd like to go on a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature."

[1271] The server receives this request and passes the request data to the generation AI. The generation AI analyzes the request, selects Hakone Hot Springs as the destination, and suggests suitable accommodation and transportation options. The server returns this information to the user, and if the user approves, it makes reservations for accommodation and transportation options and notifies the user of the final travel plan, allowing the user to easily plan their trip.

[1272] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1273] Step 1:

[1274] The user accesses the system using a terminal and inputs a travel request. The request includes desired conditions such as "I would like to take a trip to a hot spring next weekend. The budget should be under 50,000 yen, and I would like it to be somewhere where I can enjoy nature." After inputting the information, the user clicks the "Submit" button to send the request data to the server. The input of this step is the request data from the user, and the output is the sent request data.

[1275] Step 2:

[1276] The server receives the request data sent by the user. It converts the received data into JSON format and formats the data for analysis. During this process, the specific conditions of the request ("hot spring trip," "budget under 50,000 yen," "place where you can enjoy nature") are extracted. The input is the sent request data, and the output is the formatted request data.

[1277] Step 3:

[1278] The server sends the formatted request data to the generation AI. The generation AI receives the request data and begins analyzing it. Specifically, it searches past travel databases and information on the Internet to select the optimal travel destinations, activities, accommodations, and transportation methods based on the user's criteria. The input is the formatted request data, and the output is the generated travel plan.

[1279] Step 4:

[1280] The generation AI creates a travel plan based on the analysis results and sends it back to the server. The travel plan includes specific information such as Hakone hot springs, a stroll along the lake, accommodation for two nights, and Shinkansen transportation. The input is the analysis results by the generation AI, and the output is a specific travel plan.

[1281] Step 5:

[1282] The server converts the received itinerary into a format suitable for display to the user. The converted itinerary is sent to the user's device, allowing the user to view the proposals. The input is the generated itinerary, and the output is the itinerary converted for display.

[1283] Step 6:

[1284] The user checks the travel plan sent from the server on the terminal. If the proposed content is satisfactory, the user clicks the "Approve" button. The input is the displayed travel plan, and the output is the user's approval data.

[1285] Step 7:

[1286] The server receives the user's approval data and makes reservations for the proposed accommodation and transportation. It uses an online reservation system to obtain confirmed reservation information. For example, a user accesses an accommodation reservation site, confirms a hotel reservation, and also completes a Shinkansen ticket reservation. The input is the user's approval data, and the output is the confirmed reservation information.

[1287] Step 8:

[1288] The server generates a final itinerary based on the confirmed reservation information. This final itinerary includes the reservation confirmation number, accommodation details, transportation information, etc. The generated final itinerary is sent to the user's device to notify them of the trip details. The input is the confirmed reservation information, and the output is the final itinerary.

[1289] (Application example 1)

[1290] 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."

[1291] In conventional factories, work schedule adjustment and resource allocation are often performed manually by dedicated managers, resulting in issues of human error and reduced efficiency. Furthermore, inefficient resource allocation and scheduling can lead to reduced productivity and additional costs. The present invention aims to solve these issues and provide a system that optimizes factory work schedules and resource allocation.

[1292] 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.

[1293] In this invention, the server includes a means for receiving work requests input by a user, a means for using a generation AI to propose an appropriate work schedule and resource allocation based on the work requests, a means for making actual arrangements based on the proposed work schedule and resource allocation, and a means for transmitting the proposal and arrangement information to a user's terminal. This eliminates the traditional manual management method and enables efficient, error-free optimal work schedules and resource allocation.

[1294] "User" refers to the entity that uses this system to schedule work and allocate resources, such as factory or facility managers and operators.

[1295] The term "work request" refers to information including the work content and necessary resources that a user wants to perform at a specific date and time.

[1296] "Generative AI" refers to an artificial intelligence system that generates optimal schedules and resource allocations that meet the conditions based on requests entered by users.

[1297] A "work schedule" refers to a plan that determines the date, time, and order in which work should be performed based on input work requests.

[1298] "Resource allocation" refers to the appropriate allocation of resources such as labor, machinery, and materials required to carry out work.

[1299] "Arrangements" refers to the actual execution of necessary instructions and procurement based on the proposed work schedule and resource allocation.

