System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a plurality of locations designated by a user; means for calculating a route based on the plurality of locations and a departure location; means for generating an optimal visiting order based on the calculation; means for acquiring a travel time and a staying time between the locations based on the visiting order; means for optimizing an entire travel plan; and means for presenting the optimized travel plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern travelers and business people need to efficiently visit multiple locations within a limited amount of time. However, finding the optimal route on their own can be tedious and time-consuming. Therefore, there is a need for a system that allows users to easily find the optimal route to efficiently visit multiple tourist spots and create a travel plan based on that route. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides the following means: a system including means for inputting multiple locations specified by a user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and stay times between each location based on the visiting order, means for optimizing the overall itinerary, and means for presenting the optimized itinerary to a user. The system may further include means for suggesting optimal transportation based on the visiting order generated by the route calculation means, and means for receiving feedback from the user and readjusting the optimized itinerary.

[0006] "User" means any individual who intends to use the System to create or use a travel plan.

[0007] "Multiple locations" refers to tourist attractions or destinations that the user wishes to visit.

[0008] "Origin" refers to the location where a User begins their journey.

[0009] "Means for calculating a route" refers to a means for deriving the optimal visiting order and travel route based on multiple input points and the starting point.

[0010] The "means for generating a visiting sequence" refers to a means for determining the order in which the user should visit the locations based on the results of the route calculation.

[0011] "Means for obtaining travel time and stay time" refers to means for calculating or obtaining the travel time between tourist spots and the planned stay time at each tourist spot.

[0012] "Means for optimizing a travel plan" refers to means for generating the most efficient plan possible within the user's required time, taking into account the calculated route, travel time, and stay time.

[0013] "Means for presenting a travel plan" means a means for visually or otherwise displaying an optimized travel plan to a user.

[0014] The "means for suggesting transportation means" refers to a means for suggesting an appropriate transportation means to a user based on the generated visiting sequence.

[0015] "Means for receiving feedback and readjusting" refers to the means for reevaluating the generated itinerary based on correction requests and feedback from users and re-optimizing it if necessary. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that proposes the optimal route for efficiently visiting multiple tourist spots specified by the user, thereby providing the maximum travel experience within a limited time.

[0038] System Configuration

[0039] The system mainly consists of the following elements:

[0040] 1. Terminal: Provides an interface for users to input information such as the departure point, tourist attractions they wish to visit, travel time, and transportation method.

[0041] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[0042] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0043] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[0044] Program processing

[0045] 1. User Input:

[0046] Terminal: The user launches the application and inputs the departure point, the tourist spots they wish to visit, the travel time, and the mode of transportation.

[0047] Terminal: Sends these input data to the server.

[0048] 2. Generate optimal route:

[0049] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[0050] Server: Calls the map API and obtains travel time between each tourist spot.

[0051] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[0052] 3. Present your travel plan:

[0053] Server: Sends the generated travel plan to the terminal.

[0054] Device: Shows the user the best travel plans.

[0055] 4. Feedback and readjustment:

[0056] Terminal: The user reviews the travel plan and requests modifications if necessary.

[0057] Server: Receive user feedback and re-optimize.

[0058] Specific example explanation

[0059] User Input Scenarios

[0060] User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0061] Departure point: Tokyo Station

[0062] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0063] Duration: 3 hours

[0064] Transportation: Taxi

[0065] Processing flow

[0066] Terminal: Receives user A's input and sends it to the server.

[0067] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0068] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[0069] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0070] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[0071] Server: Generates a travel plan and sends it to the terminal.

[0072] Device: Show user A a detailed plan like the one below.

[0073] Departure point: Tokyo Station

[0074] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[0075] Transportation between points: Taxi

[0076] Total time required: 3 hours

[0077] Examples of feedback and readjustment

[0078] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0079] Terminal: Sends a modification request to the server.

[0080] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0081] Server: Sends the new plan to the device.

[0082] Terminal: Present the revised plan to User A.

[0083] In this way, the present invention allows users to efficiently visit tourist spots within a limited time and enjoy the maximum travel experience.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] User: Starts the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation.

[0087] Step 2:

[0088] Terminal: Receives the entered data and prompts the user for confirmation via the display screen.

[0089] Step 3:

[0090] Terminal: Receives input confirmation from the user and sends the input data to the server.

[0091] Step 4:

[0092] Server: Analyzes the received data and extracts the necessary parameters (e.g., starting point, desired destinations, travel time, and mode of transportation).

[0093] Step 5:

[0094] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0095] Step 6:

[0096] Server: Calls the map API and obtains travel time between each tourist spot.

[0097] Step 7:

[0098] Server: Optimize the overall travel plan by taking into account the order of visits, means of transportation, and duration of stay.

[0099] Step 8:

[0100] Server: Sends the generated travel plan to the terminal.

[0101] Step 9:

[0102] Terminal: Analyzes travel plans and displays them in an easy-to-understand manner to the user (e.g., providing detailed plans through a GUI).

[0103] Step 10:

[0104] User: Review the proposed itinerary and request modifications if necessary.

[0105] Step 11:

[0106] Terminal: Receives the user's modification requests and sends them to the server.

[0107] Step 12:

[0108] Server: Analyzes user feedback and again leverages the generative AI model and map API to generate a revised, optimized itinerary.

[0109] Step 13:

[0110] Server: Sends the revised travel plan to the device.

[0111] Step 14:

[0112] On the device: The revised plan is displayed to the user for final confirmation.

[0113] Step 15:

[0114] User: Finalizes and approves the revised plan.

[0115] Example 1

[0116] 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."

[0117] The objective of this invention is to generate an optimal travel plan that enables a user to efficiently visit multiple tourist spots within a limited time, and to flexibly readjust the plan based on the user's feedback on the plan. Conventional systems only partially optimize the order of visits and obtain travel times, making it difficult to readjust the plan to reflect user feedback. Furthermore, many systems do not support optimization of transportation methods or stay times, making it difficult to provide the user with an optimal travel experience.

[0118] 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.

[0119] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and staying times between each location based on the visiting order, means for optimizing the overall itinerary, means for presenting the optimized itinerary to the user, and means for receiving feedback from the user and readjusting the optimized itinerary, thereby enabling the user to efficiently and flexibly enjoy an optimal travel experience visiting multiple tourist destinations.

[0120] "User" refers to any individual or organization that uses the system to generate, review and provide feedback on travel plans.

[0121] "Multiple Locations" means multiple geographic locations that a User has designated as a desired location to visit.

[0122] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to enter information and access the System.

[0123] "Means for calculating route" refers to an algorithm and processing system for calculating the optimal visiting sequence based on multiple input points and a starting point.

[0124] The term "means for generating a visiting sequence" refers to the process and technology for determining the optimal sequence for efficiently visiting designated points.

[0125] "Means for obtaining travel time and dwell time" refers to technologies and external resources (e.g., map APIs) for obtaining travel time between points and dwell time at each point.

[0126] "Means for optimizing travel plans" refers to algorithms and systems that optimize the overall travel plan according to the user's travel time and needs, taking into account the order of visits, travel time, and duration of stay.

[0127] "Means for presenting a travel plan" refers to the technology and method for displaying an optimized travel plan on a user's device.

[0128] "Means for receiving feedback" refers to the processes and techniques for receiving correction requests and suggestions from users and incorporating them into the system.

[0129] "Generative AI model" refers to machine learning models and algorithms that generate optimal visit sequences and travel plans based on information entered by users.

[0130] The present invention relates to a system that proposes an optimal route for efficiently visiting multiple tourist spots specified by a user. This system can provide the best possible travel experience within a limited time.

[0131] System Configuration

[0132] The system mainly consists of the following elements:

[0133] 1. Terminal: A device that provides an interface for users to input information such as their departure point, the tourist spots they wish to visit, travel time, and transportation method.

[0134] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[0135] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0136] 4. Map API: Refers to external resources for obtaining travel times between tourist attractions (e.g., Google Maps API).

[0137] Program processing explanation

[0138] User input

[0139] Terminal: The user launches the application and enters the starting point, the attractions they want to visit, the travel time, and the mode of transportation. This data is collected using text boxes and drop-down lists.

[0140] Terminal: Converts collected data into JSON format or similar and sends it to the server as an HTTP POST request.

[0141] Generate optimal routes

[0142] Server: Parses the received data and formats it into a data format. Stores the parsed information in an internal data structure.

[0143] Server: Calls the generative AI model based on the prepared data and calculates the order of visits. For example, it uses a function called "calculateOptimalRoute" to consider the destinations, travel time, and transportation method.

[0144] Server: Call the map API to get the travel time between each tourist spot. Use the function "getTravelTime" and pass the pair between each spot.

[0145] Server: The results of the generative AI model are combined with data from the map API to optimize the overall travel plan. The "optimizeTravelPlan" function is used to optimize the order of visits and travel time.

[0146] Presenting your travel plan

[0147] Server: Serialize the generated travel plan in JSON format and send it to the terminal as an HTTP response.

[0148] Terminal: The received travel plan is deserialized and displayed in a user interface, specifically in a visual timeline or list format.

[0149] Feedback and Recalibration

[0150] User: Review the proposed itinerary and provide feedback if any modifications are needed, such as requesting a shorter stay at Ueno Zoo.

[0151] Terminal: Send the modification request in JSON format to the server as an HTTP POST request.

[0152] Server: Receives feedback and re-optimizes the plan using the generative AI model. Recalculates and generates a new optimal travel plan.

[0153] Server: Sends the revised travel plan to the device.

[0154] Terminal: Present the revised plan to the user.

[0155] Specific example explanation

[0156] 1. User Input Scenarios

[0157] User: User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0158] Departure point: Tokyo Station

[0159] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0160] Duration: 3 hours

[0161] Transportation: Taxi

[0162] 2. Processing Flow

[0163] Terminal: Receives user A's input and sends it to the server.

[0164] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0165] Server: Calls the map API and obtains the travel time between each visited location.

[0166] For example, suppose it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0167] Server: Optimizes the overall itinerary, taking into account the order of visits, travel time, and duration of stay (e.g., Tokyo Tower 30 minutes, Ueno Zoo 60 minutes, Sensoji Temple 45 minutes).

[0168] Server: Generates a travel plan and sends it to the terminal.

[0169] Terminal: Presents a detailed plan to User A.

[0170] 3. Examples of feedback and readjustment

[0171] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0172] Terminal: Sends a modification request to the server.

[0173] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0174] Server: Sends the new plan to the device.

[0175] Terminal: Present the revised plan to User A.

[0176] Prompt Sentence Examples

[0177] "User A wants to make the most of three hours in Tokyo. Their starting point is Tokyo Station, and they want to visit Tokyo Tower, Ueno Zoo, and Sensoji Temple. They plan to travel by taxi. What is the best time to stay at each location and the best order to visit them?"

[0178] The present invention allows users to travel to tourist spots efficiently and time-effectively, and enjoy the maximum travel experience.

[0179] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0180] Step 1:

[0181] The user enters information

[0182] User: Launches the application and enters information such as departure point, desired tourist spots, travel time, and transportation method.

[0183] Terminal: After the user enters information into the input form, convert this data into JSON format.

[0184] Input: departure point, tourist attractions you want to visit, travel time, and transportation information.

[0185] Output: JSON formatted data.

[0186] Step 2:

[0187] The device sends the data to the server

[0188] Terminal: Send the generated JSON format data to the server as an HTTP POST request.

[0189] Input: JSON formatted data.

[0190] Output: HTTP POST request to the server.

[0191] Step 3:

[0192] The server receives and analyzes the data

[0193] Server: Parses the received data and converts it into the appropriate data format, specifically deserializing it and storing it in an internal data structure.

[0194] Input: JSON formatted data included in an HTTP POST request.

[0195] Output: Parsed data stored in internal data structures.

[0196] Step 4:

[0197] The server calculates the visit order using the generative AI model

[0198] Server: Based on the parsed information, the server uses a generative AI model to calculate the optimal route. The function used for this calculation is "calculateOptimalRoute".

[0199] Input: Parsed data stored in internal data structures.

[0200] Output: The optimal visit sequence.

[0201] Step 5:

[0202] The server calls the map API to obtain the travel time between each tourist spot.

[0203] Server: Calls a map API (e.g., Google Maps API) to obtain the travel time between each point. Uses the "getTravelTime" function to obtain the travel time between each point.

[0204] Input: Optimal visit sequence.

[0205] Output: Travel time between each tourist spot.

[0206] Step 6:

[0207] The server optimizes the travel plan

[0208] Server: Optimize the overall travel plan based on the results of the generative AI model and travel time data between each tourist spot. Using the function "optimizeTravelPlan", optimization is performed taking into account the order of visits, travel time, and length of stay.

[0209] Input: Optimal visit sequence and travel time data between each tourist spot.

[0210] Output: Optimized trip plan.

[0211] Step 7:

[0212] The server sends the optimized travel plan to the device.

[0213] Server: Serialize the optimized itinerary into JSON format and send it to the terminal as an HTTP response.

[0214] Input: Optimized travel plan.

[0215] Output: HTTP response to the device.

[0216] Step 8:

[0217] The device displays the travel plan to the user.

[0218] Terminal: The travel plan received from the server is deserialized and displayed in the user interface. Specific display methods include timeline and list formats.

[0219] Input: HTTP response from the server (travel itinerary).

[0220] Output: The itinerary displayed to the user.

[0221] Step 9:

[0222] Users submit feedback

[0223] User: Review the proposed travel plan and provide feedback if necessary to make any necessary corrections.

[0224] Terminal: Convert the feedback into JSON format and send it to the server as an HTTP POST request.

[0225] Input: User feedback.

[0226] Output: HTTP POST request to the server.

[0227] Step 10:

[0228] The server will readjust based on the feedback.

[0229] Server: Analyzes the received feedback and re-optimizes the plan using the generative AI model. Re-calculate and re-optimize based on the new conditions.

[0230] Input: User feedback.

[0231] Output: The re-arranged itinerary.

[0232] Step 11:

[0233] The server sends the re-arranged travel plan to the device.

[0234] Server: Serialize the re-arranged itinerary into JSON format and send it to the terminal as an HTTP response.

[0235] Input: rearranged travel plans.

[0236] Output: HTTP response to the device.

[0237] Step 12:

[0238] The device displays the re-arranged itinerary to the user.

[0239] Terminal: Deserialize the reconciled itinerary and display it in the user interface.

[0240] Input: HTTP response from the server (the rescheduled itinerary).

[0241] Output: The re-adjusted itinerary displayed to the user.

[0242] (Application example 1)

[0243] 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."

[0244] Conventional travel planning systems have difficulty proposing optimal routes that efficiently visit tourist spots specified by the user. Furthermore, they lack the functionality to respond to traffic conditions and user feedback in real time and to actually operate the optimized plan in an autonomous vehicle. As a result, they have been unable to provide efficient use of time or a comfortable travel experience.

[0245] 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.

[0246] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and stay times between each location based on the visiting order, means for optimizing the overall travel plan, means for presenting the optimized travel plan to the user, and means for optimizing the driving route of the autonomous vehicle. This makes it possible to calculate in real time the optimal route for efficiently visiting tourist spots specified by the user, thereby maximizing the travel experience.

[0247] "Means for inputting multiple user-specified locations" refers to a mechanism that allows a user to specify and input locations they wish to visit through an interface.

[0248] The "means for calculating the route" is an algorithm or program for calculating the optimal visiting order based on the input starting point and multiple locations.

[0249] The "means for generating the optimal visiting order" is a function that derives the order in which the user should visit places based on the calculated route information.

[0250] "Means for obtaining travel time and duration between each location" refers to a method of obtaining data from an API or database to obtain the travel time between the locations you wish to visit and the duration of stay at each location.

[0251] The "means for optimizing the overall travel plan" is a calculation method for making the user's travel plan most efficient, taking into account the acquired travel time and stay time.

[0252] The "means for presenting an optimized travel plan to a user" is a mechanism for displaying or notifying a user of the calculated optimal travel plan.

[0253] A "means for optimizing the driving route of an autonomous vehicle" is a function or program that sets a route so that the autonomous vehicle can travel efficiently between specified destinations based on an optimized travel plan.

[0254] This invention provides a system that allows users to efficiently create and execute travel plans. The system mainly consists of a terminal, a server, a generative AI model, and a map API.

[0255] System Configuration

[0256] 1. Terminal: A device that allows users to input information such as their departure point, the tourist spots they want to visit, the travel time, and the mode of transportation they will use. This device can be a smartphone or an infotainment system in an autonomous vehicle. This terminal provides the user interface and has the function of sending the input data to a server.

[0257] 2. Server: The server receives information from the user and generates an optimal travel plan using the generative AI model and map API. The server includes a route calculation tool, an optimization tool, and a tool to present the plan to the user.

[0258] 3. Generative AI model: Contains algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information such as the starting point, tourist attractions, travel time, and transportation method. This model is a generative AI and performs complex route calculations and optimizations.

[0259] 4. Map API: An external resource used to obtain travel times between tourist destinations, such as Google Maps API.

[0260] Program processing

[0261] 1. User Input:

[0262] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation. This input data is then sent to the server.

[0263] Example of a user: For example, a user enters the following information:

[0264] Starting point: Central Station

[0265] Places I'd like to visit: Museums, parks, shopping malls

[0266] Duration: 4 hours

[0267] Transportation: car

[0268] 2. Generate optimal route:

[0269] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[0270] Example prompt: Create an optimal route within 240 minutes to visit the following tourist attractions: museum, park, shopping mall

[0271] Server: Calls the map API to obtain travel time between each tourist spot.

[0272] 3. Optimize and present your travel plans:

[0273] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[0274] Server: Generates an optimized travel plan and sends it to the device.

[0275] Device: Shows the user the best travel plans.

[0276] For example, the generated plan will look like this:

[0277] Starting point: Central Station

[0278] Visit order: Museum (60 mins), Park (90 mins), Shopping Mall (90 mins)

[0279] Transportation: Car

[0280] Total time: 4 hours

[0281] Server roles and software used

[0282] The server has a wide range of roles. First, it receives input data from users and generates an optimal travel plan based on that data using a generative AI model. It also uses map APIs such as Google Maps API to obtain travel times and takes them into account to optimize the overall plan. The main software used is as follows:

[0283] Flask: Used as a web application framework to process HTTP requests from users.

[0284] Requests: An HTTP request library used to retrieve data from external APIs.

[0285] OpenAI API: Used as a generative AI model to perform path calculations and optimization.

[0286] Google Maps API: Used to provide map data and obtain travel times between locations.

[0287] This system allows users to efficiently travel around tourist spots using autonomous vehicles and enjoy optimal travel plans in real time.

[0288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0289] Step 1:

[0290] The user enters travel plan information into the device.

[0291] The user inputs information such as the departure point, tourist spots they want to visit, travel time, and mode of transportation via a device (smartphone or the infotainment system of the autonomous vehicle). This data is later sent to the server. As an example of input, the departure point is "Central Station," the destinations are "museums, parks, shopping malls," the travel time is "240 minutes," and the mode of transportation is "car."

[0292] Step 2:

[0293] The device sends the input data to the server

[0294] The terminal sends the data entered by the user to the server. Specifically, it sends the data to the server in JSON format using an HTTP request. The data sent includes the departure point, destinations, travel time, and transportation method.

[0295] Step 3:

[0296] The server analyzes the received data and creates a prompt for the generative AI model.

[0297] The server analyzes the received data and creates a prompt to generate the optimal visiting sequence based on the user's specified criteria. An example of a prompt might be, "Please create the optimal route within 240 minutes by visiting the following tourist attractions: museum, park, shopping mall."

[0298] Step 4:

[0299] A generative AI model calculates the optimal visit order based on the prompt.

[0300] The server sends the prompt to the generative AI model and receives a suggestion for the optimal visiting order from the model. The optimal visiting order suggested by the generative AI model is a route that efficiently visits tourist spots within a given time. For example, suppose the generative AI model calculates the order as "museum → park → shopping mall."

[0301] Step 5:

[0302] The server obtains travel time using the map API.

[0303] The server uses a map API (such as Google Maps API) to obtain the travel time between each tourist spot. Specifically, it sends an HTTP request to the map API to obtain the travel time between each point (for example, from the central station to the museum, from the museum to the park, and from the park to the shopping mall). The output may show that the travel time from the central station to the museum is 30 minutes, from the museum to the park is 20 minutes, and from the park to the shopping mall is 40 minutes.

[0304] Step 6:

[0305] The server optimizes the overall travel plan by taking into account travel time and dwell time.

[0306] The server generates an optimized itinerary by taking into account the visit order proposed by the generative AI model, the travel time for each period obtained from the map API, and the user's stay time (e.g., 60 minutes at the museum, 90 minutes at the park, 90 minutes at the shopping mall). The optimized plan includes the specific visit order, means of transportation, travel time between each location, and stay time.

