System

Abstract

A system is provided.SOLUTION: The system includes means for a user to input a destination and a departure date and time in a mobile terminal, means for transmitting the input destination and departure date and time to a server, means for generating a time schedule by using a generative artificial intelligence based on information received by the server, means for collecting information around the destination by the server, means for transmitting the schedule generated by the server and the collected information to the mobile terminal, and means for displaying the schedule and the information to the user by the mobile terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional travel planning and daily shopping, users have had the problem of searching for destination information and appropriate shopping locations, which requires time and effort, making it inefficient. Furthermore, when planning a trip, users have to manually research local tourist spots and gourmet information, which can make planning cumbersome. Furthermore, because car navigation apps and reminder apps are not linked, it is difficult to create shopping plans that fit the route. [Means for solving the problem]

[0005] This invention provides a system for improving the efficiency of travel planning and everyday use using a mobile device. The mobile device is equipped with a means for the user to input the destination and departure date and time, and the input information is sent to a server. The server uses generative artificial intelligence to create a time schedule based on the received information and collects information about the area around the destination. The created schedule and collected information are sent to the mobile device, which displays them to the user.

[0006] Furthermore, the system is equipped with a means for sending the shopping list registered in the reminder app to the server, and sends route information entered in the car navigation app to the server. Based on the received information, the server searches for store information along the route and sends information on the most suitable stores to stop at to the mobile device. The mobile device has the function of suggesting this to the user.

[0007] In this way, we provide a system that allows users to efficiently plan their trips and also to effectively carry out daily shopping.The system also has a function that reflects weather forecasts and traffic information in the schedule and dynamically adjusts tourist spots and restaurants.

[0008] A "mobile terminal" is an electronic device that a user can carry around, and includes smartphones, tablets, and the like.

[0009] A "user" is a person who uses the system and performs operations such as inputting a destination and registering a reminder.

[0010] A "destination" is a location that a user intends to reach when traveling or moving.

[0011] The "departure date and time" is the date and time when the user starts traveling or moving.

[0012] A "server" is a computer system that has the ability to receive, analyze, and send information over a network.

[0013] "Generative AI" is an AI technology that generates new information and plans based on received information.

[0014] A "time schedule" refers to plans or schedules organized in chronological order to efficiently progress a user's travel plans.

[0015] "Information gathering" refers to gathering information on tourist spots, gourmet food, stores, etc. around the destination.

[0016] A "reminder app" is an application that allows users to input and manage schedules and shopping lists in list format.

[0017] A "car navigation app" is a navigation application that searches for routes to a destination and displays them on a map.

[0018] "Route information" refers to the route information from the departure point to the destination searched by the car navigation app.

[0019] "Store information" refers to information about the location, business hours, inventory, etc. of stores that users can visit.

[0020] A "visually easy to understand format" is a display format designed to allow users to easily understand information.

[0021] A "weather forecast" is a forecast that predicts future weather conditions and provides that information.

[0022] "Traffic information" refers to information about traffic progress, such as road congestion and traffic accidents. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. This system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stores to stop by, and provides this information to the user.

[0045] When planning a trip

[0046] 1. Enter your destination and departure date and time

[0047] User: Launches the app on their smartphone and enters their travel destination and departure date and time (e.g., Kyoto, departure August 15th at 10:00).

[0048] 2. Submit your input

[0049] Terminal: Creates a request to send the destination and departure date and time entered by the user to the server, and sends it to the server.

[0050] 3. Generate a time schedule

[0051] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[0052] 4. Collection of additional information

[0053] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[0054] 5. Submitting schedules and information

[0055] Server: Formats the generated time schedule and collected additional information into a format compatible with the mobile device and sends it to the device.

[0056] 6. Displaying Information to the User

[0057] Terminal: Displays the timetable and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[0058] User: Review the schedule and information displayed and adjust items as needed.

[0059] During normal use

[0060] 1. Registering a shopping list

[0061] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0062] 2. Send your shopping list

[0063] Terminal: Sends the entered shopping list to the server.

[0064] 3. Enter your destination and search for a route

[0065] User: Launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0066] 4. Sending route information

[0067] Device: The car navigation app sends the route information it has searched to the server.

[0068] 5. Matching routes and shopping lists

[0069] Server: Compares the received route information with the shopping list and searches for stores that can be visited along the way, taking into account store inventory and business hours.

[0070] 6. Submit your candidate stores

[0071] Server: Sends the best candidate store information to the device.

[0072] 7. Store suggestions to users

[0073] Terminal: Makes suggestions to the user based on the received store information (e.g., suggests the nearest supermarket to stop at on the way).

[0074] User: Review the suggested store information and decide whether to add the stop to their route.

[0075] In this way, a system can be constructed that provides efficient and convenient services to users. The present invention also provides a function that reflects weather forecasts and traffic information in schedules, providing users with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0076] The processing flow will be explained below.

[0077] When planning a trip

[0078] Step 1:

[0079] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0080] Step 2:

[0081] The terminal formats the input destination and departure date and time and sends a request to the server.

[0082] Step 3:

[0083] The server analyzes the received requests and uses generative artificial intelligence to create a time schedule, taking into account travel times, tourist attractions, and dining options.

[0084] Step 4:

[0085] The server collects the necessary information from the gourmet information API, tourist information API, and various databases and reflects it in the time schedule.

[0086] Step 5:

[0087] The server formats the generated time schedule and additional information in a format compatible with the mobile terminal and transmits them to the terminal.

[0088] Step 6:

[0089] The device displays the received time schedule and additional information to the user, such as pins on a map or visually displaying planned visits by time period.

[0090] Step 7:

[0091] The user reviews the presented schedule and information and makes any necessary adjustments.

[0092] During normal use

[0093] Step 1:

[0094] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0095] Step 2:

[0096] The terminal sends the shopping list to the server.

[0097] Step 3:

[0098] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0099] Step 4:

[0100] The device sends the route information searched by the car navigation app to the server.

[0101] Step 5:

[0102] The server analyzes the route information and shopping list received and searches for suitable stores along the route, taking into account store inventory and business hours.

[0103] Step 6:

[0104] The server transmits the best candidate store information to the terminal.

[0105] Step 7:

[0106] Based on the store information received by the device, the device suggests possible stores for the user to stop by (e.g., presenting the nearest supermarket that can be stopped at on the way).

[0107] Step 8:

[0108] The user checks the presented store information, selects whether to stop at the store as a stopover, and updates the route.

[0109] In this way, users can efficiently plan their trips and also effectively carry out their daily shopping.

[0110] Example 1

[0111] 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."

[0112] In order to efficiently plan trips and manage daily activities, a system that links mobile devices and servers is needed. However, existing systems require users to manually create schedules and individually search for information about the area around their destination, which requires time and effort. Furthermore, systems that suggest optimal stores based on shopping lists and travel routes are inadequate, making it difficult for users to shop efficiently. Furthermore, the lack of a function to dynamically adjust the generated schedule by reflecting weather forecasts and traffic information makes it difficult to create accurate plans.

[0113] 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.

[0114] In this invention, the server includes means for creating a time schedule using a generative AI model based on received information, means for collecting information about the area around the destination from an external system, and means for transmitting the generated schedule and collected information to a mobile device. This allows users to efficiently plan their trip. It also provides information on optimal stopover stores based on the shopping list and route information entered into the navigation app, enabling efficient support for daily activities. Furthermore, by dynamically adjusting the generated time schedule to reflect weather forecasts and traffic information, it is possible to provide highly accurate plans.