[1300] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[1301] As an embodiment of this invention, we will explain a specific implementation method of a factory work schedule optimization system that utilizes generative AI. This system consists of four main elements: a user, a terminal, a server, and generative AI.

[1302] System construction

[1303] 1. User submits work request

[1304] Users access the system using a device (e.g., a computer, tablet, or smartphone) through an app or web browser. They enter a work request in a form within the app or on a web page, such as "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." Once the input is complete, the user clicks the "Submit" button to send the request data to the server.

[1305] 2. Server request reception and analysis

[1306] The server receives work request data from the user. The server converts the received data into an appropriate format and prepares it for passing to the generation AI. Specifically, it generates a prompt statement based on the user's desired date and time, work content, number of personnel, and machine operating status.

[1307] Example prompt sentence:

[1308] Date: 2023-10-01

[1309] Personnel size: 3

[1310] Work: Maintenance of machine A, cleaning of line B

[1311] Machine availability: {'Machine A': 80, 'Machine B': 50}

[1312] 3. Proposal creation using generative AI

[1313] The generation AI analyzes the user's work request data passed from the server. Specifically, it extracts work content and resource information and generates an efficient schedule and resource allocation. Based on the past work database and information on the Internet, the AI ​​creates a work schedule like the one below:

[1314] Work date: 2023-10-01

[1315] Work: Maintenance of machine A (morning), cleaning of line B (afternoon)

[1316] Resource allocation: XX company's maintenance team (3 people)

[1317] 4. Server Proposal Submission

[1318] The server converts the proposed data received from the generation AI into a format that can be displayed to the user, and then sends the proposed data to the user's device so that the user can view it.

[1319] 5. User Verification and Authorization

[1320] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[1321] 6. Arrangements by the Server

[1322] The server then issues the necessary instructions based on the work schedule and resource allocation proposed by the generative AI, ensuring that the work is carried out efficiently and reliably.

[1323] Hardware and software used

[1324] Hardware

[1325] Server: Data center in the factory

[1326] Devices: Computers, tablets, and smartphones used by users

[1327] software

[1328] Python: used as a programming language

[1329] pandas: a library for data processing

[1330] scikit-learn: A library for machine learning models

[1331] transformers: A library for using HuggingFace's generative AI model

[1332] By combining these elements, the system can generate efficient work schedules and resource allocations based on input work requests, significantly reducing the burden on users.

[1333] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1334] Step 1:

[1335] The user uses a terminal to access the system and input a work request. Specifically, the user inputs, "I would like to perform maintenance on Machine A and cleaning on Line B on 2023-10-01. There will be three personnel, and Machine A will have an operating rate of 80%, and Machine B will have an operating rate of 50%." This input data includes the work date, work content, number of personnel, and machine operating status.

[1336] Step 2:

[1337] The device sends the user's input data to the server, which then analyzes it and converts it into a prompt format that the AI ​​uses.

[1338] Step 3:

[1339] The server generates a prompt sentence from the work request data to be passed to the generation AI. Specifically, it creates the following prompt sentence:

[1340] "Date and time: 2023-10-01 Staff size: 3 Work: Maintenance of machine A, cleaning of line B Machine operation status: {'machine A': 80, 'machine B': 50}"

[1341] Step 4:

[1342] The server sends a prompt to the generation AI, which analyzes the prompt and generates an efficient work schedule and resource allocation. The generation AI uses information from a database of past work and the internet to select the optimal schedule and resources.

[1343] Step 5:

[1344] The server receives the work schedule and resource allocation created by the generation AI. This information includes the work date and time, specific work content, and resource allocation. For example, "Work date and time: 2023-10-01 Work content: Maintenance of machine A (morning), cleaning of line B (afternoon) Resource allocation: Maintenance team (3 people)."

[1345] Step 6:

[1346] The server converts the proposed data received from the generation AI into a format that can be viewed by the user and sends it to the user's device.

[1347] Step 7:

[1348] The user checks the proposal sent from the server on the terminal. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and send the request again.

[1349] Step 8:

[1350] Upon receiving the user's "approval" request, the server makes the actual arrangements based on the proposed work schedule and resource allocation. Specifically, it deploys the maintenance team at the specified date and time and sends the necessary instructions to each person in charge via the system.