[0307] Step 7:

[0308] The server sends the optimized travel plan to the device.

[0309] The server then sends the generated optimized itinerary back to the terminal as an HTTP response. This itinerary includes detailed visit order, transportation means, travel time, and duration of stay.

[0310] Step 8:

[0311] The device displays an optimized itinerary to the user

[0312] The terminal displays the optimized itinerary received from the server to the user. The user can review the displayed itinerary and send feedback to the server if necessary. For example, the user can provide feedback such as "I would like to spend less time in the park."

[0313] Step 9:

[0314] Receive user feedback and make adjustments

[0315] The server receives user feedback, reuses the generative AI model and map API to optimize a new plan, and then sends the new plan to the device, which then presents it to the user again. By repeating this cycle, the server provides the user with the most satisfying travel plan.

[0316] 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.

[0317] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[0318] System Configuration

[0319] The system mainly consists of the following elements:

[0320] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting the user's emotions.

[0321] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[0322] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0323] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[0324] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[0325] Program processing

[0326] 1. User Input:

[0327] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[0328] Terminal: Sends these input data to the server.

[0329] 2. Generate optimal route:

[0330] Server: Analyzes the received data and extracts necessary parameters (e.g., starting point, desired points to visit, travel time, mode of transportation, emotion).

[0331] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0332] Server: Calls the map API and obtains travel time between each tourist spot.

[0333] Server: Using the emotion engine, it suggests tourist spots that suit the user's emotions and adjusts the order of visits.

[0334] 3. Present your travel plan:

[0335] Server: Sends the generated travel plan to the terminal.

[0336] Device: Shows the user the best travel plans.

[0337] 4. Feedback and readjustment:

[0338] Terminal: The user reviews the travel plan and requests modifications if necessary.

[0339] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[0340] Specific example explanation

[0341] User Input Scenarios

[0342] User B wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0343] Departure point: Tokyo Station

[0344] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0345] Duration: 3 hours

[0346] Transportation: Taxi

[0347] Current Emotion: I want to relax

[0348] Processing flow

[0349] Terminal: Receives User B's input and sends it to the server.

[0350] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0351] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[0352] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0353] Server: Activates the emotion engine and adjusts the tourist spots and visit order based on User B's emotion of "wanting to relax" (e.g., prioritize quiet areas and parks).

[0354] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[0355] Server: Generates a travel plan and sends it to the terminal.

[0356] Device: Show user B a detailed plan like the one below.

[0357] Departure point: Tokyo Station

[0358] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[0359] Transportation between points: Taxi

[0360] Total time required: 3 hours

[0361] Examples of feedback and readjustment

[0362] User: User B gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0363] Terminal: Sends a modification request to the server.

[0364] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0365] Server: Sends the new plan to the device.

[0366] Device: Present the revised plan to User B.

[0367] In this way, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[0368] The processing flow will be explained below.

[0369] Step 1:

[0370] User: Launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[0371] Step 2:

[0372] Terminal: Receives the user's input and displays it to the user via a confirmation screen.

[0373] Step 3:

[0374] User: Check the input and click the send button.

[0375] Step 4:

[0376] Terminal: Sends the confirmed input data to the server.

[0377] Step 5:

[0378] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, and emotions.

[0379] Step 6:

[0380] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0381] Step 7:

[0382] Server: Calls the map API and obtains travel time between each tourist spot.

[0383] Example: Obtain data from the API such as "Tokyo Station → Tokyo Tower: 15 minutes," "Tokyo Tower → Ueno Zoo: 20 minutes," "Ueno Zoo → Sensoji Temple: 10 minutes," and "Sensoji Temple → Tokyo Station: 15 minutes."

[0384] Step 8:

[0385] Server: Activates the emotion engine and makes additional adjustments based on the user's emotions (e.g., preferring quiet places if they want to relax).

[0386] Step 9:

[0387] Server: Optimizes the overall itinerary, taking into account visit order, mode of transportation, travel time, and duration of stay.

[0388] Step 10:

[0389] Server: Generates an optimized travel plan and sends it to the terminal in JSON format, etc.

[0390] Step 11:

[0391] Terminal: Analyzes the received travel plan and displays it in an easy-to-understand manner for the user.

[0392] Example: A detailed plan such as "Tokyo Station → Tokyo Tower (stay 30 minutes) → Ueno Zoo (stay 60 minutes) → Sensoji Temple (stay 45 minutes) → Tokyo Station" is displayed through the GUI.

[0393] Step 12:

[0394] User: Review the proposed itinerary and enter feedback on the itinerary (e.g., "I would like to spend less time at Ueno Zoo").

[0395] Step 13:

[0396] Terminal: Receives user feedback and sends correction requests to the server.

[0397] Step 14:

[0398] Server: Analyzes the feedback and again leverages the generative AI model, emotion engine, and map API to generate a re-optimized itinerary.

[0399] Step 15:

[0400] Server: Sends the revised travel plan to the device.

[0401] Step 16:

[0402] On the device: The revised plan is displayed to the user for final confirmation.

[0403] Step 17:

[0404] User: Review the revised plan and approve or request further revisions.

[0405] Step 18:

[0406] Server: Determines the finalized travel plan and stores and manages all information.

[0407] Example 2

[0408] 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."

[0409] Conventional travel plan generation systems can propose the optimal route for efficiently visiting multiple tourist spots specified by the user, but they have the problem of not being able to adjust the plan to take the user's emotions into consideration. Therefore, there is a need to provide a more satisfying travel experience by adjusting the travel plan based on the user's current emotions.

[0410] 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.

[0411] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and the departure point, means for generating an optimal visiting order, and means for recognizing the user's emotions and adjusting the travel plan based on the emotions, thereby making it possible to provide an optimal travel plan that takes the user's emotions into consideration.

[0412] "Means for inputting multiple user-specified locations" refers to an interface that allows users to input information such as their departure point and tourist spots they wish to visit.

[0413] "Means for calculating a route" refers to an algorithm or program for calculating the order of visits and travel routes based on multiple input points and the starting point.

[0414] "Means for generating the optimal visiting order" refers to an algorithm or program for determining the order in which multiple tourist spots can be visited efficiently based on calculated route information.

[0415] "Means of obtaining travel time and duration between each location" refers to means of obtaining information about travel time and duration between each tourist destination using external resources or APIs.

[0416] "Means for optimizing the overall travel plan" refers to programs and algorithms that optimize the overall travel schedule by taking into account factors such as travel time between each location, length of stay, and user emotions.

[0417] "Means for presenting an optimized travel plan to a user" refers to an interface for visually displaying an optimized travel schedule to a user.

[0418] "Means for recognizing a user's emotions and adjusting the travel plan based on said emotions" refers to an algorithm or program that recognizes a user's emotions through user input, sensors, etc., and adjusts the travel plan based on those emotions.

[0419] "Means for receiving feedback and re-adjusting the optimized itinerary" refers to functionality or algorithms for receiving revision requests from users and re-optimizing the itinerary based on those requests.

[0420] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[0421] System Configuration

[0422] The system mainly consists of the following elements:

[0423] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting user emotions.

[0424] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[0425] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0426] 4. Map API: Refers to an external resource to obtain travel time between tourist spots. For example, Google Maps API is used.

[0427] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[0428] User Input

[0429] User: Starts the application and inputs the departure point, the tourist spot they want to visit, the travel time, the mode of transportation, and their current feelings. The input method is to use the form displayed on the terminal.

[0430] Processing the data

[0431] Terminal: Sends the entered data to the server. Specifically, it converts the information entered by the user into JSON format, creates an HTTP request, and sends it to the server.

[0432] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, emotions, etc. The received data is parsed and the necessary information is extracted.

[0433] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters. The generative AI model derives the optimal sequence using, for example, a machine learning algorithm.

[0434] Server: Calls the map API and obtains the travel time between each tourist spot. For example, it uses the Google Maps API to calculate the travel distance and time between tourist spots.

[0435] Server: Operates the emotion engine to suggest tourist spots that suit the user's emotions and adjust the order of visits. The emotion engine uses natural language processing and emotion analysis techniques, for example.

[0436] Travel plan generation and presentation

[0437] Server: Sends the generated travel plan to the terminal. The generated plan is converted to JSON format and sent to the terminal as an HTTP response.

[0438] Terminal: Presents the best travel plans to the user, using an interface that parses the received data and displays it visually.

[0439] User feedback and plan realignment

[0440] User: Checks travel plans and requests amendments if necessary. Amendment requests are sent from the device.

[0441] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[0442] Specific example explanation

[0443] User Input Scenarios

[0444] User: User B, who wants to make the most of his 3 hours in Tokyo, accesses the system and enters the following information:

[0445] Departure point: Tokyo Station

[0446] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0447] Duration: 3 hours

[0448] Transportation: Taxi

[0449] Current Emotion: I want to relax

[0450] Processing flow

[0451] Terminal: Receives User B's input and sends it to the server.

[0452] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0453] Server: Uses the Google Maps API to obtain travel times between each location.

[0454] For example, it takes 15 minutes to travel from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0455] Server: Activates the emotion engine and prioritizes quiet areas and parks based on the emotion of "wanting to relax."

[0456] Server: Generates an optimal travel plan taking into account visit order, travel time, and stay time.

[0457] Server: Sends the travel plan to the device.

[0458] Device: Display the following detailed plan to User B.

[0459] Departure point: Tokyo Station

[0460] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[0461] Transportation between points: Taxi

[0462] Total time required: 3 hours

[0463] User feedback and examples of rebalancing

[0464] User: Gives feedback that they would like to reduce their time spent at Ueno Zoo.

[0465] Terminal: Sends a modification request to the server.

[0466] Server: Based on feedback, reduce the visit time at Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0467] Server: Sends the new plan to the device.

[0468] Device: Present the revised plan to User B.

[0469] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[0470] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0471] Step 1: Sending user input data

[0472] User: Launches the application and inputs their departure point, the tourist attractions they want to visit, travel time, mode of transportation, and current feelings.

[0473] Input: Departure point, list of tourist attractions, travel time, transportation method, emotion

[0474] Output: Formatted input data

[0475] Terminal: Formats the information entered by the user, converts it to JSON format, creates an HTTP request and sends it to the server.

[0476] Input: User-entered data

[0477] Output: HTTP request sent to the server

[0478] Step 2: Receiving and analyzing data

[0479] Server: Analyzes the data received from the terminal, parses the JSON data, and extracts the necessary parameters.

[0480] Input: HTTP request (JSON data)

[0481] Output: starting point, list of tourist attractions, travel time, transportation method, and emotion parameters

[0482] Step 3: Calculate the optimal route

[0483] Server: Based on the extracted parameters, the generative AI model is launched and the optimal visiting order is calculated.

[0484] Input: Departure point, list of sightseeing spots, travel time, transportation method

[0485] Output: Optimal visit sequence

[0486] What it does: It uses machine learning algorithms to derive an efficient order of visits within a given timeframe.

[0487] Step 4: Obtain travel times between locations

[0488] Server: Calls a map API (e.g., Google Maps API) and obtains travel times between tourist spots.

[0489] Input: Departure point, visit order, transportation method

[0490] Output: Travel time between each point

[0491] Specific behavior: Create an API request and calculate the travel distance and time between each tourist spot.

[0492] Step 5: Emotional Adjustment

[0493] Server: Runs the emotion engine and adjusts the travel plan based on the user's emotions.

[0494] Input: Emotion data, optimal visit order, travel time

[0495] Output: Visit sequence adjusted for sentiment

[0496] What it does: Uses a sentiment analysis algorithm to reorder visits to prioritize quieter areas and relaxing tourist spots.

[0497] Step 6: Generate an optimized itinerary

[0498] Server: Optimizes the overall travel plan based on the optimal order of visits, travel time between each location, and duration of stay.

[0499] Input: adjusted visit sequence, travel time, and dwell time

[0500] Output: Optimized itinerary

[0501] Specific operation: Performs calculations recursively to generate a schedule that provides the best travel experience within the time constraints.

[0502] Step 7: Submit and view your itinerary

[0503] Server: Convert the generated travel plan into JSON format and send it to the terminal as an HTTP response.

[0504] Input: Optimized itinerary

[0505] Output: HTTP response (travel plan) sent to the device

[0506] Terminal: Parses the received data and visually displays the itinerary in a user interface.

[0507] Input: HTTP response (JSON data)

[0508] Output: The itinerary displayed to the user

[0509] Step 8: Receive feedback and readjust

[0510] User: Checks the itinerary and requests modifications if necessary. For example, feedback that they would like to spend less time at Ueno Zoo.

[0511] Input: Feedback message

[0512] Output: Modification request to terminal

[0513] Terminal: Converts the feedback into JSON format and sends it to the server.

[0514] Input: Feedback message

[0515] Output: HTTP request sent to the server

[0516] Step 9: Recalculate the plan

[0517] Server: Analyzes the feedback and re-optimizes the itinerary using an emotion engine and generative AI models.

[0518] Input: Feedback message

[0519] Output: Revised and optimized plan

[0520] What happens: Rerun the algorithm based on the new parameters to generate an optimal schedule.

[0521] Step 10: Submit and view your remediation plan

[0522] Server: Convert the revised plan into JSON format and send it back to the terminal as an HTTP response.

[0523] Input: Revised optimized plan

[0524] Output: HTTP response sent to the device

[0525] Terminal: Parses the received data and visually displays the revised itinerary to the user.

[0526] Input: HTTP response

[0527] Output: A remediation plan that is displayed to the user

[0528] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions and reflects feedback as necessary.

[0529] (Application example 2)

[0530] 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."

[0531] Conventional travel plan generation systems are limited to calculating the optimal visiting order based on multiple locations and departure points specified by the user, making it difficult to provide new travel experiences that utilize user emotions and autonomous vehicles.In addition, there was a need for a system that could generate travel plans that take user emotions into consideration, readjust plans based on feedback, calculate optimal routes using generative AI models, and obtain travel times using map APIs.

[0532] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0533] In this invention, the server includes: means for inputting multiple locations specified by the user; means for calculating a route based on the input multiple locations and a departure point; means for generating an optimal visiting order based on the calculation; means for obtaining travel times and stay times between each location based on the visiting order; means for optimizing the overall itinerary; means for presenting the optimized itinerary to the user; means for detecting the user's emotions and adjusting the itinerary based on the detected emotions; means for receiving input from a terminal via an interface installed in the autonomous vehicle; means for calculating an optimal visiting order based on the specified information using a generative AI model; and means for calling a map API and obtaining travel times between each location. This enables the generation of a flexible and optimal itinerary that reflects the user's emotions.

[0534] "Specify" means that the user inputs specific information or conditions.

[0535] "Route calculation" is the process of calculating the optimal route and visiting sequence based on multiple specified points and a starting point.

[0536] "Visit order" refers to determining the optimal order for multiple locations specified by the user.

[0537] "Travel time" refers to the time required to travel between each location.

[0538] "Dwell time" refers to the amount of time a user spends at each tourist spot or other location.

[0539] "Optimization" is the process of creating and adjusting the most efficient and effective plan by taking multiple factors into consideration.

[0540] A "trip plan" is a travel plan that includes a specified departure point, visit points, means of transportation, travel time, and duration of stay.

[0541] "Emotion detection" refers to recognizing the user's current mood or emotional state.

[0542] An "autonomous vehicle" is a vehicle that drives autonomously without human intervention.

[0543] "Terminal" refers to a device or interface through which a user can input information and receive results.

[0544] A "generative AI model" is an algorithm that uses artificial intelligence to perform necessary calculations and predictions based on specified information.

[0545] "Maps API" means an application programming interface for providing geographic information and calculating routes and travel times.

[0546] This invention is a system that provides optimal travel plans by taking into account the user's emotions. The system mainly consists of a server, a user terminal, an autonomous vehicle, a generative AI model, a map API, and an emotion engine.

[0547] Hardware and software used

[0548] Hardware: Autonomous vehicle computers, user devices (smartphones, etc.)

[0549] software:

[0550] Python: A major programming language

[0551] Flask: a web application framework

[0552] OpenAI GPT-4: Generative AI Model

[0553] Google Maps API:Map API

[0554] Microsoft Azure Emotion API: Emotion Engine

[0555] Processing flow

[0556] The server first receives data from the user's device, including the starting point, desired tourist spots, travel time, transportation method, and current emotion. Based on the user's input data, the server uses a generative AI model to calculate the optimal visiting order, calling a map API to obtain the travel time between each point.

[0557] The generated travel plan is then analyzed using an emotion engine to analyze the user's emotional information and adjust the plan as necessary. For example, if the user has the emotion "I want to relax," the system will prioritize quiet tourist spots in the itinerary.

[0558] The final itinerary is sent to the terminal and presented to the user, who can then provide feedback on the plan, which the server then uses to re-optimize the plan.

[0559] Specific examples

[0560] User A enters the following information into the system to plan a sightseeing trip in Kyoto:

[0561] Departure point: Kyoto Station

[0562] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[0563] Duration: 4 hours

[0564] Transportation: Self-driving vehicles

[0565] Current Emotion: I want to relax

[0566] Based on this information, the server generates the optimal travel route. Example prompts for the generative AI model are:

[0567] "Departure point: Kyoto Station

[0568] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[0569] Duration: 4 hours

[0570] Emotion: I want to relax.”

[0571] The generated itinerary is provided to the user through an application installed in the autonomous vehicle, allowing the user to efficiently travel around the tourist spots listed above and enjoy a satisfying sightseeing experience.

[0572] As a result, the present invention enables the generation of flexible and optimal travel plans that reflect the user's feelings.

[0573] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0574] Step 1:

[0575] The user inputs the departure point, the tourist spots they want to visit, the required travel time, the means of transportation, and their current emotions into the terminal. These inputs are sent to the server. The input data of the terminal includes the departure point, the list of tourist spots they want to visit, the required travel time, the means of transportation, and their emotions.

[0576] Step 2:

[0577] The server analyzes the received data and extracts the parameters necessary to generate a travel plan based on the departure point, desired tourist spots, travel time, transportation method, and emotions. The input data is analyzed individually and each parameter is organized as structured data.

[0578] Step 3:

[0579] The server uses a generative AI model to calculate the optimal order of visits. At this time, data from the user is input into the generative AI model as a prompt. For example, a prompt such as "Departure point: Kyoto Station; Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine; Time required: 4 hours; Emotion: I want to relax" is created. Based on this prompt, the generative AI model outputs the optimal order of visits to tourist spots.

[0580] Step 4:

[0581] The server calls the Google Maps API to obtain travel time between each tourist spot. Based on the visit order obtained from the generative AI model, travel time data for each spot is obtained from the API. For example, specific data such as the travel time from Kyoto Station to Kiyomizu-dera Temple and from Kiyomizu-dera Temple to Kinkaku-ji Temple are obtained.

[0582] Step 5:

[0583] The server uses the emotion engine to adjust the travel plan based on the user's emotions. If the user wants to relax, the emotion engine adjusts the schedule to prioritize quiet places and relaxing tourist spots. Based on the input emotion data, the server reconfigures the list and order of tourist spots.

[0584] Step 6:

[0585] The generated optimized itinerary is sent from the server to the terminal, which then displays it to the user. Details of the optimized itinerary (such as the order of visits, travel time between each point, and duration of stay) are displayed on the user's screen.

[0586] Step 7:

[0587] When a user provides feedback on a travel plan, the device sends this feedback to the server. If the user has a specific request for revision, such as "I want to reduce the time spent at Ueno Zoo," the device communicates this to the server.

[0588] Step 8:

[0589] The server receives the user's feedback and again uses the generative AI model and emotion engine to optimize the travel plan. Based on the feedback, it creates new prompts and runs the optimization loop again to generate a revised plan.

[0590] Step 9:

[0591] The server sends the revised itinerary to the terminal, which then presents it to the user again, and the details of the revised itinerary are displayed on the user's screen.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Second embodiment]

[0596] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] In the smart glasses 214, 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.

[0607] 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."

[0608] This invention is a system that proposes the optimal route for efficiently visiting multiple tourist spots specified by the user, thereby providing the maximum travel experience within a limited time.

[0609] System Configuration

[0610] The system mainly consists of the following elements:

[0611] 1. Terminal: Provides an interface for users to input information such as the departure point, tourist attractions they wish to visit, travel time, and transportation method.

[0612] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[0613] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0614] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[0615] Program processing

[0616] 1. User Input:

[0617] Terminal: The user launches the application and inputs the departure point, the tourist spots they wish to visit, the travel time, and the mode of transportation.

[0618] Terminal: Sends these input data to the server.

[0619] 2. Generate optimal route:

[0620] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[0621] Server: Calls the map API and obtains travel time between each tourist spot.