[0115] A "destination" is a geographical location that a user sets as a destination in their travel plans or daily activities.

[0116] The "departure date and time" is the date and time that the user sets as the start time of the travel plan or activity.

[0117] A "mobile terminal" is an electronic device that a user can carry around, and includes smartphones, tablets, and the like.

[0118] A "server" is a computer system on a network that processes and stores data and communicates with external systems.

[0119] A "generative AI model" is an artificial intelligence system that automatically generates time schedules and proposals based on input information from users.

[0120] A "time schedule" is a timetable that a user plans to follow when traveling or performing daily activities.

[0121] An "external system" is an information providing system managed by a third party that the server accesses and uses to collect information.

[0122] "Gourmet information" is information about restaurants, including types of food, ratings, addresses, etc.

[0123] "Tourist information" refers to information about tourist spots, including the characteristics of tourist spots and access information.

[0124] A "shopping list" is a list of items that a user wishes to purchase.

[0125] A "navigation app" is software that users use to search for and navigate their way to a destination.

[0126] "Route information" refers to information about the route from the departure point to the destination provided by the navigation app.

[0127] "Store information" refers to information about the location, opening hours, stock status, etc. of shops and supermarkets.

[0128] A "weather forecast" is information predicting future weather conditions in a particular area.

[0129] "Traffic information" refers to information about road congestion and the operation status of public transportation.

[0130] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. The system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server uses a generative AI model to propose optimal schedules and stopover stores, and provides this information to the user.

[0131] When planning a trip

[0132] First, a user launches a travel planning app on their smartphone and inputs their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00). This information is sent from the device to the server. The server analyzes the received information and uses a generative AI model (e.g., GPT-4 (registered trademark)) to create an optimal time schedule for the user's travel plans. Specifically, the following prompt sentence is input into the generative AI model:

[0133] Example prompt sentence:

[0134] "I'm planning a trip to Kyoto. I'll be leaving on August 15th at 10:00. Can you suggest some tourist spots and good restaurants?"

[0135] Next, the server collects information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule. This information includes details of planned tourist spots and restaurants. The server sends the generated time schedule and collected information to the mobile device, which displays it to the user in an easy-to-understand format. The user can check the displayed schedule and adjust each item as necessary.

[0136] During normal use

[0137] The user enters a shopping list (e.g., milk, bread, eggs) into a reminder app (e.g., GOOGLE KEEP®). This shopping list is sent from the device to the server. Next, the user launches a navigation app (e.g., Google® Maps), enters a destination (e.g., from home to office), and searches for a route. This route information is also sent from the device to the server. The server compares the received route information with the shopping list and searches for the best stores to stop at along the route. Specifically, it generates a prompt sentence like the following:

[0138] Example prompt sentence:

[0139] "I've added milk, bread, and eggs to my shopping list. Can you tell me the best store where I can buy these items on my way from my home to the office?"

[0140] The server uses an external system (e.g., store information API) to check inventory information and business status and select the most suitable candidate store. This store information is sent to the mobile device, which then makes suggestions to the user. The user can then check the suggested store information and decide which store to visit.

[0141] In this way, the system of the present invention provides efficient and convenient travel planning and daily activity support for users. The server also has the ability to reflect weather forecasts and traffic information in the time schedule it generates, providing even more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Process steps when planning a trip

[0144] Step 1:

[0145] User: Launches a travel planning app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0146] Input: Destination and departure date and time information

[0147] Output: Input data ready to send

[0148] Specific operation: The user enters the required information into the input form and presses the "Submit" button.

[0149] Step 2:

[0150] Terminal: Generates request data including the input destination and departure date and time, and sends it to the server.

[0151] Input: User-entered destination and departure date and time information

[0152] Output: Request for destination and departure date and time sent to server

[0153] What happens: The app formats the request data into JSON format and sends it as a POST request to the API endpoint.

[0154] Step 3:

[0155] Server: Analyzes the received request data and creates and inputs prompts to the generative AI model (e.g., GPT-4) to generate the optimal time schedule.

[0156] Input: Request data in JSON format

[0157] Output: Prompt sentences to be input to the generative AI model and the generated time schedule

[0158] Specific operation: The server analyzes the request data and generates a prompt such as, "I'm planning a trip to Kyoto. I'll be leaving at 10:00 on August 15th. Please suggest tourist spots and good restaurants." and inputs this into the AI ​​model.

[0159] Step 4:

[0160] Server: Collects additional information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule.

[0161] Input: Generated time schedule

[0162] Output: Appended time schedule

[0163] Specific operation: The server calls an API from an external system, obtains information about tourist spots and gourmet restaurants, and adds it to the time schedule.

[0164] Step 5:

[0165] Server: The created time schedule and collected additional information are formatted into a format that can be displayed on a mobile device (e.g., JSON format) and sent to the device.

[0166] Input: Added time schedule and additional information

[0167] Output: JSON formatted time schedule with additional information ready to send

[0168] Specific operation: The server formats the data into JSON format and returns it to the mobile device as a response.

[0169] Step 6:

[0170] Terminal: Displays the received time schedule and information on tourist spots and gourmet restaurants in a format that is easy for the user to understand.

[0171] Input: JSON format time schedule and additional information

[0172] Output: A timetable display including information on tourist attractions and places to eat

[0173] What it does: The app parses the JSON data and displays it to the user in a list or map format.

[0174] Step 7:

[0175] User: Review the schedule and information displayed and adjust items as needed.

[0176] Input: Displayed time schedule and information

[0177] Output: Modified time schedule

[0178] Specific operation: The user checks each item in the schedule and, if necessary, presses the edit button to make changes.

[0179] Normal usage process steps

[0180] Step 1:

[0181] User: Enters a shopping list (e.g., milk, bread, eggs) into a reminders app.

[0182] Input: Shopping list

[0183] Output: Input data saved

[0184] Specific behavior: The user enters the name of the desired product into the input field of the Reminders app and presses the "Save" button.

[0185] Step 2:

[0186] Terminal: Generates request data including the input shopping list and sends it to the server.

[0187] Input: Shopping list

[0188] Output: Shopping list request data ready to send

[0189] What happens: The app formats the reminder data into JSON format and sends it to the server as a POST request.

[0190] Step 3:

[0191] User: Opens a navigation app and enters a route from home to office to search.

[0192] Input: Origin and destination information

[0193] Output: Route information search results

[0194] Specific action: A user enters the locations of "home" and "office" in the search bar of a navigation app and presses the "Search" button.

[0195] Step 4:

[0196] Terminal: The route information is converted into JSON format and sent to the server.

[0197] Input: Route information

[0198] Output: Route information request data ready to send

[0199] Specific behavior: The app formats the route information of the search results into JSON format and sends it to the server as a POST request.

[0200] Step 5:

[0201] Server: Matches the received route information with the shopping list and searches for the best stores to stop at.

[0202] Input: Route information and shopping list

[0203] Output: List of candidate stores

[0204] Specific operation: Based on the route information and shopping list, the server uses an external store information API to search for the most suitable store and checks inventory and business status.

[0205] Step 6:

[0206] Server: Generates and sends JSON data to send information on the best candidate stores to the mobile device.

[0207] Input: List of potential stores

[0208] Output: Potential store data in JSON format

[0209] Specific operation: The server formats the information about the selected store into JSON format and sends it as a response to the mobile device.