[1351] 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.

[1352] As an embodiment of this invention, we will explain a specific implementation method of a system for planning holidays using generative AI and an emotion engine. This system consists of five main elements: a user, a terminal, a server, generative AI, and an emotion engine.

[1353] 1. User submits request

[1354] Users use a device (e.g., a smartphone, tablet, or PC) to access the system through an app or web browser.

[1355] Users input their travel requests in a form within the app or on a web page, such as "I'd like to take a hot spring trip next weekend. The budget should be under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." The app also simultaneously obtains real-time emotion data using the user's facial recognition camera.

[1356] Once the input is complete, the user clicks the "Send" button to send the travel request data and emotion data to the server.

[1357] 2. Server request reception and analysis

[1358] The server receives the travel request and emotion data sent by the user.

[1359] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[1360] 3. Proposal creation using generative AI and emotion engine

[1361] The generation AI analyzes the user's travel request data passed from the server and extracts the user's desired conditions.

[1362] The emotion engine analyzes the user's emotion data and evaluates the user's current mental state, for example, determining whether the user feels like relaxing or wants to be active.

[1363] The generative AI searches historical travel databases and travel information on the internet to identify the best destinations, activities, accommodations, and transportation options for the user's requirements.

[1364] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, highlighting hot springs for a user looking to relax, for example, and suggesting adventure activities for an active user.

[1365] The generated proposal is returned to the server.

[1366] 4. Server Proposal Submission

[1367] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[1368] The server transmits the proposal data to the user's terminal so that the user can view it.

[1369] 5. User Verification and Authorization

[1370] The user checks the proposal sent from the server on the terminal.

[1371] If the user is satisfied with the proposal, he clicks the "Approve" button.

[1372] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[1373] 6. Reservation arrangements by the server

[1374] The server receives an "approval" request from the user.

[1375] The server makes online reservations for accommodation and transportation suggested by the AI.

[1376] For example, access the reservation site for a certain hot spring inn and confirm your reservation.

[1377] Execute payment processing to complete Shinkansen ticket reservations.

[1378] Make sure your booking is confirmed.

[1379] 7. Notification of Final Travel Plans

[1380] The server generates a final itinerary including booking confirmation information (confirmation number, accommodation details, transportation information).

[1381] The server sends the final itinerary to the user's terminal, allowing the user to confirm the trip details.

[1382] Specific examples

[1383] For example, a user might wish to "enjoy a hot spring trip next weekend," but is stressed and seeking relaxation. Using this system, the user accesses the system using a device and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine proposes a travel plan that emphasizes relaxation, and the generation AI selects Hakone Hot Springs as the destination and suggests appropriate accommodations and transportation options. The server returns this to the user, and if the user approves, the server makes the final reservations and notifies them of the confirmed travel plan. This allows users to smoothly plan a trip that suits their emotional state.

[1384] The processing flow will be explained below.

[1385] Step 1:

[1386] Users access the system using a terminal. The user inputs a travel request (e.g., "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I want a place where I can enjoy nature."), and the device's camera captures the user's facial expressions in real time. The data (travel request and facial expression data) are sent to the server by clicking the "Send" button.

[1387] Step 2:

[1388] The server receives the travel request data and facial expression data sent by the user, analyzes the received data, converts it into an appropriate format, and prepares it for passing to the generative AI and emotion engine.

[1389] Step 3:

[1390] The server passes the user's travel request data and facial expression data to the generation AI and emotion engine. The generation AI analyzes the request data and extracts the user's desired conditions (e.g., "hot spring trip," "budget under 50,000 yen," "natural environment").

[1391] Step 4:

[1392] The emotion engine analyzes the facial expression data passed to it and assesses the user's emotional state, for example, the emotion engine may sense that the user is seeking relaxation.

[1393] Step 5:

[1394] The generative AI searches historical travel databases and online information to identify the best destinations, activities, accommodations, and transportation options for a user's needs.

[1395] Step 6:

[1396] The emotion engine customizes the suggestions made by the generative AI based on the user's emotional state. For example, if the user is looking to relax, the generative AI will highlight hot springs and relaxation programs in its suggested plans.