[0622] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[0623] 3. Present your travel plan:

[0624] Server: Sends the generated travel plan to the terminal.

[0625] Device: Shows the user the best travel plans.

[0626] 4. Feedback and readjustment:

[0627] Terminal: The user reviews the travel plan and requests modifications if necessary.

[0628] Server: Receive user feedback and re-optimize.

[0629] Specific example explanation

[0630] User Input Scenarios

[0631] User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0632] Departure point: Tokyo Station

[0633] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0634] Duration: 3 hours

[0635] Transportation: Taxi

[0636] Processing flow

[0637] Terminal: Receives user A's input and sends it to the server.

[0638] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0639] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[0640] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0641] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[0642] Server: Generates a travel plan and sends it to the terminal.

[0643] Device: Show user A a detailed plan like the one below.

[0644] Departure point: Tokyo Station

[0645] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[0646] Transportation between points: Taxi

[0647] Total time required: 3 hours

[0648] Examples of feedback and readjustment

[0649] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0650] Terminal: Sends a modification request to the server.

[0651] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0652] Server: Sends the new plan to the device.

[0653] Terminal: Present the revised plan to User A.

[0654] In this way, the present invention allows users to efficiently visit tourist spots within a limited time and enjoy the maximum travel experience.

[0655] The processing flow will be explained below.

[0656] Step 1:

[0657] User: Starts the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation.

[0658] Step 2:

[0659] Terminal: Receives the entered data and prompts the user for confirmation via the display screen.

[0660] Step 3:

[0661] Terminal: Receives input confirmation from the user and sends the input data to the server.

[0662] Step 4:

[0663] Server: Analyzes the received data and extracts the necessary parameters (e.g., starting point, desired destinations, travel time, and mode of transportation).

[0664] Step 5:

[0665] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0666] Step 6:

[0667] Server: Calls the map API and obtains travel time between each tourist spot.

[0668] Step 7:

[0669] Server: Optimize the overall travel plan by taking into account the order of visits, means of transportation, and duration of stay.

[0670] Step 8:

[0671] Server: Sends the generated travel plan to the terminal.

[0672] Step 9:

[0673] Terminal: Analyzes travel plans and displays them in an easy-to-understand manner to the user (e.g., providing detailed plans through a GUI).

[0674] Step 10:

[0675] User: Review the proposed itinerary and request modifications if necessary.

[0676] Step 11:

[0677] Terminal: Receives the user's modification requests and sends them to the server.

[0678] Step 12:

[0679] Server: Analyzes user feedback and again leverages the generative AI model and map API to generate a revised, optimized itinerary.

[0680] Step 13:

[0681] Server: Sends the revised travel plan to the device.

[0682] Step 14:

[0683] On the device: The revised plan is displayed to the user for final confirmation.

[0684] Step 15:

[0685] User: Finalizes and approves the revised plan.

[0686] Example 1

[0687] 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."

[0688] The objective of this invention is to generate an optimal travel plan that enables a user to efficiently visit multiple tourist spots within a limited time, and to flexibly readjust the plan based on the user's feedback on the plan. Conventional systems only partially optimize the order of visits and obtain travel times, making it difficult to readjust the plan to reflect user feedback. Furthermore, many systems do not support optimization of transportation methods or stay times, making it difficult to provide the user with an optimal travel experience.

[0689] 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.

[0690] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and staying times between each location based on the visiting order, means for optimizing the overall itinerary, means for presenting the optimized itinerary to the user, and means for receiving feedback from the user and readjusting the optimized itinerary, thereby enabling the user to efficiently and flexibly enjoy an optimal travel experience visiting multiple tourist destinations.

[0691] "User" refers to any individual or organization that uses the system to generate, review and provide feedback on travel plans.

[0692] "Multiple Locations" means multiple geographic locations that a User has designated as a desired location to visit.

[0693] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to enter information and access the System.

[0694] "Means for calculating route" refers to an algorithm and processing system for calculating the optimal visiting sequence based on multiple input points and a starting point.

[0695] The term "means for generating a visiting sequence" refers to the process and technology for determining the optimal sequence for efficiently visiting designated points.

[0696] "Means for obtaining travel time and dwell time" refers to technologies and external resources (e.g., map APIs) for obtaining travel time between points and dwell time at each point.

[0697] "Means for optimizing travel plans" refers to algorithms and systems that optimize the overall travel plan according to the user's travel time and needs, taking into account the order of visits, travel time, and duration of stay.

[0698] "Means for presenting a travel plan" refers to the technology and method for displaying an optimized travel plan on a user's device.

[0699] "Means for receiving feedback" refers to the processes and techniques for receiving correction requests and suggestions from users and incorporating them into the system.

[0700] "Generative AI model" refers to machine learning models and algorithms that generate optimal visit sequences and travel plans based on information entered by users.

[0701] The present invention relates to a system that proposes an optimal route for efficiently visiting multiple tourist spots specified by a user. This system can provide the best possible travel experience within a limited time.

[0702] System Configuration

[0703] The system mainly consists of the following elements:

[0704] 1. Terminal: A device that provides an interface for users to input information such as their departure point, the tourist spots they wish to visit, travel time, and transportation method.

[0705] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[0706] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0707] 4. Map API: Refers to external resources for obtaining travel times between tourist attractions (e.g., Google Maps API).

[0708] Program processing explanation

[0709] User input

[0710] Terminal: The user launches the application and enters the starting point, the attractions they want to visit, the travel time, and the mode of transportation. This data is collected using text boxes and drop-down lists.

[0711] Terminal: Converts collected data into JSON format or similar and sends it to the server as an HTTP POST request.

[0712] Generate optimal routes

[0713] Server: Parses the received data and formats it into a data format. Stores the parsed information in an internal data structure.

[0714] Server: Calls the generative AI model based on the prepared data and calculates the order of visits. For example, it uses a function called "calculateOptimalRoute" to consider the destinations, travel time, and transportation method.

[0715] Server: Call the map API to get the travel time between each tourist spot. Use the function "getTravelTime" and pass the pair between each spot.

[0716] Server: The results of the generative AI model are combined with data from the map API to optimize the overall travel plan. The "optimizeTravelPlan" function is used to optimize the order of visits and travel time.

[0717] Presenting your travel plan

[0718] Server: Serialize the generated travel plan in JSON format and send it to the terminal as an HTTP response.

[0719] Terminal: The received travel plan is deserialized and displayed in a user interface, specifically in a visual timeline or list format.

[0720] Feedback and Recalibration

[0721] User: Review the proposed itinerary and provide feedback if any modifications are needed, such as requesting a shorter stay at Ueno Zoo.

[0722] Terminal: Send the modification request in JSON format to the server as an HTTP POST request.

[0723] Server: Receives feedback and re-optimizes the plan using the generative AI model. Recalculates and generates a new optimal travel plan.

[0724] Server: Sends the revised travel plan to the device.

[0725] Terminal: Present the revised plan to the user.

[0726] Specific example explanation

[0727] 1. User Input Scenarios

[0728] User: User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0729] Departure point: Tokyo Station

[0730] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0731] Duration: 3 hours

[0732] Transportation: Taxi

[0733] 2. Processing Flow

[0734] Terminal: Receives user A's input and sends it to the server.

[0735] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0736] Server: Calls the map API and obtains the travel time between each visited location.

[0737] For example, suppose it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0738] Server: Optimizes the overall itinerary, taking into account the order of visits, travel time, and duration of stay (e.g., Tokyo Tower 30 minutes, Ueno Zoo 60 minutes, Sensoji Temple 45 minutes).

[0739] Server: Generates a travel plan and sends it to the terminal.

[0740] Terminal: Presents a detailed plan to User A.

[0741] 3. Examples of feedback and readjustment

[0742] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0743] Terminal: Sends a modification request to the server.

[0744] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0745] Server: Sends the new plan to the device.

[0746] Terminal: Present the revised plan to User A.

[0747] Prompt Sentence Examples

[0748] "User A wants to make the most of three hours in Tokyo. Their starting point is Tokyo Station, and they want to visit Tokyo Tower, Ueno Zoo, and Sensoji Temple. They plan to travel by taxi. What is the best time to stay at each location and the best order to visit them?"

[0749] The present invention allows users to travel to tourist spots efficiently and time-effectively, and enjoy the maximum travel experience.

[0750] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] The user enters information

[0753] User: Launches the application and enters information such as departure point, desired tourist spots, travel time, and transportation method.

[0754] Terminal: After the user enters information into the input form, convert this data into JSON format.

[0755] Input: departure point, tourist attractions you want to visit, travel time, and transportation information.

[0756] Output: JSON formatted data.

[0757] Step 2:

[0758] The device sends the data to the server

[0759] Terminal: Send the generated JSON format data to the server as an HTTP POST request.

[0760] Input: JSON formatted data.

[0761] Output: HTTP POST request to the server.

[0762] Step 3:

[0763] The server receives and analyzes the data

[0764] Server: Parses the received data and converts it into the appropriate data format, specifically deserializing it and storing it in an internal data structure.

[0765] Input: JSON formatted data included in an HTTP POST request.

[0766] Output: Parsed data stored in internal data structures.

[0767] Step 4:

[0768] The server calculates the visit order using the generative AI model

[0769] Server: Based on the parsed information, the server uses a generative AI model to calculate the optimal route. The function used for this calculation is "calculateOptimalRoute".

[0770] Input: Parsed data stored in internal data structures.

[0771] Output: The optimal visit sequence.

[0772] Step 5:

[0773] The server calls the map API to obtain the travel time between each tourist spot.

[0774] Server: Calls a map API (e.g., Google Maps API) to obtain the travel time between each point. Uses the "getTravelTime" function to obtain the travel time between each point.

[0775] Input: Optimal visit sequence.

[0776] Output: Travel time between each tourist spot.

[0777] Step 6:

[0778] The server optimizes the travel plan

[0779] Server: Optimize the overall travel plan based on the results of the generative AI model and travel time data between each tourist spot. Using the function "optimizeTravelPlan", optimization is performed taking into account the order of visits, travel time, and length of stay.

[0780] Input: Optimal visit sequence and travel time data between each tourist spot.

[0781] Output: Optimized trip plan.

[0782] Step 7:

[0783] The server sends the optimized travel plan to the device.

[0784] Server: Serialize the optimized itinerary into JSON format and send it to the terminal as an HTTP response.

[0785] Input: Optimized travel plan.

[0786] Output: HTTP response to the device.

[0787] Step 8:

[0788] The device displays the travel plan to the user.

[0789] Terminal: The travel plan received from the server is deserialized and displayed in the user interface. Specific display methods include timeline and list formats.

[0790] Input: HTTP response from the server (travel itinerary).

[0791] Output: The itinerary displayed to the user.

[0792] Step 9:

[0793] Users submit feedback

[0794] User: Review the proposed travel plan and provide feedback if necessary to make any necessary corrections.

[0795] Terminal: Convert the feedback into JSON format and send it to the server as an HTTP POST request.

[0796] Input: User feedback.

[0797] Output: HTTP POST request to the server.

[0798] Step 10:

[0799] The server will readjust based on the feedback.

[0800] Server: Analyzes the received feedback and re-optimizes the plan using the generative AI model. Re-calculate and re-optimize based on the new conditions.

[0801] Input: User feedback.

[0802] Output: The re-arranged itinerary.

[0803] Step 11:

[0804] The server sends the re-arranged travel plan to the device.

[0805] Server: Serialize the re-arranged itinerary into JSON format and send it to the terminal as an HTTP response.

[0806] Input: rearranged travel plans.

[0807] Output: HTTP response to the device.

[0808] Step 12:

[0809] The device displays the re-arranged itinerary to the user.

[0810] Terminal: Deserialize the reconciled itinerary and display it in the user interface.

[0811] Input: HTTP response from the server (the rescheduled itinerary).

[0812] Output: The re-adjusted itinerary displayed to the user.

[0813] (Application example 1)

[0814] 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."

[0815] Conventional travel planning systems have difficulty proposing optimal routes that efficiently visit tourist spots specified by the user. Furthermore, they lack the functionality to respond to traffic conditions and user feedback in real time and to actually operate the optimized plan in an autonomous vehicle. As a result, they have been unable to provide efficient use of time or a comfortable travel experience.

[0816] 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.

[0817] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and stay times between each location based on the visiting order, means for optimizing the overall travel plan, means for presenting the optimized travel plan to the user, and means for optimizing the driving route of the autonomous vehicle. This makes it possible to calculate in real time the optimal route for efficiently visiting tourist spots specified by the user, thereby maximizing the travel experience.

[0818] "Means for inputting multiple user-specified locations" refers to a mechanism that allows a user to specify and input locations they wish to visit through an interface.

[0819] The "means for calculating the route" is an algorithm or program for calculating the optimal visiting order based on the input starting point and multiple locations.

[0820] The "means for generating the optimal visiting order" is a function that derives the order in which the user should visit places based on the calculated route information.

[0821] "Means for obtaining travel time and duration between each location" refers to a method of obtaining data from an API or database to obtain the travel time between the locations you wish to visit and the duration of stay at each location.

[0822] The "means for optimizing the overall travel plan" is a calculation method for making the user's travel plan most efficient, taking into account the acquired travel time and stay time.

[0823] The "means for presenting an optimized travel plan to a user" is a mechanism for displaying or notifying a user of the calculated optimal travel plan.

[0824] A "means for optimizing the driving route of an autonomous vehicle" is a function or program that sets a route so that the autonomous vehicle can travel efficiently between specified destinations based on an optimized travel plan.

[0825] This invention provides a system that allows users to efficiently create and execute travel plans. The system mainly consists of a terminal, a server, a generative AI model, and a map API.

[0826] System Configuration

[0827] 1. Terminal: A device that allows users to input information such as their departure point, the tourist spots they want to visit, the travel time, and the mode of transportation they will use. This device can be a smartphone or an infotainment system in an autonomous vehicle. This terminal provides the user interface and has the function of sending the input data to a server.

[0828] 2. Server: The server receives information from the user and generates an optimal travel plan using the generative AI model and map API. The server includes a route calculation tool, an optimization tool, and a tool to present the plan to the user.

[0829] 3. Generative AI model: Contains algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information such as the starting point, tourist attractions, travel time, and transportation method. This model is a generative AI and performs complex route calculations and optimizations.

[0830] 4. Map API: An external resource used to obtain travel times between tourist destinations, such as Google Maps API.

[0831] Program processing

[0832] 1. User Input:

[0833] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation. This input data is then sent to the server.

[0834] Example of a user: For example, a user enters the following information:

[0835] Starting point: Central Station

[0836] Places I'd like to visit: Museums, parks, shopping malls

[0837] Duration: 4 hours

[0838] Transportation: car

[0839] 2. Generate optimal route:

[0840] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[0841] Example prompt: Create an optimal route within 240 minutes to visit the following tourist attractions: museum, park, shopping mall

[0842] Server: Calls the map API to obtain travel time between each tourist spot.

[0843] 3. Optimize and present your travel plans:

[0844] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[0845] Server: Generates an optimized travel plan and sends it to the device.

[0846] Device: Shows the user the best travel plans.

[0847] For example, the generated plan will look like this:

[0848] Starting point: Central Station

[0849] Visit order: Museum (60 mins), Park (90 mins), Shopping Mall (90 mins)

[0850] Transportation: Car

[0851] Total time: 4 hours

[0852] Server roles and software used

[0853] The server has a wide range of roles. First, it receives input data from users and generates an optimal travel plan based on that data using a generative AI model. It also uses map APIs such as Google Maps API to obtain travel times and takes them into account to optimize the overall plan. The main software used is as follows:

[0854] Flask: Used as a web application framework to process HTTP requests from users.

[0855] Requests: An HTTP request library used to retrieve data from external APIs.

[0856] OpenAI API: Used as a generative AI model to perform path calculations and optimization.

[0857] Google Maps API: Used to provide map data and obtain travel times between locations.

[0858] This system allows users to efficiently travel around tourist spots using autonomous vehicles and enjoy optimal travel plans in real time.

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] The user enters travel plan information into the device.

[0862] The user inputs information such as the departure point, tourist spots they want to visit, travel time, and mode of transportation via a device (smartphone or the infotainment system of the autonomous vehicle). This data is later sent to the server. As an example of input, the departure point is "Central Station," the destinations are "museums, parks, shopping malls," the travel time is "240 minutes," and the mode of transportation is "car."

[0863] Step 2:

[0864] The device sends the input data to the server

[0865] The terminal sends the data entered by the user to the server. Specifically, it sends the data to the server in JSON format using an HTTP request. The data sent includes the departure point, destinations, travel time, and transportation method.

[0866] Step 3:

[0867] The server analyzes the received data and creates a prompt for the generative AI model.

[0868] The server analyzes the received data and creates a prompt to generate the optimal visiting sequence based on the user's specified criteria. An example of a prompt might be, "Please create the optimal route within 240 minutes by visiting the following tourist attractions: museum, park, shopping mall."

[0869] Step 4:

[0870] A generative AI model calculates the optimal visit order based on the prompt.

[0871] The server sends the prompt to the generative AI model and receives a suggestion for the optimal visiting order from the model. The optimal visiting order suggested by the generative AI model is a route that efficiently visits tourist spots within a given time. For example, suppose the generative AI model calculates the order as "museum → park → shopping mall."

[0872] Step 5:

[0873] The server obtains travel time using the map API.

[0874] The server uses a map API (such as Google Maps API) to obtain the travel time between each tourist spot. Specifically, it sends an HTTP request to the map API to obtain the travel time between each point (for example, from the central station to the museum, from the museum to the park, and from the park to the shopping mall). The output may show that the travel time from the central station to the museum is 30 minutes, from the museum to the park is 20 minutes, and from the park to the shopping mall is 40 minutes.

[0875] Step 6:

[0876] The server optimizes the overall travel plan by taking into account travel time and dwell time.

[0877] The server generates an optimized itinerary by taking into account the visit order proposed by the generative AI model, the travel time for each period obtained from the map API, and the user's stay time (e.g., 60 minutes at the museum, 90 minutes at the park, 90 minutes at the shopping mall). The optimized plan includes the specific visit order, means of transportation, travel time between each location, and stay time.

[0878] Step 7:

[0879] The server sends the optimized travel plan to the device.

[0880] The server then sends the generated optimized itinerary back to the terminal as an HTTP response. This itinerary includes detailed visit order, transportation means, travel time, and duration of stay.

[0881] Step 8:

[0882] The device displays an optimized itinerary to the user

[0883] The terminal displays the optimized itinerary received from the server to the user. The user can review the displayed itinerary and send feedback to the server if necessary. For example, the user can provide feedback such as "I would like to spend less time in the park."

[0884] Step 9:

[0885] Receive user feedback and make adjustments

[0886] The server receives user feedback, reuses the generative AI model and map API to optimize a new plan, and then sends the new plan to the device, which then presents it to the user again. By repeating this cycle, the server provides the user with the most satisfying travel plan.

[0887] 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.

[0888] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[0889] System Configuration

[0890] The system mainly consists of the following elements:

[0891] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting the user's emotions.

[0892] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[0893] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0894] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[0895] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[0896] Program processing

[0897] 1. User Input:

[0898] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[0899] Terminal: Sends these input data to the server.

[0900] 2. Generate optimal route:

[0901] Server: Analyzes the received data and extracts necessary parameters (e.g., starting point, desired points to visit, travel time, mode of transportation, emotion).

[0902] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0903] Server: Calls the map API and obtains travel time between each tourist spot.

[0904] Server: Using the emotion engine, it suggests tourist spots that suit the user's emotions and adjusts the order of visits.

[0905] 3. Present your travel plan:

[0906] Server: Sends the generated travel plan to the terminal.

[0907] Device: Shows the user the best travel plans.

[0908] 4. Feedback and readjustment:

[0909] Terminal: The user reviews the travel plan and requests modifications if necessary.

[0910] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[0911] Specific example explanation

[0912] User Input Scenarios

[0913] User B wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[0914] Departure point: Tokyo Station

[0915] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[0916] Duration: 3 hours

[0917] Transportation: Taxi

[0918] Current Emotion: I want to relax

[0919] Processing flow

[0920] Terminal: Receives User B's input and sends it to the server.

[0921] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[0922] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[0923] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[0924] Server: Activates the emotion engine and adjusts the tourist spots and visit order based on User B's emotion of "wanting to relax" (e.g., prioritize quiet areas and parks).

[0925] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[0926] Server: Generates a travel plan and sends it to the terminal.

[0927] Device: Show user B a detailed plan like the one below.

[0928] Departure point: Tokyo Station

[0929] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[0930] Transportation between points: Taxi

[0931] Total time required: 3 hours

[0932] Examples of feedback and readjustment

[0933] User: User B gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[0934] Terminal: Sends a modification request to the server.