[0210] Step 7:

[0211] Terminal: Analyzes the received information on candidate stores and makes suggestions to the user.

[0212] Input: JSON format store candidate data

[0213] Output: Display of candidate stores

[0214] Specific behavior: The app parses the JSON data and displays candidate stores to the user in map and list format.

[0215] Step 8:

[0216] User: Review the suggested store information and decide whether to add the stop to their route.

[0217] Input: Information about the candidate store

[0218] Output: Final route decision

[0219] Specific operation: The user checks the displayed store information and, if necessary, presses the "Stop" button to add the location to the route.

[0220] This concretely executes the processing steps of the entire system, allowing users to efficiently manage their travel plans and daily activities.

[0221] (Application example 1)

[0222] 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."

[0223] Currently, there are many travel planning support systems and daily activity support systems, but these systems only generate static schedules based on data entered by the user. Furthermore, systems that can provide efficient route guidance and schedule adjustments in real time when users travel using autonomous vehicles are not yet widely available. Therefore, the challenge is to efficiently optimize travel plans and daily activities in cooperation with autonomous vehicles.

[0224] 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.

[0225] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile device, means for transmitting the input destination and departure date and time to the server, means for generating a time schedule using generative artificial intelligence based on the information received by the server, means for the server to collect information about the area around the destination, means for transmitting the schedule generated by the server and the collected information to the mobile device, means for the mobile device to display the schedule and information to the user, and means for transmitting route information and departure times to the autonomously driven vehicle based on the generated schedule. This enables a user to easily create a travel plan or schedule of daily activities using the mobile device and give instructions to the autonomously driven vehicle based on the schedule to efficiently travel and make stops.

[0226] A "mobile terminal" refers to an electronic device that a user can carry around and that can input and display information.

[0227] "Destination" refers to the final location where a user travels or moves.

[0228] "Departure date and time" refers to the date and time when the user starts traveling or moving.

[0229] "Server" refers to a central computer system that receives and processes data sent by users.

[0230] "Generative artificial intelligence" refers to artificial intelligence that can generate new information and schedules based on large amounts of data.

[0231] A "time schedule" refers to a plan that organizes a user's movements and activities along a time axis.

[0232] "Route information" refers to the route from the departure point to the destination and detailed information on the route.

[0233] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[0234] "Weather forecast" refers to a service that predicts future weather and provides that information.

[0235] "Traffic information" refers to real-time information about travel, such as road congestion, accident information, and public transportation operation status.

[0236] The present invention provides a system that efficiently supports users in planning their travels and daily activities by linking a mobile terminal and a server. Specific embodiments of the system are described below.

[0237] System configuration

[0238] 1. Mobile devices

[0239] An electronic device that users can carry around and that allows them to input and display information, such as a smartphone or tablet.

[0240] 2. Server

[0241] It is a central computer system that receives and processes data sent by users, and uses generative artificial intelligence to create time schedules and collect information.

[0242] 3. Generative Artificial Intelligence

[0243] This is an artificial intelligence that can generate new information and schedules based on large amounts of data. Specifically, OpenAI (registered trademark) and GPT-3 (registered trademark) are used.

[0244] 4. Self-driving vehicles

[0245] It is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[0246] Data entry and processing

[0247] 1. Travel Planning

[0248] The user uses the mobile terminal to input the travel destination and departure date and time.

[0249] The terminal transmits the input destination and departure date and time to the server.

[0250] The server uses generative artificial intelligence to create a time schedule based on the received information, including travel time and tourist spots and restaurants to visit after arriving at the destination.

[0251] The server collects additional information using tourist information APIs and gourmet information APIs around the destination and reflects this information in the time schedule.

[0252] The server transmits the generated schedule and the collected information to the mobile terminal.

[0253] The terminal displays schedules and information in a user-friendly format.

[0254] The terminal transmits route information and departure times to the autonomous vehicle based on the generated schedule, so that the autonomous vehicle departs according to the schedule.

[0255] Specific examples

[0256] If a user selects "Tokyo Disneyland" as their destination and sets the departure date and time as "September 10th, 9:00 AM," the system will optimize the user's travel plan based on that information and send instructions to the self-driving vehicle. An example of the prompt text is as follows:

[0257] Destination: Tokyo Disneyland

[0258] Departure date and time: September 10, 2023 09:00

[0259] Data entry and processing (shopping)

[0260] 1. Shopping plan

[0261] The user inputs a shopping list into the reminder app and sends it to the server using the device.

[0262] The user inputs a destination into the car navigation app and searches for a route, which is then sent to the server.

[0263] The server compares the received route information with the shopping list and searches for information on the best stores to stop at along the route.

[0264] The server transmits information on the best stores to visit to the mobile terminal.

[0265] The terminal displays the suggested store information to the user.

[0266] Program processing

[0267] The hardware includes servers and mobile terminals, and it is desirable for the servers to have high-performance processors and large amounts of memory.

[0268] The software includes generative artificial intelligence (e.g., OpenAI GPT-3), tourist information API, gourmet information API, navigation system, reminder app, and more.

[0269] Data processing and data calculation include schedule generation by the server, information collection using API, and analysis of user input data.

[0270] This allows users to easily create travel plans and schedules of daily activities using a mobile device, and then give instructions to an autonomous vehicle based on that schedule to travel and make stops efficiently.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] The user inputs the travel destination and departure date and time using the mobile device. The data (prompt sentences) input by the user are saved in the mobile device.

[0274] Step 2:

[0275] The device sends the entered destination and departure date and time data to the server. Specifically, the device creates an HTTP POST request and sends it to a specific endpoint on the server. The input in this step is the user's input data, and the output is the request data sent to the server.

[0276] Step 3:

[0277] The server uses generative artificial intelligence (e.g., OpenAI GPT-3) to create a time schedule based on the information it receives. Specifically, the server uses a generative AI model to generate an optimal schedule based on the user's input (prompt). The input in this step is the destination and departure date and time sent from the device, and the output is the generated time schedule.

[0278] Step 4:

[0279] The server collects information about the area around the destination. Specifically, it uses the tourist information API and gourmet information API to obtain information about tourist spots and restaurants. The input in this step is information about the departure point and destination, and the output is the obtained tourist information and gourmet information.

[0280] Step 5:

[0281] The server sends the generated time schedule and collected information to the mobile device. The server formats this data into a specific format and sends it to the device using an HTTP POST request. The input in this step is the generated time schedule and collected information, and the output is the schedule data sent to the mobile device.

[0282] Step 6:

[0283] The terminal displays the schedule and information to the user. The terminal analyzes the received data and displays it in a format that is easy for the user to understand. For example, it displays the names of tourist attractions, visiting times, restaurant recommendations, etc. The input in this step is the schedule data sent from the server, and the output is the schedule information displayed to the user.

[0284] Step 7:

[0285] The terminal sends route information and departure times to the autonomous vehicle based on the generated schedule. The terminal formats this data and sends it to the autonomous vehicle's control system. The input in this step is the generated time schedule, and the output is the route information and departure times sent to the autonomous vehicle.

[0286] Step 8:

[0287] The autonomous vehicle departs according to the schedule and travels along the optimal route. The vehicle's control system starts the vehicle's operation based on the received route information and departure time. The input in this step is the route information and departure time sent from the terminal, and the output is the autonomous vehicle's driving behavior.

[0288] 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.