[1397] Step 7:

[1398] The generative AI completes a customized itinerary and sends it back to the server, with suggestions including specific destinations, accommodations, activities, and transportation details.

[1399] Step 8:

[1400] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user, and then sends the proposed data to the user's device.

[1401] Step 9:

[1402] The user checks the proposal sent from the server on the device. If the proposal is satisfactory, the user clicks the "Accept" button. If the proposal is not satisfactory, the user can change the conditions and submit the request again.

[1403] Step 10:

[1404] The server receives an "approval" request from the user, and then makes online reservations for accommodation and transportation suggested by the emotion engine and generative AI.

[1405] Step 11:

[1406] For example, the server accesses the reservation site of a certain hot spring inn and confirms the accommodation reservation.Similarly, the server executes a payment process to complete the Shinkansen ticket reservation.

[1407] Step 12:

[1408] The server confirms that the reservation is confirmed. The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device.

[1409] Step 13:

[1410] Users can check the final travel plan on their device and proceed with travel preparations, allowing them to smoothly plan a trip that suits their emotional state and desired conditions.

[1411] Example 2

[1412] 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."

[1413] Conventional travel planning systems often fail to meet user expectations because they propose travel plans without taking the user's emotional state into consideration. There is a need for a system that can efficiently book travel destinations and activities that suit individual user needs. Furthermore, there is a need for a system that can provide a more satisfying travel experience by acquiring users' emotional data in real time and reflecting it in the proposals.

[1414] 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.

[1415] In this invention, the server includes means for receiving a travel request input by a user, means for acquiring user emotion data, means for using a generation AI to suggest appropriate travel destinations, activities, accommodations, and transportation means based on the travel request, means for customizing the suggestions based on the emotion data using an emotion engine, means for booking the suggested travel destinations, activities, accommodations, and transportation means, and means for transmitting the suggestions and booking information to the user's terminal. This makes it possible to provide a personalized travel plan that takes the user's emotional state into consideration, thereby increasing user satisfaction.

[1416] 1. "User" refers to an individual who uses the System to enter a travel request and review and approve the proposed travel plan.

[1417] 2. "Device" refers to the electronic device (e.g., smartphone, tablet, PC) used by a User to enter and submit a Travel Request and receive suggested Travel Itineraries.

[1418] 3. "Server" refers to a computing device that receives requests from users, generates and provides travel plans in cooperation with the Generative AI and Emotion Engine, and processes reservations.

[1419] 4. “Generative AI” refers to artificial intelligence technology that suggests suitable travel destinations, activities, accommodations and transportation based on travel requests.

[1420] 5. "Emotion Engine" refers to a system that analyzes a user's emotional data and customizes suggestions based on the user's current emotional state.

[1421] 6. "Travel request" refers to the desired travel conditions (e.g., destination, budget, time, etc.) entered by a user into the system.

[1422] 7. “Emotional Data” means data that represents a user’s emotional state (e.g., facial expressions, voice tone, text analysis, etc.) and that is acquired by the system in real time.

[1423] 8. “Recommendations” refers to travel plans created by the Generative AI and Emotion Engine based on a user’s travel request and emotion data.

[1424] 9. "Booking" means the actual arrangement of the proposed travel destination, activity, accommodation, transportation, etc.

[1425] 10. "Travel Database" refers to a data storage facility that stores past travel information and travel-related information on the Internet.

[1426] As an embodiment of this invention, we will show a specific implementation method of a system that supports users' travel planning by utilizing generative AI and an emotion engine. This system is implemented using the following hardware and software.

[1427] Hardware used

[1428] Devices: smartphones, tablets, PCs, etc.

[1429] Server: a high-performance computing device

[1430] Software used

[1431] Generative AI models (e.g., GPT-3)

[1432] Emotion engine (e.g. Emotion API)

[1433] Frontend: App or web browser

[1434] Backend: A platform for receiving and analyzing user requests, creating and delivering proposals

[1435] Processing Description

[1436] 1. Fill out and submit your request

[1437] Users use their device to access the application or web browser and enter a travel request, such as "I'd like to take a hot spring trip next weekend, with a budget of less than 50,000 yen and somewhere where I can enjoy nature." Real-time emotional data is also provided using a facial recognition camera.