[0935] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[0936] Server: Sends the new plan to the device.

[0937] Device: Present the revised plan to User B.

[0938] In this way, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] User: Launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[0942] Step 2:

[0943] Terminal: Receives the user's input and displays it to the user via a confirmation screen.

[0944] Step 3:

[0945] User: Check the input and click the send button.

[0946] Step 4:

[0947] Terminal: Sends the confirmed input data to the server.

[0948] Step 5:

[0949] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, and emotions.

[0950] Step 6:

[0951] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[0952] Step 7:

[0953] Server: Calls the map API and obtains travel time between each tourist spot.

[0954] Example: Obtain data from the API such as "Tokyo Station → Tokyo Tower: 15 minutes," "Tokyo Tower → Ueno Zoo: 20 minutes," "Ueno Zoo → Sensoji Temple: 10 minutes," and "Sensoji Temple → Tokyo Station: 15 minutes."

[0955] Step 8:

[0956] Server: Activates the emotion engine and makes additional adjustments based on the user's emotions (e.g., preferring quiet places if they want to relax).

[0957] Step 9:

[0958] Server: Optimizes the overall itinerary, taking into account visit order, mode of transportation, travel time, and duration of stay.

[0959] Step 10:

[0960] Server: Generates an optimized travel plan and sends it to the terminal in JSON format, etc.

[0961] Step 11:

[0962] Terminal: Analyzes the received travel plan and displays it in an easy-to-understand manner for the user.

[0963] Example: A detailed plan such as "Tokyo Station → Tokyo Tower (stay 30 minutes) → Ueno Zoo (stay 60 minutes) → Sensoji Temple (stay 45 minutes) → Tokyo Station" is displayed through the GUI.

[0964] Step 12:

[0965] User: Review the proposed itinerary and enter feedback on the itinerary (e.g., "I would like to spend less time at Ueno Zoo").

[0966] Step 13:

[0967] Terminal: Receives user feedback and sends correction requests to the server.

[0968] Step 14:

[0969] Server: Analyzes the feedback and again leverages the generative AI model, emotion engine, and map API to generate a re-optimized itinerary.

[0970] Step 15:

[0971] Server: Sends the revised travel plan to the device.

[0972] Step 16:

[0973] On the device: The revised plan is displayed to the user for final confirmation.

[0974] Step 17:

[0975] User: Review the revised plan and approve or request further revisions.

[0976] Step 18:

[0977] Server: Determines the finalized travel plan and stores and manages all information.

[0978] Example 2

[0979] 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."

[0980] Conventional travel plan generation systems can propose the optimal route for efficiently visiting multiple tourist spots specified by the user, but they have the problem of not being able to adjust the plan to take the user's emotions into consideration. Therefore, there is a need to provide a more satisfying travel experience by adjusting the travel plan based on the user's current emotions.

[0981] 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.

[0982] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and the departure point, means for generating an optimal visiting order, and means for recognizing the user's emotions and adjusting the travel plan based on the emotions, thereby making it possible to provide an optimal travel plan that takes the user's emotions into consideration.

[0983] "Means for inputting multiple user-specified locations" refers to an interface that allows users to input information such as their departure point and tourist spots they wish to visit.

[0984] "Means for calculating a route" refers to an algorithm or program for calculating the order of visits and travel routes based on multiple input points and the starting point.

[0985] "Means for generating the optimal visiting order" refers to an algorithm or program for determining the order in which multiple tourist spots can be visited efficiently based on calculated route information.

[0986] "Means of obtaining travel time and duration between each location" refers to means of obtaining information about travel time and duration between each tourist destination using external resources or APIs.

[0987] "Means for optimizing the overall travel plan" refers to programs and algorithms that optimize the overall travel schedule by taking into account factors such as travel time between each location, length of stay, and user emotions.

[0988] "Means for presenting an optimized travel plan to a user" refers to an interface for visually displaying an optimized travel schedule to a user.

[0989] "Means for recognizing a user's emotions and adjusting the travel plan based on said emotions" refers to an algorithm or program that recognizes a user's emotions through user input, sensors, etc., and adjusts the travel plan based on those emotions.

[0990] "Means for receiving feedback and re-adjusting the optimized itinerary" refers to functionality or algorithms for receiving revision requests from users and re-optimizing the itinerary based on those requests.

[0991] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[0992] System Configuration

[0993] The system mainly consists of the following elements:

[0994] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting user emotions.

[0995] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[0996] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[0997] 4. Map API: Refers to an external resource to obtain travel time between tourist spots. For example, Google Maps API is used.

[0998] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[0999] User Input

[1000] User: Starts the application and inputs the departure point, the tourist spot they want to visit, the travel time, the mode of transportation, and their current feelings. The input method is to use the form displayed on the terminal.

[1001] Processing the data

[1002] Terminal: Sends the entered data to the server. Specifically, it converts the information entered by the user into JSON format, creates an HTTP request, and sends it to the server.

[1003] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, emotions, etc. The received data is parsed and the necessary information is extracted.

[1004] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters. The generative AI model derives the optimal sequence using, for example, a machine learning algorithm.

[1005] Server: Calls the map API and obtains the travel time between each tourist spot. For example, it uses the Google Maps API to calculate the travel distance and time between tourist spots.

[1006] Server: Operates the emotion engine to suggest tourist spots that suit the user's emotions and adjust the order of visits. The emotion engine uses natural language processing and emotion analysis techniques, for example.

[1007] Travel plan generation and presentation

[1008] Server: Sends the generated travel plan to the terminal. The generated plan is converted to JSON format and sent to the terminal as an HTTP response.

[1009] Terminal: Presents the best travel plans to the user, using an interface that parses the received data and displays it visually.

[1010] User feedback and plan realignment

[1011] User: Checks travel plans and requests amendments if necessary. Amendment requests are sent from the device.

[1012] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[1013] Specific example explanation

[1014] User Input Scenarios

[1015] User: User B, who wants to make the most of his 3 hours in Tokyo, accesses the system and enters the following information:

[1016] Departure point: Tokyo Station

[1017] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1018] Duration: 3 hours

[1019] Transportation: Taxi

[1020] Current Emotion: I want to relax

[1021] Processing flow

[1022] Terminal: Receives User B's input and sends it to the server.

[1023] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1024] Server: Uses the Google Maps API to obtain travel times between each location.

[1025] For example, it takes 15 minutes to travel from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1026] Server: Activates the emotion engine and prioritizes quiet areas and parks based on the emotion of "wanting to relax."

[1027] Server: Generates an optimal travel plan taking into account visit order, travel time, and stay time.

[1028] Server: Sends the travel plan to the device.

[1029] Device: Display the following detailed plan to User B.

[1030] Departure point: Tokyo Station

[1031] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[1032] Transportation between points: Taxi

[1033] Total time required: 3 hours

[1034] User feedback and examples of rebalancing

[1035] User: Gives feedback that they would like to reduce their time spent at Ueno Zoo.

[1036] Terminal: Sends a modification request to the server.

[1037] Server: Based on feedback, reduce the visit time at Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1038] Server: Sends the new plan to the device.

[1039] Device: Present the revised plan to User B.

[1040] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] Step 1: Sending user input data

[1043] User: Launches the application and inputs their departure point, the tourist attractions they want to visit, travel time, mode of transportation, and current feelings.

[1044] Input: Departure point, list of tourist attractions, travel time, transportation method, emotion

[1045] Output: Formatted input data

[1046] Terminal: Formats the information entered by the user, converts it to JSON format, creates an HTTP request and sends it to the server.

[1047] Input: User-entered data

[1048] Output: HTTP request sent to the server

[1049] Step 2: Receiving and analyzing data

[1050] Server: Analyzes the data received from the terminal, parses the JSON data, and extracts the necessary parameters.

[1051] Input: HTTP request (JSON data)

[1052] Output: starting point, list of tourist attractions, travel time, transportation method, and emotion parameters

[1053] Step 3: Calculate the optimal route

[1054] Server: Based on the extracted parameters, the generative AI model is launched and the optimal visiting order is calculated.

[1055] Input: Departure point, list of sightseeing spots, travel time, transportation method

[1056] Output: Optimal visit sequence

[1057] What it does: It uses machine learning algorithms to derive an efficient order of visits within a given timeframe.

[1058] Step 4: Obtain travel times between locations

[1059] Server: Calls a map API (e.g., Google Maps API) and obtains travel times between tourist spots.

[1060] Input: Departure point, visit order, transportation method

[1061] Output: Travel time between each point

[1062] Specific behavior: Create an API request and calculate the travel distance and time between each tourist spot.

[1063] Step 5: Emotional Adjustment

[1064] Server: Runs the emotion engine and adjusts the travel plan based on the user's emotions.

[1065] Input: Emotion data, optimal visit order, travel time

[1066] Output: Visit sequence adjusted for sentiment

[1067] What it does: Uses a sentiment analysis algorithm to reorder visits to prioritize quieter areas and relaxing tourist spots.

[1068] Step 6: Generate an optimized itinerary

[1069] Server: Optimizes the overall travel plan based on the optimal order of visits, travel time between each location, and duration of stay.

[1070] Input: adjusted visit sequence, travel time, and dwell time

[1071] Output: Optimized itinerary

[1072] Specific operation: Performs calculations recursively to generate a schedule that provides the best travel experience within the time constraints.

[1073] Step 7: Submit and view your itinerary

[1074] Server: Convert the generated travel plan into JSON format and send it to the terminal as an HTTP response.

[1075] Input: Optimized itinerary

[1076] Output: HTTP response (travel plan) sent to the device

[1077] Terminal: Parses the received data and visually displays the itinerary in a user interface.

[1078] Input: HTTP response (JSON data)

[1079] Output: The itinerary displayed to the user

[1080] Step 8: Receive feedback and readjust

[1081] User: Checks the itinerary and requests modifications if necessary. For example, feedback that they would like to spend less time at Ueno Zoo.

[1082] Input: Feedback message

[1083] Output: Modification request to terminal

[1084] Terminal: Converts the feedback into JSON format and sends it to the server.

[1085] Input: Feedback message

[1086] Output: HTTP request sent to the server

[1087] Step 9: Recalculate the plan

[1088] Server: Analyzes the feedback and re-optimizes the itinerary using an emotion engine and generative AI models.

[1089] Input: Feedback message

[1090] Output: Revised and optimized plan

[1091] What happens: Rerun the algorithm based on the new parameters to generate an optimal schedule.

[1092] Step 10: Submit and view your remediation plan

[1093] Server: Convert the revised plan into JSON format and send it back to the terminal as an HTTP response.

[1094] Input: Revised optimized plan

[1095] Output: HTTP response sent to the device

[1096] Terminal: Parses the received data and visually displays the revised itinerary to the user.

[1097] Input: HTTP response

[1098] Output: A remediation plan that is displayed to the user

[1099] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions and reflects feedback as necessary.

[1100] (Application example 2)

[1101] 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."

[1102] Conventional travel plan generation systems are limited to calculating the optimal visiting order based on multiple locations and departure points specified by the user, making it difficult to provide new travel experiences that utilize user emotions and autonomous vehicles.In addition, there was a need for a system that could generate travel plans that take user emotions into consideration, readjust plans based on feedback, calculate optimal routes using generative AI models, and obtain travel times using map APIs.

[1103] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1104] In this invention, the server includes: means for inputting multiple locations specified by the user; means for calculating a route based on the input multiple locations and a departure point; means for generating an optimal visiting order based on the calculation; means for obtaining travel times and stay times between each location based on the visiting order; means for optimizing the overall itinerary; means for presenting the optimized itinerary to the user; means for detecting the user's emotions and adjusting the itinerary based on the detected emotions; means for receiving input from a terminal via an interface installed in the autonomous vehicle; means for calculating an optimal visiting order based on the specified information using a generative AI model; and means for calling a map API and obtaining travel times between each location. This enables the generation of a flexible and optimal itinerary that reflects the user's emotions.

[1105] "Specify" means that the user inputs specific information or conditions.

[1106] "Route calculation" is the process of calculating the optimal route and visiting sequence based on multiple specified points and a starting point.

[1107] "Visit order" refers to determining the optimal order for multiple locations specified by the user.

[1108] "Travel time" refers to the time required to travel between each location.

[1109] "Dwell time" refers to the amount of time a user spends at each tourist spot or other location.

[1110] "Optimization" is the process of creating and adjusting the most efficient and effective plan by taking multiple factors into consideration.

[1111] A "trip plan" is a travel plan that includes a specified departure point, visit points, means of transportation, travel time, and duration of stay.

[1112] "Emotion detection" refers to recognizing the user's current mood or emotional state.

[1113] An "autonomous vehicle" is a vehicle that drives autonomously without human intervention.

[1114] "Terminal" refers to a device or interface through which a user can input information and receive results.

[1115] A "generative AI model" is an algorithm that uses artificial intelligence to perform necessary calculations and predictions based on specified information.

[1116] "Maps API" means an application programming interface for providing geographic information and calculating routes and travel times.

[1117] This invention is a system that provides optimal travel plans by taking into account the user's emotions. The system mainly consists of a server, a user terminal, an autonomous vehicle, a generative AI model, a map API, and an emotion engine.

[1118] Hardware and software used

[1119] Hardware: Autonomous vehicle computers, user devices (smartphones, etc.)

[1120] software:

[1121] Python: A major programming language

[1122] Flask: a web application framework

[1123] OpenAI GPT-4: Generative AI Model

[1124] Google Maps API:Map API

[1125] Microsoft Azure Emotion API: Emotion Engine

[1126] Processing flow

[1127] The server first receives data from the user's device, including the starting point, desired tourist spots, travel time, transportation method, and current emotion. Based on the user's input data, the server uses a generative AI model to calculate the optimal visiting order, calling a map API to obtain the travel time between each point.

[1128] The generated travel plan is then analyzed using an emotion engine to analyze the user's emotional information and adjust the plan as necessary. For example, if the user has the emotion "I want to relax," the system will prioritize quiet tourist spots in the itinerary.

[1129] The final itinerary is sent to the terminal and presented to the user, who can then provide feedback on the plan, which the server then uses to re-optimize the plan.

[1130] Specific examples

[1131] User A enters the following information into the system to plan a sightseeing trip in Kyoto:

[1132] Departure point: Kyoto Station

[1133] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[1134] Duration: 4 hours

[1135] Transportation: Self-driving vehicles

[1136] Current Emotion: I want to relax

[1137] Based on this information, the server generates the optimal travel route. Example prompts for the generative AI model are:

[1138] "Departure point: Kyoto Station

[1139] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[1140] Duration: 4 hours

[1141] Emotion: I want to relax.”

[1142] The generated itinerary is provided to the user through an application installed in the autonomous vehicle, allowing the user to efficiently travel around the tourist spots listed above and enjoy a satisfying sightseeing experience.

[1143] As a result, the present invention enables the generation of flexible and optimal travel plans that reflect the user's feelings.

[1144] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1145] Step 1:

[1146] The user inputs the departure point, the tourist spots they want to visit, the required travel time, the means of transportation, and their current emotions into the terminal. These inputs are sent to the server. The input data of the terminal includes the departure point, the list of tourist spots they want to visit, the required travel time, the means of transportation, and their emotions.

[1147] Step 2:

[1148] The server analyzes the received data and extracts the parameters necessary to generate a travel plan based on the departure point, desired tourist spots, travel time, transportation method, and emotions. The input data is analyzed individually and each parameter is organized as structured data.

[1149] Step 3:

[1150] The server uses a generative AI model to calculate the optimal order of visits. At this time, data from the user is input into the generative AI model as a prompt. For example, a prompt such as "Departure point: Kyoto Station; Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine; Time required: 4 hours; Emotion: I want to relax" is created. Based on this prompt, the generative AI model outputs the optimal order of visits to tourist spots.

[1151] Step 4:

[1152] The server calls the Google Maps API to obtain travel time between each tourist spot. Based on the visit order obtained from the generative AI model, travel time data for each spot is obtained from the API. For example, specific data such as the travel time from Kyoto Station to Kiyomizu-dera Temple and from Kiyomizu-dera Temple to Kinkaku-ji Temple are obtained.

[1153] Step 5:

[1154] The server uses the emotion engine to adjust the travel plan based on the user's emotions. If the user wants to relax, the emotion engine adjusts the schedule to prioritize quiet places and relaxing tourist spots. Based on the input emotion data, the server reconfigures the list and order of tourist spots.

[1155] Step 6:

[1156] The generated optimized itinerary is sent from the server to the terminal, which then displays it to the user. Details of the optimized itinerary (such as the order of visits, travel time between each point, and duration of stay) are displayed on the user's screen.

[1157] Step 7:

[1158] When a user provides feedback on a travel plan, the device sends this feedback to the server. If the user has a specific request for revision, such as "I want to reduce the time spent at Ueno Zoo," the device communicates this to the server.

[1159] Step 8:

[1160] The server receives the user's feedback and again uses the generative AI model and emotion engine to optimize the travel plan. Based on the feedback, it creates new prompts and runs the optimization loop again to generate a revised plan.

[1161] Step 9:

[1162] The server sends the revised itinerary to the terminal, which then presents it to the user again, and the details of the revised itinerary are displayed on the user's screen.

[1163] 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.

[1164] 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.

[1165] 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.

[1166] [Third embodiment]

[1167] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1168] 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.

[1169] 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).

[1170] 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.

[1171] 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.

[1172] 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).

[1173] 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.

[1174] 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.

[1175] 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.

[1176] 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.

[1177] 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.

[1178] 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."

[1179] This invention is a system that proposes the optimal route for efficiently visiting multiple tourist spots specified by the user, thereby providing the maximum travel experience within a limited time.

[1180] System Configuration

[1181] The system mainly consists of the following elements:

[1182] 1. Terminal: Provides an interface for users to input information such as the departure point, tourist attractions they wish to visit, travel time, and transportation method.

[1183] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[1184] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1185] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[1186] Program processing

[1187] 1. User Input:

[1188] Terminal: The user launches the application and inputs the departure point, the tourist spots they wish to visit, the travel time, and the mode of transportation.

[1189] Terminal: Sends these input data to the server.

[1190] 2. Generate optimal route:

[1191] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[1192] Server: Calls the map API and obtains travel time between each tourist spot.

[1193] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[1194] 3. Present your travel plan:

[1195] Server: Sends the generated travel plan to the terminal.

[1196] Device: Shows the user the best travel plans.

[1197] 4. Feedback and readjustment:

[1198] Terminal: The user reviews the travel plan and requests modifications if necessary.

[1199] Server: Receive user feedback and re-optimize.

[1200] Specific example explanation

[1201] User Input Scenarios

[1202] User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[1203] Departure point: Tokyo Station

[1204] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1205] Duration: 3 hours

[1206] Transportation: Taxi

[1207] Processing flow

[1208] Terminal: Receives user A's input and sends it to the server.

[1209] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1210] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[1211] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1212] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[1213] Server: Generates a travel plan and sends it to the terminal.

[1214] Device: Show user A a detailed plan like the one below.

[1215] Departure point: Tokyo Station

[1216] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[1217] Transportation between points: Taxi

[1218] Total time required: 3 hours

[1219] Examples of feedback and readjustment

[1220] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[1221] Terminal: Sends a modification request to the server.

[1222] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1223] Server: Sends the new plan to the device.

[1224] Terminal: Present the revised plan to User A.

[1225] In this way, the present invention allows users to efficiently visit tourist spots within a limited time and enjoy the maximum travel experience.

[1226] The processing flow will be explained below.

[1227] Step 1:

[1228] User: Starts the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation.

[1229] Step 2:

[1230] Terminal: Receives the entered data and prompts the user for confirmation via the display screen.

[1231] Step 3:

[1232] Terminal: Receives input confirmation from the user and sends the input data to the server.

[1233] Step 4:

[1234] Server: Analyzes the received data and extracts the necessary parameters (e.g., starting point, desired destinations, travel time, and mode of transportation).

[1235] Step 5:

[1236] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[1237] Step 6:

[1238] Server: Calls the map API and obtains travel time between each tourist spot.

[1239] Step 7:

[1240] Server: Optimize the overall travel plan by taking into account the order of visits, means of transportation, and duration of stay.

[1241] Step 8:

[1242] Server: Sends the generated travel plan to the terminal.

[1243] Step 9:

[1244] Terminal: Analyzes travel plans and displays them in an easy-to-understand manner to the user (e.g., providing detailed plans through a GUI).

[1245] Step 10:

[1246] User: Review the proposed itinerary and request modifications if necessary.

[1247] Step 11:

[1248] Terminal: Receives the user's modification requests and sends them to the server.

[1249] Step 12:

[1250] Server: Analyzes user feedback and again leverages the generative AI model and map API to generate a revised, optimized itinerary.

[1251] Step 13:

[1252] Server: Sends the revised travel plan to the device.