[0289] This invention is a system that combines a mobile device, a server, and an emotion engine to provide efficient and personalized support for travel planning and daily activities. The system begins when the user inputs information such as the destination, departure date and time, and shopping list on the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stopover stores, and provides this information to the user. Furthermore, the emotion engine can recognize the user's emotions and adjust the suggestions based on that information.

[0290] When planning a trip

[0291] 1. Enter your destination and departure date and time

[0292] User: Launches the app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0293] 2. Submit your input

[0294] Terminal: Formats the input destination and departure date and time and sends a request to the server.

[0295] 3. Generate a time schedule

[0296] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[0297] 4. Collection of additional information

[0298] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[0299] 5. Emotional Information Analysis

[0300] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[0301] 6. Tailor your offers based on emotions

[0302] Server: Based on the received emotional information, the server adjusts the generated time schedule and additional information according to the user's emotional state. For example, if the user is tired, the server may suggest more rest spots.

[0303] 7. Schedule and Information Submission

[0304] Server: Formats the generated time schedule and collected additional information into a format compatible with mobile devices and sends them to the devices.

[0305] 8. Displaying Information to the User

[0306] Terminal: Displays the time schedule and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[0307] User: Review the schedule and information displayed and make any necessary adjustments.

[0308] During normal use

[0309] 1. Registering a shopping list

[0310] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0311] 2. Send your shopping list

[0312] Terminal: Sends the entered shopping list to the server.

[0313] 3. Enter your destination and search for a route

[0314] User: Opens a navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0315] 4. Sending route information

[0316] Device: Sends route information searched by the car navigation app to the server.

[0317] 5. Matching routes and shopping lists

[0318] Server: Matches the received route information with the shopping list and searches for suitable stores along the route, taking into account store inventory and opening hours.

[0319] 6. Submit your candidate stores

[0320] Server: Sends the best candidate store information to the device.

[0321] 7. Store suggestions to users

[0322] Terminal: Based on the received store information, the terminal suggests possible stores for the user to stop at (e.g., presenting the nearest supermarket that can be stopped at on the way).

[0323] 8. Emotional Information Analysis

[0324] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[0325] 9. Adjust your offers based on emotions

[0326] Server: Based on the received emotion information, the server adjusts the suggested stores to stop at and the shopping items to be purchased. For example, if the user is in a hurry, the server suggests the most efficient route and stores.

[0327] 10. Final User Confirmation

[0328] User: Review the suggested information, choose whether to include the stop as a stop, and update the route.

[0329] In this way, a system can be constructed that provides efficient and personalized services to users. In this invention, the emotion engine is used to make suggestions based on the user's emotional state, providing the user with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0330] The processing flow will be explained below.

[0331] When planning a trip

[0332] Step 1:

[0333] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0334] Step 2:

[0335] The terminal formats the input destination and departure date and time information and sends a request to the server.

[0336] Step 3:

[0337] The server analyzes the received request and uses generative artificial intelligence to create a time schedule, taking into account travel time from the user's departure point to their destination, as well as recommended tourist spots and dining options.

[0338] Step 4:

[0339] The server collects information about tourist spots and gourmet restaurants around the destination from the gourmet information API, tourist information API, and other databases, and reflects this information in the time schedule.

[0340] Step 5:

[0341] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice.

[0342] Step 6:

[0343] The device formats the acquired emotional information and sends it to the server.

[0344] Step 7:

[0345] Based on the emotional information received from the emotion engine, the server adjusts the generated time schedule and suggestions according to the user's emotional state. For example, if the user is tired, it will suggest more rest spots.

[0346] Step 8:

[0347] The server transmits the adjusted time schedule and additional information to the mobile terminal.

[0348] Step 9:

[0349] The device displays the schedule and information received in a user-friendly format (e.g., tourist spots and their visiting times, gourmet restaurant recommendations, etc.).

[0350] Step 10:

[0351] The user reviews the presented schedule and information and makes any necessary adjustments.

[0352] During normal use

[0353] Step 1:

[0354] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0355] Step 2:

[0356] The terminal formats the input shopping list and sends it to the server.

[0357] Step 3:

[0358] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0359] Step 4:

[0360] The device formats the route information searched by the car navigation app and sends it to the server.

[0361] Step 5:

[0362] The server compares the route information received with the shopping list and searches for suitable stores along the route, taking into account store inventory and business hours.

[0363] Step 6:

[0364] The server formats the best candidate store information and sends it to the terminal.

[0365] Step 7:

[0366] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, while driving, the car navigation system may say, "You seem a little tired today."

[0367] Step 8:

[0368] The device formats the acquired emotional information and sends it to the server.

[0369] Step 9:

[0370] The server adjusts the suggested stores to stop by and shopping contents based on the emotional information received from the emotion engine. For example, if the user is in a hurry, it suggests the most efficient route and stores.

[0371] Step 10:

[0372] The device displays tailored store information in a user-friendly format (e.g., showing the nearest supermarket to stop at on the way).

[0373] Step 11:

[0374] The user checks the information presented, selects whether to stop at the destination as a stopover, and updates the route.

[0375] In this way, users can plan their trips and make everyday purchases more efficient and personalized.

[0376] Example 2

[0377] 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."

[0378] Conventional travel planning and daily activity support systems do not take into account the user's emotional state when making suggestions, and therefore are often not optimized to the user's actual needs or physical condition. Furthermore, there is a problem in optimizing travel plans and shopping routes, in that they are unable to perform real-time emotional analysis and dynamic adjustments based on that analysis.

[0379] 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.

[0380] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile information processing device, means for transmitting the input destination and departure date and time to the server, means for the server to create a time schedule using generative artificial intelligence based on the information received, means for the server to collect additional information about the area around the destination, means for the server to transmit the schedule generated by the server and the collected additional information to the information processing device, means for the information processing device to display the schedule and additional information to the user, means for the information processing device to analyze the user's facial expressions and voice, and means for the server to adjust the content of suggestions based on the user's emotional information. This makes it possible to suggest optimal travel plans and shopping routes based on the user's emotional state.

[0381] A "portable information processing device" is a device with computing capabilities that is portable and allows a user to input and send and receive information.

[0382] A "destination" refers to a location where a user is heading in a trip or travel.

[0383] "Departure date and time" refers to the date and time when a user starts a specific trip or movement.

[0384] A "server" is a central processing unit that works in conjunction with information processing units via a network and receives, analyzes, and transmits information.

[0385] "Generative artificial intelligence" refers to algorithms and models that dynamically generate information to provide to users based on large amounts of data.

[0386] A "time schedule" refers to a list of activities scheduled based on time in a particular plan.

[0387] "Additional information" refers to detailed information about a destination or route, or supplementary information relevant to the user.

[0388] "Analysis of facial expressions and voice" refers to the process of analyzing the user's face and voice to estimate their emotional state.

[0389] "Emotion information" refers to data that indicates the analyzed emotional state of the user.

[0390] "Adjusting the proposal content" refers to the process of optimizing the plan and proposal content provided based on the user's emotional information.

[0391] MODE FOR CARRYING OUT THE INVENTION

[0392] This invention provides a system that uses a mobile information processing device, a server, and an emotion analysis engine to provide efficient and personalized support for travel planning and daily activities. Specific implementation methods are described below.

[0393] System configuration

[0394] The system includes the following components:

[0395] 1. Portable information processing device:

[0396] For example, a smartphone or tablet is used, and users use these devices to input information such as their destination and departure date and time.

[0397] Use trip planning apps, reminder apps, and car navigation apps to input destinations, departure dates and times, shopping lists, route information, etc.