[1438] 2. Receiving and analyzing request and emotion data

[1439] The server receives the travel request and emotion data sent by the user, converts the data into an appropriate format, and passes it to the generative AI and emotion engine. For example, the server analyzes the travel request as text data and converts the emotion data into numerical data as a result of facial expression analysis.

[1440] 3. Proposal generation and customization

[1441] The generative AI analyzes the user's travel request data and extracts the user's desired conditions. The emotion engine analyzes the user's emotional data and evaluates their current mental state, for example, determining whether they want to relax or be active.

[1442] 4. Submitting and Viewing Proposals

[1443] The server converts the proposal data generated by the generative AI and emotion engine into a format that can be viewed by the user and sends it to the user's device, where the user can review the proposal and approve or change the conditions.

[1444] 5. Reservation Procedure

[1445] If the user approves the proposal, the server will make online reservations for accommodation and transportation, for example, accessing the website of a hot spring inn to confirm the reservation and reserving a Shinkansen ticket.

[1446] 6. Notification of Travel Plans

[1447] The server generates a final itinerary (including reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device, where the user can confirm the trip details.

[1448] Specific examples

[1449] For example, if a user wishes to "enjoy a hot spring trip next weekend" and is feeling stressed and seeking relaxation, they can use this system. The user accesses the system using a terminal and inputs a travel request such as, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature." At the same time, they transmit facial expression data via the camera indicating their stress level. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine creates a travel plan that emphasizes relaxation, and the generation AI suggests appropriate accommodations and transportation options with Hakone Hot Springs as the destination. The server returns this to the user, and if the user approves, it makes the final reservations and notifies them of the confirmed travel plan. An example of a prompt sentence might be, "I want to take a hot spring trip next weekend. My budget is under 50,000 yen, and I'd like to go somewhere where I can enjoy nature. I'm currently feeling stressed and want to relax."

[1450] In this way, the system allows users to smoothly plan their trip according to their emotional state.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1: User enters and submits request

[1453] Users use devices such as smartphones, tablets, or PCs to access an app or web browser and enter their travel request. For example, they might enter, "I'd like to take a trip to a hot spring next weekend. My budget is under 50,000 yen, and I'd like it to be somewhere where I can enjoy nature." At this time, real-time emotional data (facial expression data) is also acquired using the device's camera. Once the input data and emotional data are ready, the user clicks the "Send" button to send the data to the server.

[1454] Step 2: The server receives and formats the request

[1455] The server receives the travel request data and emotion data sent by the user. It analyzes the received data and converts it into an appropriate format to be passed to the generative AI and emotion engine. Specifically, it analyzes the travel request as text data and converts facial expression data into numerical data. The analyzed data is then passed to the next processing step.

[1456] Step 3: Generative AI analyzes the travel request

[1457] The generation AI extracts the user's specific desired conditions based on the travel request data passed from the server. For example, conditions such as destination, budget, and places to enjoy nature are extracted through text analysis. This provides the basic data for generating travel destination, activity, accommodation, and transportation options. The input is the travel request data, and the output is the extracted desired conditions.

[1458] Step 4: Analyze the emotional data with the emotion engine

[1459] The emotion engine analyzes the emotion data passed from the server and evaluates the user's current mental state. Specifically, it analyzes facial expression data to determine whether the user wants to relax or be active. The results of this analysis are used by the generative AI to customize suggestions. The input is emotion data, and the output is the analyzed emotional state.

[1460] Step 5: Generative AI creates proposals

[1461] The generation AI proposes specific travel destinations, activities, accommodations, and transportation methods based on the extracted user's desired conditions and the analysis results of the emotion engine. For example, it generates a plan that includes "Hakone Hot Springs as the destination, relaxing accommodation A, and scenic hiking course B." The input is the user's desired conditions and emotional state, and the output is a specific travel proposal.

[1462] Step 6: Server Sends Proposal

[1463] The server converts the generated travel proposals into a format that can be viewed by the user and sends them to the user's device. Specifically, it converts them into HTML or JSON format so that the proposals can be displayed on the device screen. The input is the travel proposal data, and the output is in a format that can be displayed on the user's screen.

[1464] Step 7: User Review and Authorization

[1465] The user reviews the submitted travel proposal on their device. If they are satisfied with the proposal, they click the "Accept" button. If they are not satisfied, they can change the conditions and submit the request again. The input is the travel proposal data, and the output is the user's approval or a new request.