[1253] Step 14:

[1254] On the device: The revised plan is displayed to the user for final confirmation.

[1255] Step 15:

[1256] User: Finalizes and approves the revised plan.

[1257] Example 1

[1258] 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."

[1259] The objective of this invention is to generate an optimal travel plan that enables a user to efficiently visit multiple tourist spots within a limited time, and to flexibly readjust the plan based on the user's feedback on the plan. Conventional systems only partially optimize the order of visits and obtain travel times, making it difficult to readjust the plan to reflect user feedback. Furthermore, many systems do not support optimization of transportation methods or stay times, making it difficult to provide the user with an optimal travel experience.

[1260] 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.

[1261] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and staying times between each location based on the visiting order, means for optimizing the overall itinerary, means for presenting the optimized itinerary to the user, and means for receiving feedback from the user and readjusting the optimized itinerary, thereby enabling the user to efficiently and flexibly enjoy an optimal travel experience visiting multiple tourist destinations.

[1262] "User" refers to any individual or organization that uses the system to generate, review and provide feedback on travel plans.

[1263] "Multiple Locations" means multiple geographic locations that a User has designated as a desired location to visit.

[1264] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to enter information and access the System.

[1265] "Means for calculating route" refers to an algorithm and processing system for calculating the optimal visiting sequence based on multiple input points and a starting point.

[1266] The term "means for generating a visiting sequence" refers to the process and technology for determining the optimal sequence for efficiently visiting designated points.

[1267] "Means for obtaining travel time and dwell time" refers to technologies and external resources (e.g., map APIs) for obtaining travel time between points and dwell time at each point.

[1268] "Means for optimizing travel plans" refers to algorithms and systems that optimize the overall travel plan according to the user's travel time and needs, taking into account the order of visits, travel time, and duration of stay.

[1269] "Means for presenting a travel plan" refers to the technology and method for displaying an optimized travel plan on a user's device.

[1270] "Means for receiving feedback" refers to the processes and techniques for receiving correction requests and suggestions from users and incorporating them into the system.

[1271] "Generative AI model" refers to machine learning models and algorithms that generate optimal visit sequences and travel plans based on information entered by users.

[1272] The present invention relates to a system that proposes an optimal route for efficiently visiting multiple tourist spots specified by a user. This system can provide the best possible travel experience within a limited time.

[1273] System Configuration

[1274] The system mainly consists of the following elements:

[1275] 1. Terminal: A device that provides an interface for users to input information such as their departure point, the tourist spots they wish to visit, travel time, and transportation method.

[1276] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[1277] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1278] 4. Map API: Refers to external resources for obtaining travel times between tourist attractions (e.g., Google Maps API).

[1279] Program processing explanation

[1280] User input

[1281] Terminal: The user launches the application and enters the starting point, the attractions they want to visit, the travel time, and the mode of transportation. This data is collected using text boxes and drop-down lists.

[1282] Terminal: Converts collected data into JSON format or similar and sends it to the server as an HTTP POST request.

[1283] Generate optimal routes

[1284] Server: Parses the received data and formats it into a data format. Stores the parsed information in an internal data structure.

[1285] Server: Calls the generative AI model based on the prepared data and calculates the order of visits. For example, it uses a function called "calculateOptimalRoute" to consider the destinations, travel time, and transportation method.

[1286] Server: Call the map API to get the travel time between each tourist spot. Use the function "getTravelTime" and pass the pair between each spot.

[1287] Server: The results of the generative AI model are combined with data from the map API to optimize the overall travel plan. The "optimizeTravelPlan" function is used to optimize the order of visits and travel time.

[1288] Presenting your travel plan

[1289] Server: Serialize the generated travel plan in JSON format and send it to the terminal as an HTTP response.

[1290] Terminal: The received travel plan is deserialized and displayed in a user interface, specifically in a visual timeline or list format.

[1291] Feedback and Recalibration

[1292] User: Review the proposed itinerary and provide feedback if any modifications are needed, such as requesting a shorter stay at Ueno Zoo.

[1293] Terminal: Send the modification request in JSON format to the server as an HTTP POST request.

[1294] Server: Receives feedback and re-optimizes the plan using the generative AI model. Recalculates and generates a new optimal travel plan.

[1295] Server: Sends the revised travel plan to the device.

[1296] Terminal: Present the revised plan to the user.

[1297] Specific example explanation

[1298] 1. User Input Scenarios

[1299] User: User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[1300] Departure point: Tokyo Station

[1301] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1302] Duration: 3 hours

[1303] Transportation: Taxi

[1304] 2. Processing Flow

[1305] Terminal: Receives user A's input and sends it to the server.

[1306] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1307] Server: Calls the map API and obtains the travel time between each visited location.

[1308] For example, suppose it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1309] Server: Optimizes the overall itinerary, taking into account the order of visits, travel time, and duration of stay (e.g., Tokyo Tower 30 minutes, Ueno Zoo 60 minutes, Sensoji Temple 45 minutes).

[1310] Server: Generates a travel plan and sends it to the terminal.

[1311] Terminal: Presents a detailed plan to User A.

[1312] 3. Examples of feedback and readjustment

[1313] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[1314] Terminal: Sends a modification request to the server.

[1315] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1316] Server: Sends the new plan to the device.

[1317] Terminal: Present the revised plan to User A.

[1318] Prompt Sentence Examples

[1319] "User A wants to make the most of three hours in Tokyo. Their starting point is Tokyo Station, and they want to visit Tokyo Tower, Ueno Zoo, and Sensoji Temple. They plan to travel by taxi. What is the best time to stay at each location and the best order to visit them?"

[1320] The present invention allows users to travel to tourist spots efficiently and time-effectively, and enjoy the maximum travel experience.

[1321] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1322] Step 1:

[1323] The user enters information

[1324] User: Launches the application and enters information such as departure point, desired tourist spots, travel time, and transportation method.

[1325] Terminal: After the user enters information into the input form, convert this data into JSON format.

[1326] Input: departure point, tourist attractions you want to visit, travel time, and transportation information.

[1327] Output: JSON formatted data.

[1328] Step 2:

[1329] The device sends the data to the server

[1330] Terminal: Send the generated JSON format data to the server as an HTTP POST request.

[1331] Input: JSON formatted data.

[1332] Output: HTTP POST request to the server.

[1333] Step 3:

[1334] The server receives and analyzes the data

[1335] Server: Parses the received data and converts it into the appropriate data format, specifically deserializing it and storing it in an internal data structure.

[1336] Input: JSON formatted data included in an HTTP POST request.

[1337] Output: Parsed data stored in internal data structures.

[1338] Step 4:

[1339] The server calculates the visit order using the generative AI model

[1340] Server: Based on the parsed information, the server uses a generative AI model to calculate the optimal route. The function used for this calculation is "calculateOptimalRoute".

[1341] Input: Parsed data stored in internal data structures.

[1342] Output: The optimal visit sequence.

[1343] Step 5:

[1344] The server calls the map API to obtain the travel time between each tourist spot.

[1345] Server: Calls a map API (e.g., Google Maps API) to obtain the travel time between each point. Uses the "getTravelTime" function to obtain the travel time between each point.

[1346] Input: Optimal visit sequence.

[1347] Output: Travel time between each tourist spot.

[1348] Step 6:

[1349] The server optimizes the travel plan

[1350] Server: Optimize the overall travel plan based on the results of the generative AI model and travel time data between each tourist spot. Using the function "optimizeTravelPlan", optimization is performed taking into account the order of visits, travel time, and length of stay.

[1351] Input: Optimal visit sequence and travel time data between each tourist spot.

[1352] Output: Optimized trip plan.

[1353] Step 7:

[1354] The server sends the optimized travel plan to the device.

[1355] Server: Serialize the optimized itinerary into JSON format and send it to the terminal as an HTTP response.

[1356] Input: Optimized travel plan.

[1357] Output: HTTP response to the device.

[1358] Step 8:

[1359] The device displays the travel plan to the user.

[1360] Terminal: The travel plan received from the server is deserialized and displayed in the user interface. Specific display methods include timeline and list formats.

[1361] Input: HTTP response from the server (travel itinerary).

[1362] Output: The itinerary displayed to the user.

[1363] Step 9:

[1364] Users submit feedback

[1365] User: Review the proposed travel plan and provide feedback if necessary to make any necessary corrections.

[1366] Terminal: Convert the feedback into JSON format and send it to the server as an HTTP POST request.

[1367] Input: User feedback.

[1368] Output: HTTP POST request to the server.

[1369] Step 10:

[1370] The server will readjust based on the feedback.

[1371] Server: Analyzes the received feedback and re-optimizes the plan using the generative AI model. Re-calculate and re-optimize based on the new conditions.

[1372] Input: User feedback.

[1373] Output: The re-arranged itinerary.

[1374] Step 11:

[1375] The server sends the re-arranged travel plan to the device.

[1376] Server: Serialize the re-arranged itinerary into JSON format and send it to the terminal as an HTTP response.

[1377] Input: rearranged travel plans.

[1378] Output: HTTP response to the device.

[1379] Step 12:

[1380] The device displays the re-arranged itinerary to the user.

[1381] Terminal: Deserialize the reconciled itinerary and display it in the user interface.

[1382] Input: HTTP response from the server (the rescheduled itinerary).

[1383] Output: The re-adjusted itinerary displayed to the user.

[1384] (Application example 1)

[1385] 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."

[1386] Conventional travel planning systems have difficulty proposing optimal routes that efficiently visit tourist spots specified by the user. Furthermore, they lack the functionality to respond to traffic conditions and user feedback in real time and to actually operate the optimized plan in an autonomous vehicle. As a result, they have been unable to provide efficient use of time or a comfortable travel experience.

[1387] 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.

[1388] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and stay times between each location based on the visiting order, means for optimizing the overall travel plan, means for presenting the optimized travel plan to the user, and means for optimizing the driving route of the autonomous vehicle. This makes it possible to calculate in real time the optimal route for efficiently visiting tourist spots specified by the user, thereby maximizing the travel experience.

[1389] "Means for inputting multiple user-specified locations" refers to a mechanism that allows a user to specify and input locations they wish to visit through an interface.

[1390] The "means for calculating the route" is an algorithm or program for calculating the optimal visiting order based on the input starting point and multiple locations.

[1391] The "means for generating the optimal visiting order" is a function that derives the order in which the user should visit places based on the calculated route information.

[1392] "Means for obtaining travel time and duration between each location" refers to a method of obtaining data from an API or database to obtain the travel time between the locations you wish to visit and the duration of stay at each location.

[1393] The "means for optimizing the overall travel plan" is a calculation method for making the user's travel plan most efficient, taking into account the acquired travel time and stay time.

[1394] The "means for presenting an optimized travel plan to a user" is a mechanism for displaying or notifying a user of the calculated optimal travel plan.

[1395] A "means for optimizing the driving route of an autonomous vehicle" is a function or program that sets a route so that the autonomous vehicle can travel efficiently between specified destinations based on an optimized travel plan.

[1396] This invention provides a system that allows users to efficiently create and execute travel plans. The system mainly consists of a terminal, a server, a generative AI model, and a map API.

[1397] System Configuration

[1398] 1. Terminal: A device that allows users to input information such as their departure point, the tourist spots they want to visit, the travel time, and the mode of transportation they will use. This device can be a smartphone or an infotainment system in an autonomous vehicle. This terminal provides the user interface and has the function of sending the input data to a server.

[1399] 2. Server: The server receives information from the user and generates an optimal travel plan using the generative AI model and map API. The server includes a route calculation tool, an optimization tool, and a tool to present the plan to the user.

[1400] 3. Generative AI model: Contains algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information such as the starting point, tourist attractions, travel time, and transportation method. This model is a generative AI and performs complex route calculations and optimizations.

[1401] 4. Map API: An external resource used to obtain travel times between tourist destinations, such as Google Maps API.

[1402] Program processing

[1403] 1. User Input:

[1404] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation. This input data is then sent to the server.

[1405] Example of a user: For example, a user enters the following information:

[1406] Starting point: Central Station

[1407] Places I'd like to visit: Museums, parks, shopping malls

[1408] Duration: 4 hours

[1409] Transportation: car

[1410] 2. Generate optimal route:

[1411] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[1412] Example prompt: Create an optimal route within 240 minutes to visit the following tourist attractions: museum, park, shopping mall

[1413] Server: Calls the map API to obtain travel time between each tourist spot.

[1414] 3. Optimize and present your travel plans:

[1415] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[1416] Server: Generates an optimized travel plan and sends it to the device.

[1417] Device: Shows the user the best travel plans.

[1418] For example, the generated plan will look like this:

[1419] Starting point: Central Station

[1420] Visit order: Museum (60 mins), Park (90 mins), Shopping Mall (90 mins)

[1421] Transportation: Car

[1422] Total time: 4 hours

[1423] Server roles and software used

[1424] The server has a wide range of roles. First, it receives input data from users and generates an optimal travel plan based on that data using a generative AI model. It also uses map APIs such as Google Maps API to obtain travel times and takes them into account to optimize the overall plan. The main software used is as follows:

[1425] Flask: Used as a web application framework to process HTTP requests from users.

[1426] Requests: An HTTP request library used to retrieve data from external APIs.

[1427] OpenAI API: Used as a generative AI model to perform path calculations and optimization.

[1428] Google Maps API: Used to provide map data and obtain travel times between locations.

[1429] This system allows users to efficiently travel around tourist spots using autonomous vehicles and enjoy optimal travel plans in real time.

[1430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1431] Step 1:

[1432] The user enters travel plan information into the device.

[1433] The user inputs information such as the departure point, tourist spots they want to visit, travel time, and mode of transportation via a device (smartphone or the infotainment system of the autonomous vehicle). This data is later sent to the server. As an example of input, the departure point is "Central Station," the destinations are "museums, parks, shopping malls," the travel time is "240 minutes," and the mode of transportation is "car."

[1434] Step 2:

[1435] The device sends the input data to the server

[1436] The terminal sends the data entered by the user to the server. Specifically, it sends the data to the server in JSON format using an HTTP request. The data sent includes the departure point, destinations, travel time, and transportation method.

[1437] Step 3:

[1438] The server analyzes the received data and creates a prompt for the generative AI model.

[1439] The server analyzes the received data and creates a prompt to generate the optimal visiting sequence based on the user's specified criteria. An example of a prompt might be, "Please create the optimal route within 240 minutes by visiting the following tourist attractions: museum, park, shopping mall."

[1440] Step 4:

[1441] A generative AI model calculates the optimal visit order based on the prompt.

[1442] The server sends the prompt to the generative AI model and receives a suggestion for the optimal visiting order from the model. The optimal visiting order suggested by the generative AI model is a route that efficiently visits tourist spots within a given time. For example, suppose the generative AI model calculates the order as "museum → park → shopping mall."

[1443] Step 5:

[1444] The server obtains travel time using the map API.

[1445] The server uses a map API (such as Google Maps API) to obtain the travel time between each tourist spot. Specifically, it sends an HTTP request to the map API to obtain the travel time between each point (for example, from the central station to the museum, from the museum to the park, and from the park to the shopping mall). The output may show that the travel time from the central station to the museum is 30 minutes, from the museum to the park is 20 minutes, and from the park to the shopping mall is 40 minutes.

[1446] Step 6:

[1447] The server optimizes the overall travel plan by taking into account travel time and dwell time.

[1448] The server generates an optimized itinerary by taking into account the visit order proposed by the generative AI model, the travel time for each period obtained from the map API, and the user's stay time (e.g., 60 minutes at the museum, 90 minutes at the park, 90 minutes at the shopping mall). The optimized plan includes the specific visit order, means of transportation, travel time between each location, and stay time.

[1449] Step 7:

[1450] The server sends the optimized travel plan to the device.

[1451] The server then sends the generated optimized itinerary back to the terminal as an HTTP response. This itinerary includes detailed visit order, transportation means, travel time, and duration of stay.

[1452] Step 8:

[1453] The device displays an optimized itinerary to the user

[1454] The terminal displays the optimized itinerary received from the server to the user. The user can review the displayed itinerary and send feedback to the server if necessary. For example, the user can provide feedback such as "I would like to spend less time in the park."

[1455] Step 9:

[1456] Receive user feedback and make adjustments

[1457] The server receives user feedback, reuses the generative AI model and map API to optimize a new plan, and then sends the new plan to the device, which then presents it to the user again. By repeating this cycle, the server provides the user with the most satisfying travel plan.

[1458] 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.

[1459] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[1460] System Configuration

[1461] The system mainly consists of the following elements:

[1462] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting the user's emotions.

[1463] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[1464] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1465] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[1466] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[1467] Program processing

[1468] 1. User Input:

[1469] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[1470] Terminal: Sends these input data to the server.

[1471] 2. Generate optimal route:

[1472] Server: Analyzes the received data and extracts necessary parameters (e.g., starting point, desired points to visit, travel time, mode of transportation, emotion).

[1473] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[1474] Server: Calls the map API and obtains travel time between each tourist spot.

[1475] Server: Using the emotion engine, it suggests tourist spots that suit the user's emotions and adjusts the order of visits.

[1476] 3. Present your travel plan:

[1477] Server: Sends the generated travel plan to the terminal.

[1478] Device: Shows the user the best travel plans.

[1479] 4. Feedback and readjustment:

[1480] Terminal: The user reviews the travel plan and requests modifications if necessary.

[1481] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[1482] Specific example explanation

[1483] User Input Scenarios

[1484] User B wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[1485] Departure point: Tokyo Station

[1486] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1487] Duration: 3 hours

[1488] Transportation: Taxi

[1489] Current Emotion: I want to relax

[1490] Processing flow

[1491] Terminal: Receives User B's input and sends it to the server.

[1492] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1493] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[1494] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1495] Server: Activates the emotion engine and adjusts the tourist spots and visit order based on User B's emotion of "wanting to relax" (e.g., prioritize quiet areas and parks).

[1496] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[1497] Server: Generates a travel plan and sends it to the terminal.

[1498] Device: Show user B a detailed plan like the one below.

[1499] Departure point: Tokyo Station

[1500] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[1501] Transportation between points: Taxi

[1502] Total time required: 3 hours

[1503] Examples of feedback and readjustment

[1504] User: User B gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[1505] Terminal: Sends a modification request to the server.

[1506] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1507] Server: Sends the new plan to the device.

[1508] Device: Present the revised plan to User B.

[1509] In this way, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[1510] The processing flow will be explained below.

[1511] Step 1:

[1512] User: Launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[1513] Step 2:

[1514] Terminal: Receives the user's input and displays it to the user via a confirmation screen.

[1515] Step 3:

[1516] User: Check the input and click the send button.

[1517] Step 4:

[1518] Terminal: Sends the confirmed input data to the server.

[1519] Step 5:

[1520] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, and emotions.

[1521] Step 6:

[1522] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[1523] Step 7:

[1524] Server: Calls the map API and obtains travel time between each tourist spot.

[1525] Example: Obtain data from the API such as "Tokyo Station → Tokyo Tower: 15 minutes," "Tokyo Tower → Ueno Zoo: 20 minutes," "Ueno Zoo → Sensoji Temple: 10 minutes," and "Sensoji Temple → Tokyo Station: 15 minutes."

[1526] Step 8:

[1527] Server: Activates the emotion engine and makes additional adjustments based on the user's emotions (e.g., preferring quiet places if they want to relax).

[1528] Step 9:

[1529] Server: Optimizes the overall itinerary, taking into account visit order, mode of transportation, travel time, and duration of stay.

[1530] Step 10:

[1531] Server: Generates an optimized travel plan and sends it to the terminal in JSON format, etc.

[1532] Step 11:

[1533] Terminal: Analyzes the received travel plan and displays it in an easy-to-understand manner for the user.

[1534] Example: A detailed plan such as "Tokyo Station → Tokyo Tower (stay 30 minutes) → Ueno Zoo (stay 60 minutes) → Sensoji Temple (stay 45 minutes) → Tokyo Station" is displayed through the GUI.

[1535] Step 12:

[1536] User: Review the proposed itinerary and enter feedback on the itinerary (e.g., "I would like to spend less time at Ueno Zoo").

[1537] Step 13:

[1538] Terminal: Receives user feedback and sends correction requests to the server.

[1539] Step 14:

[1540] Server: Analyzes the feedback and again leverages the generative AI model, emotion engine, and map API to generate a re-optimized itinerary.

[1541] Step 15:

[1542] Server: Sends the revised travel plan to the device.

[1543] Step 16:

[1544] On the device: The revised plan is displayed to the user for final confirmation.

[1545] Step 17:

[1546] User: Review the revised plan and approve or request further revisions.

[1547] Step 18:

[1548] Server: Determines the finalized travel plan and stores and manages all information.