[0398] 2. Server:

[0399] The server cooperates with the information processing device via the network to receive, analyze, and transmit information on the destination and departure date and time.

[0400] As a generative artificial intelligence, for example, we use OpenAI's GPT model, which automatically generates a time schedule.

[0401] We use external APIs such as Google Places API and Yelp API to collect information on gourmet food and tourist spots around the destination.

[0402] 3. Sentiment Analysis Engine:

[0403] To analyze the user's facial expressions and voice, we use the Emotion API from Microsoft's Azure Cognitive Services, which acquires the user's emotional information and sends it to the server.

[0404] Example: Travel Planning

[0405] The following steps are taken by a user using a travel planning app on their smartphone to plan a trip to Kyoto on August 15th.

[0406] 1. Enter your destination and departure date and time:

[0407] The user launches the app on their smartphone and enters "Destination: Kyoto, Departure date and time: August 15th, 10:00."

[0408] 2. Generate time schedule:

[0409] The server uses the received information to generate a time schedule using the GPT model, which includes travel time from the departure point to the destination and tourist spots to visit after arriving.

[0410] 3. Additional Information Collection:

[0411] The server uses the Google Places API and Yelp API to collect information about tourist attractions and restaurants around the destination and reflects this information in the timetable.

[0412] 4. Emotional information analysis:

[0413] The smartphone analyzes the user's voice and facial expressions and sends emotional information to the server. If the analysis finds that the user is "tired," that information is sent to the server.

[0414] 5. Tailor your offers based on emotions:

[0415] The server adds more rest spots to the schedule based on the emotion information and provides it to the user, enabling personalized suggestions according to the user's state.

[0416] Example: Everyday shopping

[0417] The following steps show how a user can use a smartphone reminder app and a car navigation app to efficiently shop while on the move.

[0418] 1. Registering a shopping list:

[0419] A user enters "Shopping list: milk, bread, eggs" into a Reminders app.

[0420] 2. Sending route information:

[0421] The user enters "destination: office" into the car navigation app and searches for the optimal route.

[0422] 3. Proposal of candidate stores:

[0423] The server compares the received route information with the shopping list, searches for the best store information along the route, and sends the selected store information to the user's smartphone.

[0424] 4. Emotional information analysis:

[0425] The smartphone analyzes the user's voice and facial expressions, and if it determines that the user is in a hurry, it sends that information to the server.

[0426] 5. Tailor your offers based on emotions:

[0427] The server then uses the emotion information to suggest the most efficient route and the nearest store. For example, if the user feels "in a hurry," it will suggest a convenience store on the shortest route.

[0428] Prompt Sentence Examples

[0429] Below are some examples of specific travel planning prompts:

[0430] Travel Planner

[0431] Destination: Kyoto

[0432] Departure date and time: August 15th, 10:00

[0433] User Emotion: Tired

[0434] Generate optimal travel itineraries based on user requests and include plenty of rest stops.

[0435] Below are some examples of specific prompts for everyday shopping:

[0436] Regular use

[0437] Shopping list: milk, bread, eggs

[0438] Destination: Office

[0439] Route: Home to office

[0440] User Sentiment: Urgent

[0441] Suggest the best shopping store and route.

[0442] As described above, this system can provide optimal suggestions based on the user's condition and needs, enabling efficient and personalized travel planning and support for daily activities.

[0443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0444] Travel planning process

[0445] Step 1:

[0446] A user launches a travel planning app on their smartphone and inputs a destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00 AM) by typing the information into the app's text fields and pressing the submit button.

[0447] Input: Destination and departure date and time

[0448] Output: None (preparing input data)

[0449] Step 2:

[0450] The device formats the destination and departure date and time entered by the user and sends it to the server via an HTTP POST request, which is sent as JSON-formatted data.

[0451] Input: User input data (destination and departure date and time)

[0452] Output: Request sent to server

[0453] Step 3:

[0454] The server analyzes the received requests and generates a time schedule using generative artificial intelligence (GPT model). The server analyzes the received data and creates a schedule that takes into account travel time and tourist spots to visit after arrival.

[0455] Input: Request from the device (destination and departure date and time)

[0456] Output: Generated time schedule

[0457] Step 4:

[0458] The server uses the Google Places API, Yelp API, etc. to collect information on tourist spots and gourmet restaurants around the destination, analyzes the information obtained from the API, and adds it to the generated time schedule.

[0459] Input: Destination information

[0460] Output: Added tourist spots and gourmet restaurant information

[0461] Step 5:

[0462] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[0463] Input: User's voice and facial expression data

[0464] Output: Parsed emotion information

[0465] Step 6:

[0466] The server adjusts the generated time schedule and additional information based on the received emotion information. For example, if the server determines that the user is "tired," it adds more rest spots to the schedule.

[0467] Input: Emotion information

[0468] Output: Adjusted time schedule and additional information

[0469] Step 7:

[0470] The server then formats the final generated time schedule and additional information into JSON format and sends it back to the terminal.

[0471] Input: Adjusted time schedule and additional information

[0472] Output: Data sent to the terminal

[0473] Step 8:

[0474] The device displays the schedule and information received from the server in an easy-to-understand format to the user, such as a list of tourist spots to visit, the times to visit them, and recommended restaurants.

[0475] Input: Data sent from the server

[0476] Output: Schedules and information displayed to the user

[0477] Processing during normal use

[0478] Step 1:

[0479] A user enters a shopping list (e.g., milk, bread, eggs) into a reminder app, which stores the list in a local database and prepares it for sending.

[0480] Input: Shopping list item

[0481] Output: None (preparing input data)

[0482] Step 2:

[0483] The terminal sends the shopping list entered by the user to the server via an HTTP POST request. This list is sent as JSON format data.

[0484] Input: A shopping list entered by the user

[0485] Output: Request sent to server

[0486] Step 3:

[0487] A user starts a car navigation app, inputs a destination (e.g., an office), and searches for a route. This information is processed within the car navigation app, and the route information provided is generated.

[0488] Input: Destination information

[0489] Output: Route information

[0490] Step 4:

[0491] The device sends route information searched by the car navigation app to the server, which is also sent in JSON format.

[0492] Input: Route information

[0493] Output: Request sent to server

[0494] Step 5:

[0495] The server compares the received route information with the shopping list and searches for appropriate stores along the route, taking into account each store's inventory and business hours.

[0496] Input: Route information and shopping list

[0497] Output: List of optimal store candidates

[0498] Step 6:

[0499] The server formats the best candidate store information in JSON format and sends it to the terminal.

[0500] Input: List of optimal store candidates

[0501] Output: Data sent to the terminal

[0502] Step 7:

[0503] The device will then suggest possible stores to the user based on the received store information, and the app screen will display the nearest supermarkets that the user can stop at along the way.

[0504] Input: Data sent from the server

[0505] Output: Store suggestions to the user

[0506] Step 8:

[0507] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[0508] Input: User's voice and facial expression data

[0509] Output: Parsed emotion information

[0510] Step 9:

[0511] The server then adjusts the suggested stores to stop by and the shopping items to be purchased based on the received emotional information. For example, if the user is in a hurry, it will suggest the most efficient route and stores.

[0512] Input: Emotion information

[0513] Output: Adjusted proposal

[0514] Step 10:

[0515] The user reviews the proposed information, selects whether to include stops as intermediate locations, and updates the route. Once the user gives final approval, the system resends the updated information to the server.