[1466] Step 8: Reservations are made by the server

[1467] The server receives the user's approval request and makes online reservations for the accommodation and transportation suggested by the generation AI. For example, the user accesses a reservation website for a hot spring inn, confirms the reservation, and executes payment processing for the Shinkansen ticket. The input is the user's approval request, and the output is the reservation confirmation information.

[1468] Step 9: Finalize your travel plans

[1469] The server generates a final itinerary including the reservation confirmation information (reservation confirmation number, accommodation details, and transportation information) and sends it to the user's device. The user can then confirm the trip details. The input is the reservation confirmation information, and the output is the final itinerary.

[1470] These steps allow users to seamlessly create personalized travel plans based on their emotional state.

[1471] (Application example 2)

[1472] 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."

[1473] Conventional video streaming services suggest content based on a user's viewing history and general preferences, but they cannot suggest content that takes into account the user's real-time emotional state, resulting in low user satisfaction. In particular, there is a demand for services that can suggest relaxing content to users who are feeling stressed, and stimulating content to users who are full of energy. It is also important that the suggested content can be viewed immediately.

[1474] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a viewing request input by a user, means for using a generation AI to suggest appropriate video content based on the viewing request, means for analyzing a facial image of the user and acquiring emotional data, means for customizing the suggested video content based on the emotional data, and means for transmitting information about the suggested video content to the user's terminal. This makes it possible to suggest video content that takes into account the user's real-time emotional state, thereby increasing user satisfaction.

[1475] "User" refers to a person who utilizes the system to enter a viewing request and view suggested video content.

[1476] A "viewing request" is data about a video viewing request that a user inputs into the system, and includes the type and theme of the content they wish to view.

[1477] "Generative AI" is an artificial intelligence that uses machine learning algorithms to suggest appropriate video content based on viewing requests.

[1478] "Video content" refers to videos that are proposed to and viewed by users, and there are a variety of types, including relaxing and stimulating videos.

[1479] "Facial image analysis" refers to the process of capturing emotional data using a user's facial image and using algorithms to determine emotions.

[1480] "Emotion data" is data that represents the user's real-time emotional state and is obtained through facial image analysis.

[1481] "Customization" refers to tailoring content suggestions to a user's specific preferences and state based on captured emotional data.

[1482] A "terminal" is a device used by a user to input a viewing request or view suggested video content, and includes a smartphone, tablet, personal computer, etc.

[1483] "Suggestion" refers to the act and content of the generative AI selecting the most suitable video content based on the user's viewing request and emotional data and showing it to the user.

[1484] As an embodiment of this invention, we will explain a specific implementation method of a system that combines emotion data and viewing requests to propose optimal video content. This system consists of five main elements: a user, a terminal, a server, a generation AI, and an emotion engine.

[1485] 1. User submits request

[1486] Users use a device (e.g., a smartphone, tablet, or personal computer) to access the system through an app or web browser.

[1487] Users enter viewing requests such as "I want to watch a relaxing video" into an in-app form or on a web page.

[1488] In addition, facial images are captured in real time using the device's camera, and emotional data is simultaneously obtained.

[1489] Once you have completed the input, click the "Send" button to send the viewing request data and emotion data to the server.

[1490] 2. Server request reception and analysis

[1491] The server receives the viewing request and emotion data sent from the user.

[1492] The server converts the received data into an appropriate format and prepares it for passing to the generative AI and emotion engine.

[1493] 3. Proposal creation using generative AI and emotion engine

[1494] The generation AI analyzes the user's viewing request data passed from the server and extracts the user's desired conditions.

[1495] The emotion engine analyzes the user's facial images to obtain emotional data and evaluate their real-time emotional state, for example, determining whether they are in a relaxed mood or wanting to engage in energetic activities.

[1496] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[1497] The emotion engine further customizes the suggestions made by the generative AI based on the user's emotional state, for example highlighting calming content for a user looking to relax, or suggesting active content for an energetic user.

[1498] The generated proposal is returned to the server.

[1499] 4. Server Proposal Submission

[1500] The server converts the proposed data received from the generative AI and emotion engine into a format that can be displayed to the user.