[1549] Example 2

[1550] 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."

[1551] Conventional travel plan generation systems can propose the optimal route for efficiently visiting multiple tourist spots specified by the user, but they have the problem of not being able to adjust the plan to take the user's emotions into consideration. Therefore, there is a need to provide a more satisfying travel experience by adjusting the travel plan based on the user's current emotions.

[1552] 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.

[1553] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and the departure point, means for generating an optimal visiting order, and means for recognizing the user's emotions and adjusting the travel plan based on the emotions, thereby making it possible to provide an optimal travel plan that takes the user's emotions into consideration.

[1554] "Means for inputting multiple user-specified locations" refers to an interface that allows users to input information such as their departure point and tourist spots they wish to visit.

[1555] "Means for calculating a route" refers to an algorithm or program for calculating the order of visits and travel routes based on multiple input points and the starting point.

[1556] "Means for generating the optimal visiting order" refers to an algorithm or program for determining the order in which multiple tourist spots can be visited efficiently based on calculated route information.

[1557] "Means of obtaining travel time and duration between each location" refers to means of obtaining information about travel time and duration between each tourist destination using external resources or APIs.

[1558] "Means for optimizing the overall travel plan" refers to programs and algorithms that optimize the overall travel schedule by taking into account factors such as travel time between each location, length of stay, and user emotions.

[1559] "Means for presenting an optimized travel plan to a user" refers to an interface for visually displaying an optimized travel schedule to a user.

[1560] "Means for recognizing a user's emotions and adjusting the travel plan based on said emotions" refers to an algorithm or program that recognizes a user's emotions through user input, sensors, etc., and adjusts the travel plan based on those emotions.

[1561] "Means for receiving feedback and re-adjusting the optimized itinerary" refers to functionality or algorithms for receiving revision requests from users and re-optimizing the itinerary based on those requests.

[1562] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[1563] System Configuration

[1564] The system mainly consists of the following elements:

[1565] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting user emotions.

[1566] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[1567] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1568] 4. Map API: Refers to an external resource to obtain travel time between tourist spots. For example, Google Maps API is used.

[1569] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[1570] User Input

[1571] User: Starts the application and inputs the departure point, the tourist spot they want to visit, the travel time, the mode of transportation, and their current feelings. The input method is to use the form displayed on the terminal.

[1572] Processing the data

[1573] Terminal: Sends the entered data to the server. Specifically, it converts the information entered by the user into JSON format, creates an HTTP request, and sends it to the server.

[1574] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, emotions, etc. The received data is parsed and the necessary information is extracted.

[1575] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters. The generative AI model derives the optimal sequence using, for example, a machine learning algorithm.

[1576] Server: Calls the map API and obtains the travel time between each tourist spot. For example, it uses the Google Maps API to calculate the travel distance and time between tourist spots.

[1577] Server: Operates the emotion engine to suggest tourist spots that suit the user's emotions and adjust the order of visits. The emotion engine uses natural language processing and emotion analysis techniques, for example.

[1578] Travel plan generation and presentation

[1579] Server: Sends the generated travel plan to the terminal. The generated plan is converted to JSON format and sent to the terminal as an HTTP response.

[1580] Terminal: Presents the best travel plans to the user, using an interface that parses the received data and displays it visually.

[1581] User feedback and plan realignment

[1582] User: Checks travel plans and requests amendments if necessary. Amendment requests are sent from the device.

[1583] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[1584] Specific example explanation

[1585] User Input Scenarios

[1586] User: User B, who wants to make the most of his 3 hours in Tokyo, accesses the system and enters the following information:

[1587] Departure point: Tokyo Station

[1588] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1589] Duration: 3 hours

[1590] Transportation: Taxi

[1591] Current Emotion: I want to relax

[1592] Processing flow

[1593] Terminal: Receives User B's input and sends it to the server.

[1594] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1595] Server: Uses the Google Maps API to obtain travel times between each location.

[1596] For example, it takes 15 minutes to travel from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1597] Server: Activates the emotion engine and prioritizes quiet areas and parks based on the emotion of "wanting to relax."

[1598] Server: Generates an optimal travel plan taking into account visit order, travel time, and stay time.

[1599] Server: Sends the travel plan to the device.

[1600] Device: Display the following detailed plan to User B.

[1601] Departure point: Tokyo Station

[1602] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[1603] Transportation between points: Taxi

[1604] Total time required: 3 hours

[1605] User feedback and examples of rebalancing

[1606] User: Gives feedback that they would like to reduce their time spent at Ueno Zoo.

[1607] Terminal: Sends a modification request to the server.

[1608] Server: Based on feedback, reduce the visit time at Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1609] Server: Sends the new plan to the device.

[1610] Device: Present the revised plan to User B.

[1611] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[1612] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1613] Step 1: Sending user input data

[1614] User: Launches the application and inputs their departure point, the tourist attractions they want to visit, travel time, mode of transportation, and current feelings.

[1615] Input: Departure point, list of tourist attractions, travel time, transportation method, emotion

[1616] Output: Formatted input data

[1617] Terminal: Formats the information entered by the user, converts it to JSON format, creates an HTTP request and sends it to the server.

[1618] Input: User-entered data

[1619] Output: HTTP request sent to the server

[1620] Step 2: Receiving and analyzing data

[1621] Server: Analyzes the data received from the terminal, parses the JSON data, and extracts the necessary parameters.

[1622] Input: HTTP request (JSON data)

[1623] Output: starting point, list of tourist attractions, travel time, transportation method, and emotion parameters

[1624] Step 3: Calculate the optimal route

[1625] Server: Based on the extracted parameters, the generative AI model is launched and the optimal visiting order is calculated.

[1626] Input: Departure point, list of sightseeing spots, travel time, transportation method

[1627] Output: Optimal visit sequence

[1628] What it does: It uses machine learning algorithms to derive an efficient order of visits within a given timeframe.

[1629] Step 4: Obtain travel times between locations

[1630] Server: Calls a map API (e.g., Google Maps API) and obtains travel times between tourist spots.

[1631] Input: Departure point, visit order, transportation method

[1632] Output: Travel time between each point

[1633] Specific behavior: Create an API request and calculate the travel distance and time between each tourist spot.

[1634] Step 5: Emotional Adjustment

[1635] Server: Runs the emotion engine and adjusts the travel plan based on the user's emotions.

[1636] Input: Emotion data, optimal visit order, travel time

[1637] Output: Visit sequence adjusted for sentiment

[1638] What it does: Uses a sentiment analysis algorithm to reorder visits to prioritize quieter areas and relaxing tourist spots.

[1639] Step 6: Generate an optimized itinerary

[1640] Server: Optimizes the overall travel plan based on the optimal order of visits, travel time between each location, and duration of stay.

[1641] Input: adjusted visit sequence, travel time, and dwell time

[1642] Output: Optimized itinerary

[1643] Specific operation: Performs calculations recursively to generate a schedule that provides the best travel experience within the time constraints.

[1644] Step 7: Submit and view your itinerary

[1645] Server: Convert the generated travel plan into JSON format and send it to the terminal as an HTTP response.

[1646] Input: Optimized itinerary

[1647] Output: HTTP response (travel plan) sent to the device

[1648] Terminal: Parses the received data and visually displays the itinerary in a user interface.

[1649] Input: HTTP response (JSON data)

[1650] Output: The itinerary displayed to the user

[1651] Step 8: Receive feedback and readjust

[1652] User: Checks the itinerary and requests modifications if necessary. For example, feedback that they would like to spend less time at Ueno Zoo.

[1653] Input: Feedback message

[1654] Output: Modification request to terminal

[1655] Terminal: Converts the feedback into JSON format and sends it to the server.

[1656] Input: Feedback message

[1657] Output: HTTP request sent to the server

[1658] Step 9: Recalculate the plan

[1659] Server: Analyzes the feedback and re-optimizes the itinerary using an emotion engine and generative AI models.

[1660] Input: Feedback message

[1661] Output: Revised and optimized plan

[1662] What happens: Rerun the algorithm based on the new parameters to generate an optimal schedule.

[1663] Step 10: Submit and view your remediation plan

[1664] Server: Convert the revised plan into JSON format and send it back to the terminal as an HTTP response.

[1665] Input: Revised optimized plan

[1666] Output: HTTP response sent to the device

[1667] Terminal: Parses the received data and visually displays the revised itinerary to the user.

[1668] Input: HTTP response

[1669] Output: A remediation plan that is displayed to the user

[1670] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions and reflects feedback as necessary.

[1671] (Application example 2)

[1672] 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."

[1673] Conventional travel plan generation systems are limited to calculating the optimal visiting order based on multiple locations and departure points specified by the user, making it difficult to provide new travel experiences that utilize user emotions and autonomous vehicles.In addition, there was a need for a system that could generate travel plans that take user emotions into consideration, readjust plans based on feedback, calculate optimal routes using generative AI models, and obtain travel times using map APIs.

[1674] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1675] In this invention, the server includes: means for inputting multiple locations specified by the user; means for calculating a route based on the input multiple locations and a departure point; means for generating an optimal visiting order based on the calculation; means for obtaining travel times and stay times between each location based on the visiting order; means for optimizing the overall itinerary; means for presenting the optimized itinerary to the user; means for detecting the user's emotions and adjusting the itinerary based on the detected emotions; means for receiving input from a terminal via an interface installed in the autonomous vehicle; means for calculating an optimal visiting order based on the specified information using a generative AI model; and means for calling a map API and obtaining travel times between each location. This enables the generation of a flexible and optimal itinerary that reflects the user's emotions.

[1676] "Specify" means that the user inputs specific information or conditions.

[1677] "Route calculation" is the process of calculating the optimal route and visiting sequence based on multiple specified points and a starting point.

[1678] "Visit order" refers to determining the optimal order for multiple locations specified by the user.

[1679] "Travel time" refers to the time required to travel between each location.

[1680] "Dwell time" refers to the amount of time a user spends at each tourist spot or other location.

[1681] "Optimization" is the process of creating and adjusting the most efficient and effective plan by taking multiple factors into consideration.

[1682] A "trip plan" is a travel plan that includes a specified departure point, visit points, means of transportation, travel time, and duration of stay.

[1683] "Emotion detection" refers to recognizing the user's current mood or emotional state.

[1684] An "autonomous vehicle" is a vehicle that drives autonomously without human intervention.

[1685] "Terminal" refers to a device or interface through which a user can input information and receive results.

[1686] A "generative AI model" is an algorithm that uses artificial intelligence to perform necessary calculations and predictions based on specified information.

[1687] "Maps API" means an application programming interface for providing geographic information and calculating routes and travel times.

[1688] This invention is a system that provides optimal travel plans by taking into account the user's emotions. The system mainly consists of a server, a user terminal, an autonomous vehicle, a generative AI model, a map API, and an emotion engine.

[1689] Hardware and software used

[1690] Hardware: Autonomous vehicle computers, user devices (smartphones, etc.)

[1691] software:

[1692] Python: A major programming language

[1693] Flask: a web application framework

[1694] OpenAI GPT-4: Generative AI Model

[1695] Google Maps API:Map API

[1696] Microsoft Azure Emotion API: Emotion Engine

[1697] Processing flow

[1698] The server first receives data from the user's device, including the starting point, desired tourist spots, travel time, transportation method, and current emotion. Based on the user's input data, the server uses a generative AI model to calculate the optimal visiting order, calling a map API to obtain the travel time between each point.

[1699] The generated travel plan is then analyzed using an emotion engine to analyze the user's emotional information and adjust the plan as necessary. For example, if the user has the emotion "I want to relax," the system will prioritize quiet tourist spots in the itinerary.

[1700] The final itinerary is sent to the terminal and presented to the user, who can then provide feedback on the plan, which the server then uses to re-optimize the plan.

[1701] Specific examples

[1702] User A enters the following information into the system to plan a sightseeing trip in Kyoto:

[1703] Departure point: Kyoto Station

[1704] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[1705] Duration: 4 hours

[1706] Transportation: Self-driving vehicles

[1707] Current Emotion: I want to relax

[1708] Based on this information, the server generates the optimal travel route. Example prompts for the generative AI model are:

[1709] "Departure point: Kyoto Station

[1710] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[1711] Duration: 4 hours

[1712] Emotion: I want to relax.”

[1713] The generated itinerary is provided to the user through an application installed in the autonomous vehicle, allowing the user to efficiently travel around the tourist spots listed above and enjoy a satisfying sightseeing experience.

[1714] As a result, the present invention enables the generation of flexible and optimal travel plans that reflect the user's feelings.

[1715] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1716] Step 1:

[1717] The user inputs the departure point, the tourist spots they want to visit, the required travel time, the means of transportation, and their current emotions into the terminal. These inputs are sent to the server. The input data of the terminal includes the departure point, the list of tourist spots they want to visit, the required travel time, the means of transportation, and their emotions.

[1718] Step 2:

[1719] The server analyzes the received data and extracts the parameters necessary to generate a travel plan based on the departure point, desired tourist spots, travel time, transportation method, and emotions. The input data is analyzed individually and each parameter is organized as structured data.

[1720] Step 3:

[1721] The server uses a generative AI model to calculate the optimal order of visits. At this time, data from the user is input into the generative AI model as a prompt. For example, a prompt such as "Departure point: Kyoto Station; Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine; Time required: 4 hours; Emotion: I want to relax" is created. Based on this prompt, the generative AI model outputs the optimal order of visits to tourist spots.

[1722] Step 4:

[1723] The server calls the Google Maps API to obtain travel time between each tourist spot. Based on the visit order obtained from the generative AI model, travel time data for each spot is obtained from the API. For example, specific data such as the travel time from Kyoto Station to Kiyomizu-dera Temple and from Kiyomizu-dera Temple to Kinkaku-ji Temple are obtained.

[1724] Step 5:

[1725] The server uses the emotion engine to adjust the travel plan based on the user's emotions. If the user wants to relax, the emotion engine adjusts the schedule to prioritize quiet places and relaxing tourist spots. Based on the input emotion data, the server reconfigures the list and order of tourist spots.

[1726] Step 6:

[1727] The generated optimized itinerary is sent from the server to the terminal, which then displays it to the user. Details of the optimized itinerary (such as the order of visits, travel time between each point, and duration of stay) are displayed on the user's screen.

[1728] Step 7:

[1729] When a user provides feedback on a travel plan, the device sends this feedback to the server. If the user has a specific request for revision, such as "I want to reduce the time spent at Ueno Zoo," the device communicates this to the server.

[1730] Step 8:

[1731] The server receives the user's feedback and again uses the generative AI model and emotion engine to optimize the travel plan. Based on the feedback, it creates new prompts and runs the optimization loop again to generate a revised plan.

[1732] Step 9:

[1733] The server sends the revised itinerary to the terminal, which then presents it to the user again, and the details of the revised itinerary are displayed on the user's screen.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] [Fourth embodiment]

[1738] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1739] 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.

[1740] 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).

[1741] 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.

[1742] 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.

[1743] 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).

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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."

[1751] This invention is a system that proposes the optimal route for efficiently visiting multiple tourist spots specified by the user, thereby providing the maximum travel experience within a limited time.

[1752] System Configuration

[1753] The system mainly consists of the following elements:

[1754] 1. Terminal: Provides an interface for users to input information such as the departure point, tourist attractions they wish to visit, travel time, and transportation method.

[1755] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[1756] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1757] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[1758] Program processing

[1759] 1. User Input:

[1760] Terminal: The user launches the application and inputs the departure point, the tourist spots they wish to visit, the travel time, and the mode of transportation.

[1761] Terminal: Sends these input data to the server.

[1762] 2. Generate optimal route:

[1763] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[1764] Server: Calls the map API and obtains travel time between each tourist spot.

[1765] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[1766] 3. Present your travel plan:

[1767] Server: Sends the generated travel plan to the terminal.

[1768] Device: Shows the user the best travel plans.

[1769] 4. Feedback and readjustment:

[1770] Terminal: The user reviews the travel plan and requests modifications if necessary.

[1771] Server: Receive user feedback and re-optimize.

[1772] Specific example explanation

[1773] User Input Scenarios

[1774] User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[1775] Departure point: Tokyo Station

[1776] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1777] Duration: 3 hours

[1778] Transportation: Taxi

[1779] Processing flow

[1780] Terminal: Receives user A's input and sends it to the server.

[1781] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1782] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[1783] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1784] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[1785] Server: Generates a travel plan and sends it to the terminal.

[1786] Device: Show user A a detailed plan like the one below.

[1787] Departure point: Tokyo Station

[1788] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[1789] Transportation between points: Taxi

[1790] Total time required: 3 hours

[1791] Examples of feedback and readjustment

[1792] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[1793] Terminal: Sends a modification request to the server.

[1794] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1795] Server: Sends the new plan to the device.

[1796] Terminal: Present the revised plan to User A.

[1797] In this way, the present invention allows users to efficiently visit tourist spots within a limited time and enjoy the maximum travel experience.

[1798] The processing flow will be explained below.

[1799] Step 1:

[1800] User: Starts the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation.

[1801] Step 2:

[1802] Terminal: Receives the entered data and prompts the user for confirmation via the display screen.

[1803] Step 3:

[1804] Terminal: Receives input confirmation from the user and sends the input data to the server.

[1805] Step 4:

[1806] Server: Analyzes the received data and extracts the necessary parameters (e.g., starting point, desired destinations, travel time, and mode of transportation).

[1807] Step 5:

[1808] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[1809] Step 6:

[1810] Server: Calls the map API and obtains travel time between each tourist spot.

[1811] Step 7:

[1812] Server: Optimize the overall travel plan by taking into account the order of visits, means of transportation, and duration of stay.

[1813] Step 8:

[1814] Server: Sends the generated travel plan to the terminal.

[1815] Step 9:

[1816] Terminal: Analyzes travel plans and displays them in an easy-to-understand manner to the user (e.g., providing detailed plans through a GUI).

[1817] Step 10:

[1818] User: Review the proposed itinerary and request modifications if necessary.

[1819] Step 11:

[1820] Terminal: Receives the user's modification requests and sends them to the server.

[1821] Step 12:

[1822] Server: Analyzes user feedback and again leverages the generative AI model and map API to generate a revised, optimized itinerary.

[1823] Step 13:

[1824] Server: Sends the revised travel plan to the device.

[1825] Step 14:

[1826] On the device: The revised plan is displayed to the user for final confirmation.

[1827] Step 15:

[1828] User: Finalizes and approves the revised plan.

[1829] Example 1

[1830] 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."

[1831] The objective of this invention is to generate an optimal travel plan that enables a user to efficiently visit multiple tourist spots within a limited time, and to flexibly readjust the plan based on the user's feedback on the plan. Conventional systems only partially optimize the order of visits and obtain travel times, making it difficult to readjust the plan to reflect user feedback. Furthermore, many systems do not support optimization of transportation methods or stay times, making it difficult to provide the user with an optimal travel experience.

[1832] 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.

[1833] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and staying times between each location based on the visiting order, means for optimizing the overall itinerary, means for presenting the optimized itinerary to the user, and means for receiving feedback from the user and readjusting the optimized itinerary, thereby enabling the user to efficiently and flexibly enjoy an optimal travel experience visiting multiple tourist destinations.

[1834] "User" refers to any individual or organization that uses the system to generate, review and provide feedback on travel plans.

[1835] "Multiple Locations" means multiple geographic locations that a User has designated as a desired location to visit.

[1836] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to enter information and access the System.

[1837] "Means for calculating route" refers to an algorithm and processing system for calculating the optimal visiting sequence based on multiple input points and a starting point.

[1838] The term "means for generating a visiting sequence" refers to the process and technology for determining the optimal sequence for efficiently visiting designated points.

[1839] "Means for obtaining travel time and dwell time" refers to technologies and external resources (e.g., map APIs) for obtaining travel time between points and dwell time at each point.

[1840] "Means for optimizing travel plans" refers to algorithms and systems that optimize the overall travel plan according to the user's travel time and needs, taking into account the order of visits, travel time, and duration of stay.

[1841] "Means for presenting a travel plan" refers to the technology and method for displaying an optimized travel plan on a user's device.

[1842] "Means for receiving feedback" refers to the processes and techniques for receiving correction requests and suggestions from users and incorporating them into the system.

[1843] "Generative AI model" refers to machine learning models and algorithms that generate optimal visit sequences and travel plans based on information entered by users.

[1844] The present invention relates to a system that proposes an optimal route for efficiently visiting multiple tourist spots specified by a user. This system can provide the best possible travel experience within a limited time.

[1845] System Configuration

[1846] The system mainly consists of the following elements:

[1847] 1. Terminal: A device that provides an interface for users to input information such as their departure point, the tourist spots they wish to visit, travel time, and transportation method.

[1848] 2. Server: Receives information from users and generates optimal travel plans using generative AI models and map APIs.