[0516] Input: User selection and confirmation

[0517] Output: The final route

[0518] (Application example 2)

[0519] 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."

[0520] Conventional travel planning and daily shopping route guidance systems have difficulty responding to the user's emotional state and real-time environmental changes. In particular, in autonomous vehicles, optimal route suggestions and selection of stops often do not take the driver's emotional state into account, resulting in a loss of comfort and convenience for the user. Furthermore, fixed route suggestions without understanding the driver's emotional state can increase the user's stress and fatigue.

[0521] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a destination and departure date and time on a mobile terminal; a means for transmitting the input destination and departure date and time to the server; a means for the server to create a time schedule using a generation AI system based on the information received; a means for the server to collect information about the area around the destination; a means for the server to transmit the schedule generated by the server and the collected information to the mobile terminal; a means for the mobile terminal to analyze the user's emotional state; a means for the server to adjust the schedule based on the user's emotional information; and a means for the mobile terminal to display the schedule and information to the user visually or audibly. This makes it possible to grasp the user's emotional state in real time and suggest optimal routes and stops based on that state.

[0522] "Mobile terminal" refers to a relocatable, portable electronic device that allows a user to input information and exchange information with a server.

[0523] A "generative AI system" is an artificial intelligence technology that has the ability to generate and predict appropriate schedules and routes based on the information it receives.

[0524] A "time schedule" is a specific action plan that organizes the user's planned activities by date and time.

[0525] "Emotion information" is information about the emotional state obtained by analyzing data such as the user's facial expressions and voice.

[0526] "Means for visually or audibly displaying schedules and information" means the functionality of a mobile terminal to provide information to a user through a screen display or audio output.

[0527] A "stop-by store" is a store or facility that a user visits temporarily while traveling, and is a place used for shopping, resting, etc.

[0528] A "server" is a central processing unit that receives requests from clients (for example, mobile terminals) via a network and processes and provides information.

[0529] The system for implementing this invention mainly comprises a mobile terminal, a server, a generative AI system, and an emotion analysis engine. A specific implementation method will be described in detail below.

[0530] First, the user inputs the travel destination and departure date and time into a mobile device. The mobile device can be a smartphone, tablet, or in-car display. The input information is then sent to a server via a network. The server then uses a pre-designed generative AI system to create an optimal time schedule based on the received destination and departure date and time.

[0531] The generative AI system optimizes schedules in real time, taking into account information such as travel time from the user's departure point to their destination, traffic conditions, weather forecasts, and tourist spots they want to visit. It also obtains gourmet and tourist information around the destination through external APIs and provides it to users.

[0532] Next, the mobile device uses its built-in camera and microphone to analyze the user's facial expressions and voice to obtain emotional information. This process is performed using an emotion analysis engine, such as Microsoft Azure's Emotion API. The analyzed emotional information is then sent back to the server, where the schedule generated on the server side is adjusted to match the user's emotional state.

[0533] For example, if the user is tired, the system will suggest more spots for rest and relaxation, while if the user is energetic, it will dynamically adjust the places to visit, suggesting more active tourist spots.

[0534] The final time schedule is then sent to the mobile device and presented to the user visually or audibly. Visual display is provided on the smartphone screen or the vehicle display, while audio guidance is provided through the audio speaker.

[0535] As a concrete example, the following prompt sentences are input into a generative AI model to generate a schedule:

[0536] "The departure point is the current location, the destination is Tokyo Station, and the departure time is 10:00 on August 15, 2023. The user is in a calm emotional state. Based on this state, please generate the optimal route that includes a stop at the museum."

[0537] In this way, a user of the present invention can obtain an optimal travel plan that matches his or her emotional state, and can realize more comfortable and efficient travel.

[0538] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0539] Step 1:

[0540] The user inputs the travel destination and departure date and time on the mobile terminal.

[0541] Input: The destination (e.g. Tokyo Station) and departure date and time (e.g. August 15, 2023, 10:00) entered by the user.

[0542] Output: Input data stored on the mobile device.

[0543] Step 2:

[0544] The mobile terminal transmits the input destination and departure date and time to the server.

[0545] Input: Destination and departure date and time data obtained in step 1.

[0546] Output: The request data sent to the server.

[0547] Step 3:

[0548] Based on the information received by the server, a time schedule is created using a generative AI model.

[0549] Input: Destination and departure date / time data sent in step 2.

[0550] Processing: The generative AI model generates a schedule taking into account travel time from the user's origin to their destination, traffic information, and weather forecasts.

[0551] Output: The generated time schedule data.

[0552] Step 4:

[0553] The server collects information about the area around the destination.

[0554] Input: Destination information.

[0555] Processing: The server collects information on tourist spots and gourmet restaurants around the destination through the gourmet information API and tourist information API.

[0556] Output: Collected tourist spot and gourmet restaurant information.

[0557] Step 5:

[0558] The mobile device uses a built-in camera and microphone to analyze the user's emotional state and transmit it to a server.

[0559] Input: User's facial and voice data.

[0560] Processing: Analyze emotional information using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[0561] Output: Parsed emotion information is sent to the server.

[0562] Step 6:

[0563] The server-generated schedule and collected information are adjusted based on the user's emotional state.

[0564] Input: Time schedule data generated in step 3, information collected in step 4, and emotion information sent in step 5.

[0565] Processing: Dynamically adjust the time schedule and suggested tourist spots based on emotion information. For example, if the user is tired, a schedule with more breaks will be suggested.

[0566] Output: Adjusted schedule and stop data.

[0567] Step 7:

[0568] The server transmits the generated schedule and collected information to the mobile terminal.

[0569] Input: Schedule and stop data adjusted in step 6.

[0570] Output: Transmitted to mobile device.

[0571] Step 8:

[0572] The mobile terminal displays the schedule and information visually and audibly to the user.

[0573] Input: Schedule and stop-off point data received from the server in step 7.

[0574] Processing: The mobile terminal provides information to the user through a screen display and audio output.

[0575] Output: The visual and audio information provided to the user.

[0576] 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.

[0577] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0578] 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.

[0579] [Second embodiment]

[0580] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0591] 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."

[0592] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. This system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stores to stop by, and provides this information to the user.

[0593] When planning a trip

[0594] 1. Enter your destination and departure date and time

[0595] User: Launches the app on their smartphone and enters their travel destination and departure date and time (e.g., Kyoto, departure August 15th at 10:00).

[0596] 2. Submit your input

[0597] Terminal: Creates a request to send the destination and departure date and time entered by the user to the server, and sends it to the server.

[0598] 3. Generate a time schedule

[0599] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[0600] 4. Collection of additional information

[0601] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[0602] 5. Submitting schedules and information

[0603] Server: Formats the generated time schedule and collected additional information into a format compatible with the mobile device and sends it to the device.

[0604] 6. Displaying Information to the User

[0605] Terminal: Displays the timetable and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[0606] User: Review the schedule and information displayed and adjust items as needed.

[0607] During normal use

[0608] 1. Registering a shopping list

[0609] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0610] 2. Send your shopping list

[0611] Terminal: Sends the entered shopping list to the server.

[0612] 3. Enter your destination and search for a route

[0613] User: Launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0614] 4. Sending route information

[0615] Device: The car navigation app sends the route information it has searched to the server.

[0616] 5. Matching routes and shopping lists

[0617] Server: Compares the received route information with the shopping list and searches for stores that can be visited along the way, taking into account store inventory and business hours.