[1501] The server transmits the proposal data to the user's terminal so that the user can view it.

[1502] 5. User Verification and Viewing

[1503] The user checks the proposal sent from the server on the terminal.

[1504] If the user is satisfied with the suggestions, they can click the "Watch" button to immediately begin watching the suggested video content.

[1505] If the user is not satisfied with the proposal, they can change the conditions and submit the request again.

[1506] Examples:

[1507] For example, if a user wishes to "watch a relaxing video" but is stressed and seeking relaxation, they can use this system. The user uses their device to access the system and input a viewing request such as "I want to watch a relaxing video," and at the same time, the camera analyzes the user's facial expressions to detect stress. The server receives this data and passes it on to the generation AI and emotion engine. The emotion engine suggests video content that emphasizes relaxation, and the generation AI presents an appropriate video. The server returns this to the user, who then begins watching.

[1508] Example prompt sentence:

[1509] "Please suggest video content that will help users relax."

[1510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1511] Step 1:

[1512] A user accesses the system through a device, an app, or a web browser. The user inputs a viewing request and simultaneously captures a facial image with the device's camera. This viewing request data and facial image data are then sent to the server.

[1513] Input: Viewing request data, facial image data

[1514] Output: Viewing request data and facial image data sent to the server

[1515] Specific operation: The user types "I want to watch a relaxing video" in the app, and the camera simultaneously captures an image of the user's face.

[1516] Step 2:

[1517] The server receives the viewing request data and facial image data sent by the user and converts the data into an appropriate format. The viewing request data is passed to the generation AI, and the facial image data is passed to the emotion engine.

[1518] Input: Viewing request data, facial image data

[1519] Output: Data formatted to feed generative AI and emotion engines

[1520] Specific operation: The server prepares to pass the viewing request data to the generation AI and the facial image data to the emotion engine.

[1521] Step 3:

[1522] The emotion engine analyzes facial image data to assess the user's real-time emotional state, for example, whether the user is stressed or wants to relax.

[1523] Input: Facial image data

[1524] Output: User's emotional state data

[1525] How it works: The emotion engine uses facial image analysis algorithms to infer emotional states (e.g., stress) from captured facial images.

[1526] Step 4:

[1527] The generation AI analyzes the viewing request data and extracts the user's desired conditions, which clarifies the content of the viewing request.

[1528] Input: Viewing request data

[1529] Output: Extracted user preference data

[1530] Specific operation: The generation AI extracts the desired conditions of the user who is looking for a "relaxing video."

[1531] Step 5:

[1532] The generative AI searches past viewing databases and video information on the Internet to identify the video content that best suits the user's requirements.

[1533] Input: User's desired conditions data

[1534] Output: Optimal video content data

[1535] How it works: The generative AI searches a database for relaxing videos and identifies several candidates.

[1536] Step 6:

[1537] The emotion engine customizes suggestions based on the user's emotional state data and the video content identified by the generative AI. For example, if the user prioritizes relaxation, the generative AI will select particularly calming videos from the suggested content.

[1538] Input: User emotional state data, video content data generated by generative AI

[1539] Output: Customized video suggestion data

[1540] Specific operation: The emotion engine prioritizes video content suitable for relaxation.

[1541] Step 7:

[1542] The server sends the customized video suggestion data received from the generative AI and emotion engine to the user's device so that the user can view it.

[1543] Input: Customized video suggestion data

[1544] Output: Video suggestion data sent to the user's device

[1545] Specific operation: The server sends video suggestion data to the user's device and displays it on the app or browser.

[1546] Step 8:

[1547] The user can review the suggested video content on their device and click the "Watch" button to begin watching. If they are not satisfied with the suggested content, they can change the conditions and submit the request again.

[1548] Input: User confirmation action

[1549] Output: Start watching or re-request

[1550] Specific Action: User clicks the "Watch" button to play the video or resubmits the request.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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).

[1558] 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.

[1559] 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."

[1560] 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.

[1561] 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).

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] The following is further disclosed regarding the above embodiment.