[1849] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[1850] 4. Map API: Refers to external resources for obtaining travel times between tourist attractions (e.g., Google Maps API).

[1851] Program processing explanation

[1852] User input

[1853] Terminal: The user launches the application and enters the starting point, the attractions they want to visit, the travel time, and the mode of transportation. This data is collected using text boxes and drop-down lists.

[1854] Terminal: Converts collected data into JSON format or similar and sends it to the server as an HTTP POST request.

[1855] Generate optimal routes

[1856] Server: Parses the received data and formats it into a data format. Stores the parsed information in an internal data structure.

[1857] Server: Calls the generative AI model based on the prepared data and calculates the order of visits. For example, it uses a function called "calculateOptimalRoute" to consider the destinations, travel time, and transportation method.

[1858] Server: Call the map API to get the travel time between each tourist spot. Use the function "getTravelTime" and pass the pair between each spot.

[1859] Server: The results of the generative AI model are combined with data from the map API to optimize the overall travel plan. The "optimizeTravelPlan" function is used to optimize the order of visits and travel time.

[1860] Presenting your travel plan

[1861] Server: Serialize the generated travel plan in JSON format and send it to the terminal as an HTTP response.

[1862] Terminal: The received travel plan is deserialized and displayed in a user interface, specifically in a visual timeline or list format.

[1863] Feedback and Recalibration

[1864] User: Review the proposed itinerary and provide feedback if any modifications are needed, such as requesting a shorter stay at Ueno Zoo.

[1865] Terminal: Send the modification request in JSON format to the server as an HTTP POST request.

[1866] Server: Receives feedback and re-optimizes the plan using the generative AI model. Recalculates and generates a new optimal travel plan.

[1867] Server: Sends the revised travel plan to the device.

[1868] Terminal: Present the revised plan to the user.

[1869] Specific example explanation

[1870] 1. User Input Scenarios

[1871] User: User A wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[1872] Departure point: Tokyo Station

[1873] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[1874] Duration: 3 hours

[1875] Transportation: Taxi

[1876] 2. Processing Flow

[1877] Terminal: Receives user A's input and sends it to the server.

[1878] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[1879] Server: Calls the map API and obtains the travel time between each visited location.

[1880] For example, suppose it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[1881] Server: Optimizes the overall itinerary, taking into account the order of visits, travel time, and duration of stay (e.g., Tokyo Tower 30 minutes, Ueno Zoo 60 minutes, Sensoji Temple 45 minutes).

[1882] Server: Generates a travel plan and sends it to the terminal.

[1883] Terminal: Presents a detailed plan to User A.

[1884] 3. Examples of feedback and readjustment

[1885] User: User A gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[1886] Terminal: Sends a modification request to the server.

[1887] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[1888] Server: Sends the new plan to the device.

[1889] Terminal: Present the revised plan to User A.

[1890] Prompt Sentence Examples

[1891] "User A wants to make the most of three hours in Tokyo. Their starting point is Tokyo Station, and they want to visit Tokyo Tower, Ueno Zoo, and Sensoji Temple. They plan to travel by taxi. What is the best time to stay at each location and the best order to visit them?"

[1892] The present invention allows users to travel to tourist spots efficiently and time-effectively, and enjoy the maximum travel experience.

[1893] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1894] Step 1:

[1895] The user enters information

[1896] User: Launches the application and enters information such as departure point, desired tourist spots, travel time, and transportation method.

[1897] Terminal: After the user enters information into the input form, convert this data into JSON format.

[1898] Input: departure point, tourist attractions you want to visit, travel time, and transportation information.

[1899] Output: JSON formatted data.

[1900] Step 2:

[1901] The device sends the data to the server

[1902] Terminal: Send the generated JSON format data to the server as an HTTP POST request.

[1903] Input: JSON formatted data.

[1904] Output: HTTP POST request to the server.

[1905] Step 3:

[1906] The server receives and analyzes the data

[1907] Server: Parses the received data and converts it into the appropriate data format, specifically deserializing it and storing it in an internal data structure.

[1908] Input: JSON formatted data included in an HTTP POST request.

[1909] Output: Parsed data stored in internal data structures.

[1910] Step 4:

[1911] The server calculates the visit order using the generative AI model

[1912] Server: Based on the parsed information, the server uses a generative AI model to calculate the optimal route. The function used for this calculation is "calculateOptimalRoute".

[1913] Input: Parsed data stored in internal data structures.

[1914] Output: The optimal visit sequence.

[1915] Step 5:

[1916] The server calls the map API to obtain the travel time between each tourist spot.

[1917] Server: Calls a map API (e.g., Google Maps API) to obtain the travel time between each point. Uses the "getTravelTime" function to obtain the travel time between each point.

[1918] Input: Optimal visit sequence.

[1919] Output: Travel time between each tourist spot.

[1920] Step 6:

[1921] The server optimizes the travel plan

[1922] Server: Optimize the overall travel plan based on the results of the generative AI model and travel time data between each tourist spot. Using the function "optimizeTravelPlan", optimization is performed taking into account the order of visits, travel time, and length of stay.

[1923] Input: Optimal visit sequence and travel time data between each tourist spot.

[1924] Output: Optimized trip plan.

[1925] Step 7:

[1926] The server sends the optimized travel plan to the device.

[1927] Server: Serialize the optimized itinerary into JSON format and send it to the terminal as an HTTP response.

[1928] Input: Optimized travel plan.

[1929] Output: HTTP response to the device.

[1930] Step 8:

[1931] The device displays the travel plan to the user.

[1932] Terminal: The travel plan received from the server is deserialized and displayed in the user interface. Specific display methods include timeline and list formats.

[1933] Input: HTTP response from the server (travel itinerary).

[1934] Output: The itinerary displayed to the user.

[1935] Step 9:

[1936] Users submit feedback

[1937] User: Review the proposed travel plan and provide feedback if necessary to make any necessary corrections.

[1938] Terminal: Convert the feedback into JSON format and send it to the server as an HTTP POST request.

[1939] Input: User feedback.

[1940] Output: HTTP POST request to the server.

[1941] Step 10:

[1942] The server will readjust based on the feedback.

[1943] Server: Analyzes the received feedback and re-optimizes the plan using the generative AI model. Re-calculate and re-optimize based on the new conditions.

[1944] Input: User feedback.

[1945] Output: The re-arranged itinerary.

[1946] Step 11:

[1947] The server sends the re-arranged travel plan to the device.

[1948] Server: Serialize the re-arranged itinerary into JSON format and send it to the terminal as an HTTP response.

[1949] Input: rearranged travel plans.

[1950] Output: HTTP response to the device.

[1951] Step 12:

[1952] The device displays the re-arranged itinerary to the user.

[1953] Terminal: Deserialize the reconciled itinerary and display it in the user interface.

[1954] Input: HTTP response from the server (the rescheduled itinerary).

[1955] Output: The re-adjusted itinerary displayed to the user.

[1956] (Application example 1)

[1957] 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."

[1958] Conventional travel planning systems have difficulty proposing optimal routes that efficiently visit tourist spots specified by the user. Furthermore, they lack the functionality to respond to traffic conditions and user feedback in real time and to actually operate the optimized plan in an autonomous vehicle. As a result, they have been unable to provide efficient use of time or a comfortable travel experience.

[1959] 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.

[1960] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and a departure point, means for generating an optimal visiting order based on the calculation, means for obtaining travel times and stay times between each location based on the visiting order, means for optimizing the overall travel plan, means for presenting the optimized travel plan to the user, and means for optimizing the driving route of the autonomous vehicle. This makes it possible to calculate in real time the optimal route for efficiently visiting tourist spots specified by the user, thereby maximizing the travel experience.

[1961] "Means for inputting multiple user-specified locations" refers to a mechanism that allows a user to specify and input locations they wish to visit through an interface.

[1962] The "means for calculating the route" is an algorithm or program for calculating the optimal visiting order based on the input starting point and multiple locations.

[1963] The "means for generating the optimal visiting order" is a function that derives the order in which the user should visit places based on the calculated route information.

[1964] "Means for obtaining travel time and duration between each location" refers to a method of obtaining data from an API or database to obtain the travel time between the locations you wish to visit and the duration of stay at each location.

[1965] The "means for optimizing the overall travel plan" is a calculation method for making the user's travel plan most efficient, taking into account the acquired travel time and stay time.

[1966] The "means for presenting an optimized travel plan to a user" is a mechanism for displaying or notifying a user of the calculated optimal travel plan.

[1967] A "means for optimizing the driving route of an autonomous vehicle" is a function or program that sets a route so that the autonomous vehicle can travel efficiently between specified destinations based on an optimized travel plan.

[1968] This invention provides a system that allows users to efficiently create and execute travel plans. The system mainly consists of a terminal, a server, a generative AI model, and a map API.

[1969] System Configuration

[1970] 1. Terminal: A device that allows users to input information such as their departure point, the tourist spots they want to visit, the travel time, and the mode of transportation they will use. This device can be a smartphone or an infotainment system in an autonomous vehicle. This terminal provides the user interface and has the function of sending the input data to a server.

[1971] 2. Server: The server receives information from the user and generates an optimal travel plan using the generative AI model and map API. The server includes a route calculation tool, an optimization tool, and a tool to present the plan to the user.

[1972] 3. Generative AI model: Contains algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information such as the starting point, tourist attractions, travel time, and transportation method. This model is a generative AI and performs complex route calculations and optimizations.

[1973] 4. Map API: An external resource used to obtain travel times between tourist destinations, such as Google Maps API.

[1974] Program processing

[1975] 1. User Input:

[1976] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, and the mode of transportation. This input data is then sent to the server.

[1977] Example of a user: For example, a user enters the following information:

[1978] Starting point: Central Station

[1979] Places I'd like to visit: Museums, parks, shopping malls

[1980] Duration: 4 hours

[1981] Transportation: car

[1982] 2. Generate optimal route:

[1983] Server: Analyzes the received data and calculates the optimal visit order using a generative AI model.

[1984] Example prompt: Create an optimal route within 240 minutes to visit the following tourist attractions: museum, park, shopping mall

[1985] Server: Calls the map API to obtain travel time between each tourist spot.

[1986] 3. Optimize and present your travel plans:

[1987] Server: Optimizes the overall travel plan by taking into account the order of visits, travel time, and length of stay.

[1988] Server: Generates an optimized travel plan and sends it to the device.

[1989] Device: Shows the user the best travel plans.

[1990] For example, the generated plan will look like this:

[1991] Starting point: Central Station

[1992] Visit order: Museum (60 mins), Park (90 mins), Shopping Mall (90 mins)

[1993] Transportation: Car

[1994] Total time: 4 hours

[1995] Server roles and software used

[1996] The server has a wide range of roles. First, it receives input data from users and generates an optimal travel plan based on that data using a generative AI model. It also uses map APIs such as Google Maps API to obtain travel times and takes them into account to optimize the overall plan. The main software used is as follows:

[1997] Flask: Used as a web application framework to process HTTP requests from users.

[1998] Requests: An HTTP request library used to retrieve data from external APIs.

[1999] OpenAI API: Used as a generative AI model to perform path calculations and optimization.

[2000] Google Maps API: Used to provide map data and obtain travel times between locations.

[2001] This system allows users to efficiently travel around tourist spots using autonomous vehicles and enjoy optimal travel plans in real time.

[2002] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2003] Step 1:

[2004] The user enters travel plan information into the device.

[2005] The user inputs information such as the departure point, tourist spots they want to visit, travel time, and mode of transportation via a device (smartphone or the infotainment system of the autonomous vehicle). This data is later sent to the server. As an example of input, the departure point is "Central Station," the destinations are "museums, parks, shopping malls," the travel time is "240 minutes," and the mode of transportation is "car."

[2006] Step 2:

[2007] The device sends the input data to the server

[2008] The terminal sends the data entered by the user to the server. Specifically, it sends the data to the server in JSON format using an HTTP request. The data sent includes the departure point, destinations, travel time, and transportation method.

[2009] Step 3:

[2010] The server analyzes the received data and creates a prompt for the generative AI model.

[2011] The server analyzes the received data and creates a prompt to generate the optimal visiting sequence based on the user's specified criteria. An example of a prompt might be, "Please create the optimal route within 240 minutes by visiting the following tourist attractions: museum, park, shopping mall."

[2012] Step 4:

[2013] A generative AI model calculates the optimal visit order based on the prompt.

[2014] The server sends the prompt to the generative AI model and receives a suggestion for the optimal visiting order from the model. The optimal visiting order suggested by the generative AI model is a route that efficiently visits tourist spots within a given time. For example, suppose the generative AI model calculates the order as "museum → park → shopping mall."

[2015] Step 5:

[2016] The server obtains travel time using the map API.

[2017] The server uses a map API (such as Google Maps API) to obtain the travel time between each tourist spot. Specifically, it sends an HTTP request to the map API to obtain the travel time between each point (for example, from the central station to the museum, from the museum to the park, and from the park to the shopping mall). The output may show that the travel time from the central station to the museum is 30 minutes, from the museum to the park is 20 minutes, and from the park to the shopping mall is 40 minutes.

[2018] Step 6:

[2019] The server optimizes the overall travel plan by taking into account travel time and dwell time.

[2020] The server generates an optimized itinerary by taking into account the visit order proposed by the generative AI model, the travel time for each period obtained from the map API, and the user's stay time (e.g., 60 minutes at the museum, 90 minutes at the park, 90 minutes at the shopping mall). The optimized plan includes the specific visit order, means of transportation, travel time between each location, and stay time.

[2021] Step 7:

[2022] The server sends the optimized travel plan to the device.

[2023] The server then sends the generated optimized itinerary back to the terminal as an HTTP response. This itinerary includes detailed visit order, transportation means, travel time, and duration of stay.

[2024] Step 8:

[2025] The device displays an optimized itinerary to the user

[2026] The terminal displays the optimized itinerary received from the server to the user. The user can review the displayed itinerary and send feedback to the server if necessary. For example, the user can provide feedback such as "I would like to spend less time in the park."

[2027] Step 9:

[2028] Receive user feedback and make adjustments

[2029] The server receives user feedback, reuses the generative AI model and map API to optimize a new plan, and then sends the new plan to the device, which then presents it to the user again. By repeating this cycle, the server provides the user with the most satisfying travel plan.

[2030] 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.

[2031] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[2032] System Configuration

[2033] The system mainly consists of the following elements:

[2034] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting the user's emotions.

[2035] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[2036] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[2037] 4. Map API: Refers to an external resource for obtaining travel times between tourist spots.

[2038] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[2039] Program processing

[2040] 1. User Input:

[2041] Terminal: The user launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[2042] Terminal: Sends these input data to the server.

[2043] 2. Generate optimal route:

[2044] Server: Analyzes the received data and extracts necessary parameters (e.g., starting point, desired points to visit, travel time, mode of transportation, emotion).

[2045] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[2046] Server: Calls the map API and obtains travel time between each tourist spot.

[2047] Server: Using the emotion engine, it suggests tourist spots that suit the user's emotions and adjusts the order of visits.

[2048] 3. Present your travel plan:

[2049] Server: Sends the generated travel plan to the terminal.

[2050] Device: Shows the user the best travel plans.

[2051] 4. Feedback and readjustment:

[2052] Terminal: The user reviews the travel plan and requests modifications if necessary.

[2053] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[2054] Specific example explanation

[2055] User Input Scenarios

[2056] User B wants to make the most of his 3 hours in Tokyo. He accesses the system via a browser and enters the following information:

[2057] Departure point: Tokyo Station

[2058] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[2059] Duration: 3 hours

[2060] Transportation: Taxi

[2061] Current Emotion: I want to relax

[2062] Processing flow

[2063] Terminal: Receives User B's input and sends it to the server.

[2064] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[2065] Server: Calls the map API and obtains travel time between each location (Tokyo Station, Tokyo Tower, Ueno Zoo, Sensoji Temple).

[2066] For example, it takes 15 minutes by taxi from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[2067] Server: Activates the emotion engine and adjusts the tourist spots and visit order based on User B's emotion of "wanting to relax" (e.g., prioritize quiet areas and parks).

[2068] Server: Optimize the overall itinerary by taking into account the order of visits, travel time, and duration of stay (e.g., 30 minutes at Tokyo Tower, 60 minutes at Ueno Zoo, 45 minutes at Sensoji Temple).

[2069] Server: Generates a travel plan and sends it to the terminal.

[2070] Device: Show user B a detailed plan like the one below.

[2071] Departure point: Tokyo Station

[2072] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[2073] Transportation between points: Taxi

[2074] Total time required: 3 hours

[2075] Examples of feedback and readjustment

[2076] User: User B gives feedback saying, "I want to reduce the time I spend at Ueno Zoo."

[2077] Terminal: Sends a modification request to the server.

[2078] Server: Based on the feedback, reduce the visit time to Ueno Zoo to 45 minutes and re-optimize the overall plan.

[2079] Server: Sends the new plan to the device.

[2080] Device: Present the revised plan to User B.

[2081] In this way, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[2082] The processing flow will be explained below.

[2083] Step 1:

[2084] User: Launches the application and inputs the departure point, the tourist spots they want to visit, the travel time, the mode of transportation, and their current feelings.

[2085] Step 2:

[2086] Terminal: Receives the user's input and displays it to the user via a confirmation screen.

[2087] Step 3:

[2088] User: Check the input and click the send button.

[2089] Step 4:

[2090] Terminal: Sends the confirmed input data to the server.

[2091] Step 5:

[2092] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, and emotions.

[2093] Step 6:

[2094] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters.

[2095] Step 7:

[2096] Server: Calls the map API and obtains travel time between each tourist spot.

[2097] Example: Obtain data from the API such as "Tokyo Station → Tokyo Tower: 15 minutes," "Tokyo Tower → Ueno Zoo: 20 minutes," "Ueno Zoo → Sensoji Temple: 10 minutes," and "Sensoji Temple → Tokyo Station: 15 minutes."

[2098] Step 8:

[2099] Server: Activates the emotion engine and makes additional adjustments based on the user's emotions (e.g., preferring quiet places if they want to relax).

[2100] Step 9:

[2101] Server: Optimizes the overall itinerary, taking into account visit order, mode of transportation, travel time, and duration of stay.

[2102] Step 10:

[2103] Server: Generates an optimized travel plan and sends it to the terminal in JSON format, etc.

[2104] Step 11:

[2105] Terminal: Analyzes the received travel plan and displays it in an easy-to-understand manner for the user.

[2106] Example: A detailed plan such as "Tokyo Station → Tokyo Tower (stay 30 minutes) → Ueno Zoo (stay 60 minutes) → Sensoji Temple (stay 45 minutes) → Tokyo Station" is displayed through the GUI.

[2107] Step 12:

[2108] User: Review the proposed itinerary and enter feedback on the itinerary (e.g., "I would like to spend less time at Ueno Zoo").

[2109] Step 13:

[2110] Terminal: Receives user feedback and sends correction requests to the server.

[2111] Step 14:

[2112] Server: Analyzes the feedback and again leverages the generative AI model, emotion engine, and map API to generate a re-optimized itinerary.

[2113] Step 15:

[2114] Server: Sends the revised travel plan to the device.

[2115] Step 16:

[2116] On the device: The revised plan is displayed to the user for final confirmation.

[2117] Step 17:

[2118] User: Review the revised plan and approve or request further revisions.

[2119] Step 18:

[2120] Server: Determines the finalized travel plan and stores and manages all information.

[2121] Example 2

[2122] 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."

[2123] Conventional travel plan generation systems can propose the optimal route for efficiently visiting multiple tourist spots specified by the user, but they have the problem of not being able to adjust the plan to take the user's emotions into consideration. Therefore, there is a need to provide a more satisfying travel experience by adjusting the travel plan based on the user's current emotions.

[2124] 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.

[2125] In this invention, the server includes means for inputting multiple locations specified by the user, means for calculating a route based on the input multiple locations and the departure point, means for generating an optimal visiting order, and means for recognizing the user's emotions and adjusting the travel plan based on the emotions, thereby making it possible to provide an optimal travel plan that takes the user's emotions into consideration.

[2126] "Means for inputting multiple user-specified locations" refers to an interface that allows users to input information such as their departure point and tourist spots they wish to visit.

[2127] "Means for calculating a route" refers to an algorithm or program for calculating the order of visits and travel routes based on multiple input points and the starting point.

[2128] "Means for generating the optimal visiting order" refers to an algorithm or program for determining the order in which multiple tourist spots can be visited efficiently based on calculated route information.

[2129] "Means of obtaining travel time and duration between each location" refers to means of obtaining information about travel time and duration between each tourist destination using external resources or APIs.

[2130] "Means for optimizing the overall travel plan" refers to programs and algorithms that optimize the overall travel schedule by taking into account factors such as travel time between each location, length of stay, and user emotions.