[0618] 6. Submit your candidate stores

[0619] Server: Sends the best candidate store information to the device.

[0620] 7. Store suggestions to users

[0621] Terminal: Makes suggestions to the user based on the received store information (e.g., suggests the nearest supermarket to stop at on the way).

[0622] User: Review the suggested store information and decide whether to add the stop to their route.

[0623] In this way, a system can be constructed that provides efficient and convenient services to users. The present invention also provides a function that reflects weather forecasts and traffic information in schedules, providing users with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0624] The processing flow will be explained below.

[0625] When planning a trip

[0626] Step 1:

[0627] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0628] Step 2:

[0629] The terminal formats the input destination and departure date and time and sends a request to the server.

[0630] Step 3:

[0631] The server analyzes the received requests and uses generative artificial intelligence to create a time schedule, taking into account travel times, tourist attractions, and dining options.

[0632] Step 4:

[0633] The server collects the necessary information from the gourmet information API, tourist information API, and various databases and reflects it in the time schedule.

[0634] Step 5:

[0635] The server formats the generated time schedule and additional information in a format compatible with the mobile terminal and transmits them to the terminal.

[0636] Step 6:

[0637] The device displays the received time schedule and additional information to the user, such as pins on a map or visually displaying planned visits by time period.

[0638] Step 7:

[0639] The user reviews the presented schedule and information and makes any necessary adjustments.

[0640] During normal use

[0641] Step 1:

[0642] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0643] Step 2:

[0644] The terminal sends the shopping list to the server.

[0645] Step 3:

[0646] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0647] Step 4:

[0648] The device sends the route information searched by the car navigation app to the server.

[0649] Step 5:

[0650] The server analyzes the route information and shopping list received and searches for suitable stores along the route, taking into account store inventory and business hours.

[0651] Step 6:

[0652] The server transmits the best candidate store information to the terminal.

[0653] Step 7:

[0654] Based on the store information received by the device, the device suggests possible stores for the user to stop by (e.g., presenting the nearest supermarket that can be stopped at on the way).

[0655] Step 8:

[0656] The user checks the presented store information, selects whether to stop at the store as a stopover, and updates the route.

[0657] In this way, users can efficiently plan their trips and also effectively carry out their daily shopping.

[0658] Example 1

[0659] 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."

[0660] In order to efficiently plan trips and manage daily activities, a system that links mobile devices and servers is needed. However, existing systems require users to manually create schedules and individually search for information about the area around their destination, which requires time and effort. Furthermore, systems that suggest optimal stores based on shopping lists and travel routes are inadequate, making it difficult for users to shop efficiently. Furthermore, the lack of a function to dynamically adjust the generated schedule by reflecting weather forecasts and traffic information makes it difficult to create accurate plans.

[0661] 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.

[0662] In this invention, the server includes means for creating a time schedule using a generative AI model based on received information, means for collecting information about the area around the destination from an external system, and means for transmitting the generated schedule and collected information to a mobile device. This allows users to efficiently plan their trip. It also provides information on optimal stopover stores based on the shopping list and route information entered into the navigation app, enabling efficient support for daily activities. Furthermore, by dynamically adjusting the generated time schedule to reflect weather forecasts and traffic information, it is possible to provide highly accurate plans.

[0663] A "destination" is a geographical location that a user sets as a destination in their travel plans or daily activities.

[0664] The "departure date and time" is the date and time that the user sets as the start time of the travel plan or activity.

[0665] A "mobile terminal" is an electronic device that a user can carry around, and includes smartphones, tablets, and the like.

[0666] A "server" is a computer system on a network that processes and stores data and communicates with external systems.

[0667] A "generative AI model" is an artificial intelligence system that automatically generates time schedules and proposals based on input information from users.

[0668] A "time schedule" is a timetable that a user plans to follow when traveling or performing daily activities.

[0669] An "external system" is an information providing system managed by a third party that the server accesses and uses to collect information.

[0670] "Gourmet information" is information about restaurants, including types of food, ratings, addresses, etc.

[0671] "Tourist information" refers to information about tourist spots, including the characteristics of tourist spots and access information.

[0672] A "shopping list" is a list of items that a user wishes to purchase.

[0673] A "navigation app" is software that users use to search for and navigate their way to a destination.

[0674] "Route information" refers to information about the route from the departure point to the destination provided by the navigation app.

[0675] "Store information" refers to information about the location, opening hours, stock status, etc. of shops and supermarkets.

[0676] A "weather forecast" is information predicting future weather conditions in a particular area.

[0677] "Traffic information" refers to information about road congestion and the operation status of public transportation.

[0678] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. The system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server uses a generative AI model to propose optimal schedules and stopover stores, and provides this information to the user.

[0679] When planning a trip

[0680] First, the user launches a travel planning app on their smartphone and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00). This information is sent from the device to the server. The server analyzes the received information and uses a generative AI model (e.g., GPT-4) to create an optimal time schedule for the user's travel plans. Specifically, the following prompt sentence is input into the generative AI model:

[0681] Example prompt sentence:

[0682] "I'm planning a trip to Kyoto. I'll be leaving on August 15th at 10:00. Can you suggest some tourist spots and good restaurants?"

[0683] Next, the server collects information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule. This information includes details of planned tourist spots and restaurants. The server sends the generated time schedule and collected information to the mobile device, which displays it to the user in an easy-to-understand format. The user can check the displayed schedule and adjust each item as necessary.

[0684] During normal use

[0685] The user enters a shopping list (e.g., milk, bread, eggs) into a reminder app (e.g., Google Keep). This shopping list is sent from the device to the server. Next, the user launches a navigation app (e.g., Google Maps), enters a destination (e.g., from home to office), and searches for a route. This route information is also sent from the device to the server. The server compares the received route information with the shopping list and searches for the best stores to stop at along the route. Specifically, it generates a prompt sentence like the following:

[0686] Example prompt sentence:

[0687] "I've added milk, bread, and eggs to my shopping list. Can you tell me the best store where I can buy these items on my way from my home to the office?"

[0688] The server uses an external system (e.g., store information API) to check inventory information and business status and select the most suitable candidate store. This store information is sent to the mobile device, which then makes suggestions to the user. The user can then check the suggested store information and decide which store to visit.

[0689] In this way, the system of the present invention provides efficient and convenient travel planning and daily activity support for users. The server also has the ability to reflect weather forecasts and traffic information in the time schedule it generates, providing even more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0690] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0691] Process steps when planning a trip

[0692] Step 1:

[0693] User: Launches a travel planning app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0694] Input: Destination and departure date and time information

[0695] Output: Input data ready to send

[0696] Specific operation: The user enters the required information into the input form and presses the "Submit" button.

[0697] Step 2:

[0698] Terminal: Generates request data including the input destination and departure date and time, and sends it to the server.

[0699] Input: User-entered destination and departure date and time information

[0700] Output: Request for destination and departure date and time sent to server

[0701] What happens: The app formats the request data into JSON format and sends it as a POST request to the API endpoint.

[0702] Step 3:

[0703] Server: Analyzes the received request data and creates and inputs prompts to the generative AI model (e.g., GPT-4) to generate the optimal time schedule.

[0704] Input: Request data in JSON format

[0705] Output: Prompt sentences to be input to the generative AI model and the generated time schedule

[0706] Specific operation: The server analyzes the request data and generates a prompt such as, "I'm planning a trip to Kyoto. I'll be leaving at 10:00 on August 15th. Please suggest tourist spots and good restaurants." and inputs this into the AI ​​model.