[1573] (Claim 1)

[1574] means for receiving a travel request input from a user;

[1575] using a generative AI to suggest suitable travel destinations, activities, accommodations, and transportation based on said travel request;

[1576] means for booking said suggested travel destinations, activities, accommodations and transportation;

[1577] means for transmitting the proposal and reservation information to a user's terminal;

[1578] A system including:

[1579] (Claim 2)

[1580] The system of claim 1, characterized in that the generation AI extracts user preferences from the travel request and customizes the proposals.

[1581] (Claim 3)

[1582] The system of claim 1, characterized in that the generating AI searches a past travel database and information on the Internet to select optimal travel destinations and activities.

[1583] "Example 1"

[1584] (Claim 1)

[1585] means for receiving a travel request input from a user;

[1586] means for converting the travel request data into JSON format and preparing it for analysis;

[1587] using a generative AI to suggest suitable travel destinations, activities, accommodations, and transportation based on said travel request;

[1588] means for booking said suggested travel destinations, activities, accommodations and transportation;

[1589] means for transmitting the proposal and reservation information to a user's terminal;

[1590] means for confirming the booking and generating a final itinerary upon receiving user approval;

[1591] means for transmitting the final itinerary to a user's terminal and notifying the user of the trip details;

[1592] A system including:

[1593] (Claim 2)

[1594] The system of claim 1, characterized in that the generation AI extracts user preferences from the travel request and customizes the proposals.

[1595] (Claim 3)

[1596] The system of claim 1, characterized in that the generating AI searches a past travel database and information on the Internet to select optimal travel destinations and activities.

[1597] "Application Example 1"

[1598] (Claim 1)

[1599] means for receiving a work request input by a user;

[1600] A means for proposing an appropriate work schedule and resource allocation based on the work requirements using a generation AI;

[1601] means for making actual arrangements based on the proposed work schedule and resource allocation;

[1602] means for transmitting the proposal and arrangement information to a user terminal;

[1603] A system including:

[1604] (Claim 2)

[1605] The system according to claim 1, characterized in that the generation AI extracts user preferences and efficient resource utilization from the work request and customizes the proposal content.

[1606] (Claim 3)

[1607] The system according to claim 1, characterized in that the generating AI searches a database of past work and information on the Internet to select the optimal work schedule and resource allocation.

[1608] "Example 2: Combining Emotion Engines"

[1609] (Claim 1)

[1610] means for receiving a travel request input from a user;

[1611] A means for acquiring user emotion data;

[1612] using a generative AI to suggest suitable travel destinations, activities, accommodations, and transportation based on said travel request;

[1613] means for customizing suggestions based on said emotion data using an emotion engine;

[1614] means for booking said suggested travel destinations, activities, accommodations and transportation;

[1615] means for transmitting the proposal and reservation information to a user's terminal;

[1616] A system including:

[1617] (Claim 2)

[1618] The system of claim 1, characterized in that the generation AI extracts user preferences from the travel request and customizes the proposals.

[1619] (Claim 3)

[1620] The system of claim 1, characterized in that the generating AI searches a past travel database and information on the Internet to select optimal travel destinations and activities.

[1621] "Application example 2 when combining emotion engines"

[1622] (Claim 1)

[1623] means for receiving a viewing request input by a user;

[1624] means for suggesting appropriate video content based on the viewing request using a generative AI;

[1625] A means for analyzing a facial image of a user and acquiring emotion data;

[1626] means for customizing video content suggestions based on the emotion data;

[1627] means for transmitting information about the proposed video content to a user terminal;

[1628] A system including:

[1629] (Claim 2)

[1630] The system of claim 1, characterized in that the generation AI extracts user preferences from the viewing request and emotional data and customizes the suggestions.

[1631] (Claim 3)

[1632] The system described in claim 1, characterized in that the generation AI searches a past viewing database and information on the Internet to select the most appropriate video content. [Explanation of symbols]

[1633] 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 receiving a travel request input from a user; using a generative AI to suggest suitable travel destinations, activities, accommodations, and transportation based on said travel request; means for booking said suggested travel destinations, activities, accommodations and transportation; means for transmitting the proposal and reservation information to a user's terminal; A system including:

2. 2. The system of claim 1, wherein the generating AI extracts user preferences from the travel request and customizes the recommendations.

3. 2. The system according to claim 1, wherein the generating AI searches a database of past travels and information on the Internet to select optimal travel destinations and activities.

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