[2131] "Means for presenting an optimized travel plan to a user" refers to an interface for visually displaying an optimized travel schedule to a user.

[2132] "Means for recognizing a user's emotions and adjusting the travel plan based on said emotions" refers to an algorithm or program that recognizes a user's emotions through user input, sensors, etc., and adjusts the travel plan based on those emotions.

[2133] "Means for receiving feedback and re-adjusting the optimized itinerary" refers to functionality or algorithms for receiving revision requests from users and re-optimizing the itinerary based on those requests.

[2134] The present invention aims to further improve the travel experience by adding an emotion engine that recognizes the user's emotions to a system that suggests the optimal route for efficiently visiting multiple tourist spots specified by the user.

[2135] System Configuration

[2136] The system mainly consists of the following elements:

[2137] 1. Terminal: Provides an interface for users to input their departure point, desired tourist spots, travel time, and transportation method. It also includes an interface for inputting or detecting user emotions.

[2138] 2. Server: Receives information from users and generates optimal travel plans using generative AI models, map APIs, and an emotion engine.

[2139] 3. Generative AI model: Includes algorithms that calculate the optimal visit sequence and transportation methods based on user-specified information.

[2140] 4. Map API: Refers to an external resource to obtain travel time between tourist spots. For example, Google Maps API is used.

[2141] 5. Emotion engine: Contains algorithms to recognize user emotions and adjust travel plans accordingly.

[2142] User Input

[2143] User: Starts the application and inputs the departure point, the tourist spot they want to visit, the travel time, the mode of transportation, and their current feelings. The input method is to use the form displayed on the terminal.

[2144] Processing the data

[2145] Terminal: Sends the entered data to the server. Specifically, it converts the information entered by the user into JSON format, creates an HTTP request, and sends it to the server.

[2146] Server: Analyzes the received data and extracts parameters such as the starting point, desired points to visit, travel time, mode of transportation, emotions, etc. The received data is parsed and the necessary information is extracted.

[2147] Server: Launches the generative AI model and calculates the optimal visit sequence based on the specified parameters. The generative AI model derives the optimal sequence using, for example, a machine learning algorithm.

[2148] Server: Calls the map API and obtains the travel time between each tourist spot. For example, it uses the Google Maps API to calculate the travel distance and time between tourist spots.

[2149] Server: Operates the emotion engine to suggest tourist spots that suit the user's emotions and adjust the order of visits. The emotion engine uses natural language processing and emotion analysis techniques, for example.

[2150] Travel plan generation and presentation

[2151] Server: Sends the generated travel plan to the terminal. The generated plan is converted to JSON format and sent to the terminal as an HTTP response.

[2152] Terminal: Presents the best travel plans to the user, using an interface that parses the received data and displays it visually.

[2153] User feedback and plan realignment

[2154] User: Checks travel plans and requests amendments if necessary. Amendment requests are sent from the device.

[2155] Server: Receives user feedback and re-optimizes using the emotion engine and generative AI model.

[2156] Specific example explanation

[2157] User Input Scenarios

[2158] User: User B, who wants to make the most of his 3 hours in Tokyo, accesses the system and enters the following information:

[2159] Departure point: Tokyo Station

[2160] Places I want to visit: Tokyo Tower, Ueno Zoo, Sensoji Temple

[2161] Duration: 3 hours

[2162] Transportation: Taxi

[2163] Current Emotion: I want to relax

[2164] Processing flow

[2165] Terminal: Receives User B's input and sends it to the server.

[2166] Server: Receives the information and calculates the optimal visit sequence using a generative AI model.

[2167] Server: Uses the Google Maps API to obtain travel times between each location.

[2168] For example, it takes 15 minutes to travel from Tokyo Station to Tokyo Tower, 20 minutes from Tokyo Tower to Ueno Zoo, 10 minutes from Ueno Zoo to Sensoji Temple, and 15 minutes from Sensoji Temple to Tokyo Station.

[2169] Server: Activates the emotion engine and prioritizes quiet areas and parks based on the emotion of "wanting to relax."

[2170] Server: Generates an optimal travel plan taking into account visit order, travel time, and stay time.

[2171] Server: Sends the travel plan to the device.

[2172] Device: Display the following detailed plan to User B.

[2173] Departure point: Tokyo Station

[2174] Visit order: Tokyo Tower (30 minutes), Ueno Zoo (60 minutes), Sensoji Temple (45 minutes)

[2175] Transportation between points: Taxi

[2176] Total time required: 3 hours

[2177] User feedback and examples of rebalancing

[2178] User: Gives feedback that they would like to reduce their time spent at Ueno Zoo.

[2179] Terminal: Sends a modification request to the server.

[2180] Server: Based on feedback, reduce the visit time at Ueno Zoo to 45 minutes and re-optimize the overall plan.

[2181] Server: Sends the new plan to the device.

[2182] Device: Present the revised plan to User B.

[2183] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions, and provide the best possible travel experience within a limited time.

[2184] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2185] Step 1: Sending user input data

[2186] User: Launches the application and inputs their departure point, the tourist attractions they want to visit, travel time, mode of transportation, and current feelings.

[2187] Input: Departure point, list of tourist attractions, travel time, transportation method, emotion

[2188] Output: Formatted input data

[2189] Terminal: Formats the information entered by the user, converts it to JSON format, creates an HTTP request and sends it to the server.

[2190] Input: User-entered data

[2191] Output: HTTP request sent to the server

[2192] Step 2: Receiving and analyzing data

[2193] Server: Analyzes the data received from the terminal, parses the JSON data, and extracts the necessary parameters.

[2194] Input: HTTP request (JSON data)

[2195] Output: starting point, list of tourist attractions, travel time, transportation method, and emotion parameters

[2196] Step 3: Calculate the optimal route

[2197] Server: Based on the extracted parameters, the generative AI model is launched and the optimal visiting order is calculated.

[2198] Input: Departure point, list of sightseeing spots, travel time, transportation method

[2199] Output: Optimal visit sequence

[2200] What it does: It uses machine learning algorithms to derive an efficient order of visits within a given timeframe.

[2201] Step 4: Obtain travel times between locations

[2202] Server: Calls a map API (e.g., Google Maps API) and obtains travel times between tourist spots.

[2203] Input: Departure point, visit order, transportation method

[2204] Output: Travel time between each point

[2205] Specific behavior: Create an API request and calculate the travel distance and time between each tourist spot.

[2206] Step 5: Emotional Adjustment

[2207] Server: Runs the emotion engine and adjusts the travel plan based on the user's emotions.

[2208] Input: Emotion data, optimal visit order, travel time

[2209] Output: Visit sequence adjusted for sentiment

[2210] What it does: Uses a sentiment analysis algorithm to reorder visits to prioritize quieter areas and relaxing tourist spots.

[2211] Step 6: Generate an optimized itinerary

[2212] Server: Optimizes the overall travel plan based on the optimal order of visits, travel time between each location, and duration of stay.

[2213] Input: adjusted visit sequence, travel time, and dwell time

[2214] Output: Optimized itinerary

[2215] Specific operation: Performs calculations recursively to generate a schedule that provides the best travel experience within the time constraints.

[2216] Step 7: Submit and view your itinerary

[2217] Server: Convert the generated travel plan into JSON format and send it to the terminal as an HTTP response.

[2218] Input: Optimized itinerary

[2219] Output: HTTP response (travel plan) sent to the device

[2220] Terminal: Parses the received data and visually displays the itinerary in a user interface.

[2221] Input: HTTP response (JSON data)

[2222] Output: The itinerary displayed to the user

[2223] Step 8: Receive feedback and readjust

[2224] User: Checks the itinerary and requests modifications if necessary. For example, feedback that they would like to spend less time at Ueno Zoo.

[2225] Input: Feedback message

[2226] Output: Modification request to terminal

[2227] Terminal: Converts the feedback into JSON format and sends it to the server.

[2228] Input: Feedback message

[2229] Output: HTTP request sent to the server

[2230] Step 9: Recalculate the plan

[2231] Server: Analyzes the feedback and re-optimizes the itinerary using an emotion engine and generative AI models.

[2232] Input: Feedback message

[2233] Output: Revised and optimized plan

[2234] What happens: Rerun the algorithm based on the new parameters to generate an optimal schedule.

[2235] Step 10: Submit and view your remediation plan

[2236] Server: Convert the revised plan into JSON format and send it back to the terminal as an HTTP response.

[2237] Input: Revised optimized plan

[2238] Output: HTTP response sent to the device

[2239] Terminal: Parses the received data and visually displays the revised itinerary to the user.

[2240] Input: HTTP response

[2241] Output: A remediation plan that is displayed to the user

[2242] As a result, the present invention can provide an optimal travel plan that takes into account the user's emotions and reflects feedback as necessary.

[2243] (Application example 2)

[2244] 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."

[2245] Conventional travel plan generation systems are limited to calculating the optimal visiting order based on multiple locations and departure points specified by the user, making it difficult to provide new travel experiences that utilize user emotions and autonomous vehicles.In addition, there was a need for a system that could generate travel plans that take user emotions into consideration, readjust plans based on feedback, calculate optimal routes using generative AI models, and obtain travel times using map APIs.

[2246] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2247] In this invention, the server includes: means for inputting multiple locations specified by the user; means for calculating a route based on the input multiple locations and a departure point; means for generating an optimal visiting order based on the calculation; means for obtaining travel times and stay times between each location based on the visiting order; means for optimizing the overall itinerary; means for presenting the optimized itinerary to the user; means for detecting the user's emotions and adjusting the itinerary based on the detected emotions; means for receiving input from a terminal via an interface installed in the autonomous vehicle; means for calculating an optimal visiting order based on the specified information using a generative AI model; and means for calling a map API and obtaining travel times between each location. This enables the generation of a flexible and optimal itinerary that reflects the user's emotions.

[2248] "Specify" means that the user inputs specific information or conditions.

[2249] "Route calculation" is the process of calculating the optimal route and visiting sequence based on multiple specified points and a starting point.

[2250] "Visit order" refers to determining the optimal order for multiple locations specified by the user.

[2251] "Travel time" refers to the time required to travel between each location.

[2252] "Dwell time" refers to the amount of time a user spends at each tourist spot or other location.

[2253] "Optimization" is the process of creating and adjusting the most efficient and effective plan by taking multiple factors into consideration.

[2254] A "trip plan" is a travel plan that includes a specified departure point, visit points, means of transportation, travel time, and duration of stay.

[2255] "Emotion detection" refers to recognizing the user's current mood or emotional state.

[2256] An "autonomous vehicle" is a vehicle that drives autonomously without human intervention.

[2257] "Terminal" refers to a device or interface through which a user can input information and receive results.

[2258] A "generative AI model" is an algorithm that uses artificial intelligence to perform necessary calculations and predictions based on specified information.

[2259] "Maps API" means an application programming interface for providing geographic information and calculating routes and travel times.

[2260] This invention is a system that provides optimal travel plans by taking into account the user's emotions. The system mainly consists of a server, a user terminal, an autonomous vehicle, a generative AI model, a map API, and an emotion engine.

[2261] Hardware and software used

[2262] Hardware: Autonomous vehicle computers, user devices (smartphones, etc.)

[2263] software:

[2264] Python: A major programming language

[2265] Flask: a web application framework

[2266] OpenAI GPT-4: Generative AI Model

[2267] Google Maps API:Map API

[2268] Microsoft Azure Emotion API: Emotion Engine

[2269] Processing flow

[2270] The server first receives data from the user's device, including the starting point, desired tourist spots, travel time, transportation method, and current emotion. Based on the user's input data, the server uses a generative AI model to calculate the optimal visiting order, calling a map API to obtain the travel time between each point.

[2271] The generated travel plan is then analyzed using an emotion engine to analyze the user's emotional information and adjust the plan as necessary. For example, if the user has the emotion "I want to relax," the system will prioritize quiet tourist spots in the itinerary.

[2272] The final itinerary is sent to the terminal and presented to the user, who can then provide feedback on the plan, which the server then uses to re-optimize the plan.

[2273] Specific examples

[2274] User A enters the following information into the system to plan a sightseeing trip in Kyoto:

[2275] Departure point: Kyoto Station

[2276] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[2277] Duration: 4 hours

[2278] Transportation: Self-driving vehicles

[2279] Current Emotion: I want to relax

[2280] Based on this information, the server generates the optimal travel route. Example prompts for the generative AI model are:

[2281] "Departure point: Kyoto Station

[2282] Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine

[2283] Duration: 4 hours

[2284] Emotion: I want to relax.”

[2285] The generated itinerary is provided to the user through an application installed in the autonomous vehicle, allowing the user to efficiently travel around the tourist spots listed above and enjoy a satisfying sightseeing experience.

[2286] As a result, the present invention enables the generation of flexible and optimal travel plans that reflect the user's feelings.

[2287] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2288] Step 1:

[2289] The user inputs the departure point, the tourist spots they want to visit, the required travel time, the means of transportation, and their current emotions into the terminal. These inputs are sent to the server. The input data of the terminal includes the departure point, the list of tourist spots they want to visit, the required travel time, the means of transportation, and their emotions.

[2290] Step 2:

[2291] The server analyzes the received data and extracts the parameters necessary to generate a travel plan based on the departure point, desired tourist spots, travel time, transportation method, and emotions. The input data is analyzed individually and each parameter is organized as structured data.

[2292] Step 3:

[2293] The server uses a generative AI model to calculate the optimal order of visits. At this time, data from the user is input into the generative AI model as a prompt. For example, a prompt such as "Departure point: Kyoto Station; Places to visit: Kiyomizu-dera Temple, Kinkaku-ji Temple, Fushimi Inari Taisha Shrine; Time required: 4 hours; Emotion: I want to relax" is created. Based on this prompt, the generative AI model outputs the optimal order of visits to tourist spots.

[2294] Step 4:

[2295] The server calls the Google Maps API to obtain travel time between each tourist spot. Based on the visit order obtained from the generative AI model, travel time data for each spot is obtained from the API. For example, specific data such as the travel time from Kyoto Station to Kiyomizu-dera Temple and from Kiyomizu-dera Temple to Kinkaku-ji Temple are obtained.

[2296] Step 5:

[2297] The server uses the emotion engine to adjust the travel plan based on the user's emotions. If the user wants to relax, the emotion engine adjusts the schedule to prioritize quiet places and relaxing tourist spots. Based on the input emotion data, the server reconfigures the list and order of tourist spots.

[2298] Step 6:

[2299] The generated optimized itinerary is sent from the server to the terminal, which then displays it to the user. Details of the optimized itinerary (such as the order of visits, travel time between each point, and duration of stay) are displayed on the user's screen.

[2300] Step 7:

[2301] When a user provides feedback on a travel plan, the device sends this feedback to the server. If the user has a specific request for revision, such as "I want to reduce the time spent at Ueno Zoo," the device communicates this to the server.

[2302] Step 8:

[2303] The server receives the user's feedback and again uses the generative AI model and emotion engine to optimize the travel plan. Based on the feedback, it creates new prompts and runs the optimization loop again to generate a revised plan.

[2304] Step 9:

[2305] The server sends the revised itinerary to the terminal, which then presents it to the user again, and the details of the revised itinerary are displayed on the user's screen.

[2306] 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.

[2307] 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.

[2308] 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.

[2309] 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.

[2310] 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.

[2311] 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.

[2312] 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).

[2313] 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.

[2314] 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."

[2315] 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.

[2316] 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).

[2317] 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.

[2318] 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.

[2319] 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.

[2320] 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.

[2321] 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.

[2322] 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.

[2323] 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.

[2324] 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.

[2325] 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.

[2326] 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.

[2327] The following is further disclosed regarding the above embodiment.

[2328] (Claim 1)

[2329] a means for inputting a plurality of user-specified locations;

[2330] means for calculating a route based on the inputted plurality of points and a starting point;

[2331] means for generating an optimal visiting sequence based on the calculation;

[2332] A means for acquiring travel time and stay time between each location based on the visiting order;

[2333] A means to optimize your overall travel plan;

[2334] means for presenting the optimized travel plan to a user;

[2335] A system including:

[2336] (Claim 2)

[2337] The system according to claim 1, further comprising means for proposing an optimal transportation means based on the visiting sequence generated by said route calculation means.

[2338] (Claim 3)

[2339] 10. The system of claim 1, further comprising means for receiving feedback from a user and readjusting the optimized travel plan.

[2340] "Example 1"

[2341] (Claim 1)

[2342] a means for inputting a plurality of user-specified locations;

[2343] means for calculating a route based on the inputted plurality of points and a starting point;

[2344] means for generating an optimal visiting sequence based on the calculation;

[2345] A means for acquiring travel time and stay time between each location based on the visiting order;

[2346] A means to optimize your overall travel plan;

[2347] means for presenting the optimized travel plan to a user;

[2348] means for receiving feedback from a user and readjusting the optimized travel plan;

[2349] A system including:

[2350] (Claim 2)

[2351] The system according to claim 1, further comprising means for proposing an optimal transportation means based on the visiting sequence generated by said route calculation means.

[2352] (Claim 3)

[2353] A means for users to input information such as the departure point, the places they want to visit, the required time, and the means of transportation via a terminal;

[2354] The system of claim 1 further comprising means for calculating an optimal visiting sequence using a generative AI model based on the information.

[2355] "Application Example 1"

[2356] (Claim 1)

[2357] a means for inputting a plurality of user-specified locations;

[2358] means for calculating a route based on the inputted plurality of points and a starting point;

[2359] means for generating an optimal visiting sequence based on the calculation;

[2360] A means for acquiring travel time and stay time between each location based on the visiting order;

[2361] A means to optimize your overall travel plan;

[2362] means for presenting the optimized travel plan to a user;

[2363] A means for optimizing a driving route of an autonomous vehicle;

[2364] A system including:

[2365] (Claim 2)

[2366] The system according to claim 1, further comprising means for proposing an optimal transportation means based on the visiting sequence generated by said route calculation means.

[2367] (Claim 3)

[2368] 10. The system of claim 1, further comprising means for receiving feedback from a user and readjusting the optimized travel plan.

[2369] "Example 2: Combining Emotion Engines"

[2370] (Claim 1)

[2371] a means for inputting a plurality of user-specified locations;

[2372] means for calculating a route based on the inputted plurality of points and a starting point;

[2373] means for generating an optimal visiting sequence based on the calculation;

[2374] A means for acquiring travel time and stay time between each location based on the visiting order;

[2375] A means to optimize your overall travel plan;

[2376] means for presenting the optimized travel plan to a user;

[2377] means for recognizing a user's emotions and adjusting the travel plan based on said emotions;

[2378] A system including:

[2379] (Claim 2)

[2380] The system according to claim 1, further comprising means for proposing an optimal transportation means based on the visiting sequence generated by said route calculation means.

[2381] (Claim 3)

[2382] 10. The system of claim 1, further comprising means for receiving feedback from a user and readjusting the optimized travel plan.

[2383] "Application example 2 when combining emotion engines"

[2384] (Claim 1)

[2385] a means for inputting a plurality of user-specified locations;

[2386] means for calculating a route based on the inputted plurality of points and a starting point;

[2387] means for generating an optimal visiting sequence based on the calculation;

[2388] A means for acquiring travel time and stay time between each location based on the visiting order;

[2389] A means to optimize your overall travel plan;

[2390] means for presenting the optimized travel plan to a user;

[2391] means for detecting a user's emotion and adjusting the travel plan based on the detected emotion;

[2392] A system including:

[2393] (Claim 2)

[2394] The system according to claim 1, further comprising means for proposing an optimal transportation means based on the visiting sequence generated by said route calculation means.

[2395] (Claim 3)

[2396] 10. The system of claim 1, further comprising means for receiving feedback from a user and readjusting the optimized travel plan.

[2397] (Claim 4)

[2398] 10. The system of claim 1, further comprising: means for receiving input from a terminal via an interface mounted on the autonomous vehicle.

[2399] (Claim 5)

[2400] 10. The system of claim 1, further comprising means for using a generative AI model to calculate an optimal visiting sequence based on the specified information.

[2401] (Claim 6)

[2402] The system of claim 1, further comprising means for calling a map API and obtaining travel times between each of the locations. [Explanation of symbols]

[2403] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting a plurality of user-specified locations; means for calculating a route based on the inputted plurality of points and a starting point; means for generating an optimal visiting sequence based on the calculation; A means for acquiring travel time and stay time between each location based on the visiting order; A means to optimize your overall travel plan; means for presenting the optimized travel plan to a user; A system including:

2. The system according to claim 1, further comprising means for suggesting an optimal transportation means based on the visiting sequence generated by said route calculation means.

3. The system of claim 1 , further comprising means for receiving feedback from a user and readjusting the optimized travel plan.

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