[0707] Step 4:

[0708] Server: Collects additional information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule.

[0709] Input: Generated time schedule

[0710] Output: Appended time schedule

[0711] Specific operation: The server calls an API from an external system, obtains information about tourist spots and gourmet restaurants, and adds it to the time schedule.

[0712] Step 5:

[0713] Server: The created time schedule and collected additional information are formatted into a format that can be displayed on a mobile device (e.g., JSON format) and sent to the device.

[0714] Input: Added time schedule and additional information

[0715] Output: JSON formatted time schedule with additional information ready to send

[0716] Specific operation: The server formats the data into JSON format and returns it to the mobile device as a response.

[0717] Step 6:

[0718] Terminal: Displays the received time schedule and information on tourist spots and gourmet restaurants in a format that is easy for the user to understand.

[0719] Input: JSON format time schedule and additional information

[0720] Output: A timetable display including information on tourist attractions and places to eat

[0721] What it does: The app parses the JSON data and displays it to the user in a list or map format.

[0722] Step 7:

[0723] User: Review the schedule and information displayed and adjust items as needed.

[0724] Input: Displayed time schedule and information

[0725] Output: Modified time schedule

[0726] Specific operation: The user checks each item in the schedule and, if necessary, presses the edit button to make changes.

[0727] Normal usage process steps

[0728] Step 1:

[0729] User: Enters a shopping list (e.g., milk, bread, eggs) into a reminders app.

[0730] Input: Shopping list

[0731] Output: Input data saved

[0732] Specific behavior: The user enters the name of the desired product into the input field of the Reminders app and presses the "Save" button.

[0733] Step 2:

[0734] Terminal: Generates request data including the input shopping list and sends it to the server.

[0735] Input: Shopping list

[0736] Output: Shopping list request data ready to send

[0737] What happens: The app formats the reminder data into JSON format and sends it to the server as a POST request.

[0738] Step 3:

[0739] User: Opens a navigation app and enters a route from home to office to search.

[0740] Input: Origin and destination information

[0741] Output: Route information search results

[0742] Specific action: A user enters the locations of "home" and "office" in the search bar of a navigation app and presses the "Search" button.

[0743] Step 4:

[0744] Terminal: The route information is converted into JSON format and sent to the server.

[0745] Input: Route information

[0746] Output: Route information request data ready to send

[0747] Specific behavior: The app formats the route information of the search results into JSON format and sends it to the server as a POST request.

[0748] Step 5:

[0749] Server: Matches the received route information with the shopping list and searches for the best stores to stop at.

[0750] Input: Route information and shopping list

[0751] Output: List of candidate stores

[0752] Specific operation: Based on the route information and shopping list, the server uses an external store information API to search for the most suitable store and checks inventory and business status.

[0753] Step 6:

[0754] Server: Generates and sends JSON data to send information on the best candidate stores to the mobile device.

[0755] Input: List of potential stores

[0756] Output: Potential store data in JSON format

[0757] Specific operation: The server formats the information about the selected store into JSON format and sends it as a response to the mobile device.

[0758] Step 7:

[0759] Terminal: Analyzes the received information on candidate stores and makes suggestions to the user.

[0760] Input: JSON format store candidate data

[0761] Output: Display of candidate stores

[0762] Specific behavior: The app parses the JSON data and displays candidate stores to the user in map and list format.

[0763] Step 8:

[0764] User: Review the suggested store information and decide whether to add the stop to their route.

[0765] Input: Information about the candidate store

[0766] Output: Final route decision

[0767] Specific operation: The user checks the displayed store information and, if necessary, presses the "Stop" button to add the location to the route.

[0768] This concretely executes the processing steps of the entire system, allowing users to efficiently manage their travel plans and daily activities.

[0769] (Application example 1)

[0770] 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."

[0771] Currently, there are many travel planning support systems and daily activity support systems, but these systems only generate static schedules based on data entered by the user. Furthermore, systems that can provide efficient route guidance and schedule adjustments in real time when users travel using autonomous vehicles are not yet widely available. Therefore, the challenge is to efficiently optimize travel plans and daily activities in cooperation with autonomous vehicles.

[0772] 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.

[0773] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile device, means for transmitting the input destination and departure date and time to the server, means for generating a time schedule using generative artificial intelligence based on the information received by the server, means for the server to collect information about the area around the destination, means for transmitting the schedule generated by the server and the collected information to the mobile device, means for the mobile device to display the schedule and information to the user, and means for transmitting route information and departure times to the autonomously driven vehicle based on the generated schedule. This enables a user to easily create a travel plan or schedule of daily activities using the mobile device and give instructions to the autonomously driven vehicle based on the schedule to efficiently travel and make stops.

[0774] A "mobile terminal" refers to an electronic device that a user can carry around and that can input and display information.

[0775] "Destination" refers to the final location where a user travels or moves.

[0776] "Departure date and time" refers to the date and time when the user starts traveling or moving.

[0777] "Server" refers to a central computer system that receives and processes data sent by users.

[0778] "Generative artificial intelligence" refers to artificial intelligence that can generate new information and schedules based on large amounts of data.

[0779] A "time schedule" refers to a plan that organizes a user's movements and activities along a time axis.

[0780] "Route information" refers to the route from the departure point to the destination and detailed information on the route.

[0781] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[0782] "Weather forecast" refers to a service that predicts future weather and provides that information.

[0783] "Traffic information" refers to real-time information about travel, such as road congestion, accident information, and public transportation operation status.

[0784] The present invention provides a system that efficiently supports users in planning their travels and daily activities by linking a mobile terminal and a server. Specific embodiments of the system are described below.

[0785] System configuration

[0786] 1. Mobile devices

[0787] An electronic device that users can carry around and that allows them to input and display information, such as a smartphone or tablet.

[0788] 2. Server

[0789] It is a central computer system that receives and processes data sent by users, and uses generative artificial intelligence to create time schedules and collect information.

[0790] 3. Generative Artificial Intelligence

[0791] It is an artificial intelligence that can generate new information and schedules based on large amounts of data. Specifically, OpenAI GPT-3 and other models are used.

[0792] 4. Self-driving vehicles

[0793] It is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[0794] Data entry and processing

[0795] 1. Travel Planning

[0796] The user uses the mobile terminal to input the travel destination and departure date and time.

[0797] The terminal transmits the input destination and departure date and time to the server.

[0798] The server uses generative artificial intelligence to create a time schedule based on the received information, including travel time and tourist spots and restaurants to visit after arriving at the destination.

[0799] The server collects additional information using tourist information APIs and gourmet information APIs around the destination and reflects this information in the time schedule.

[0800] The server transmits the generated schedule and the collected information to the mobile terminal.

[0801] The terminal displays schedules and information in a user-friendly format.

[0802] The terminal transmits route information and departure times to the autonomous vehicle based on the generated schedule, so that the autonomous vehicle departs according to the schedule.

[0803] Specific examples

[0804] If a user selects "Tokyo Disneyland" as their destination and sets the departure date and time as "September 10th, 9:00 AM," the system will optimize the user's travel plan based on that information and send instructions to the self-driving vehicle. An example of the prompt text is as follows:

[0805] Destination: Tokyo Disneyland

[0806] Departure date and time: September 10, 2023 09:00

[0807] Data entry and processing (shopping)

[0808] 1. Shopping plan

[0809] The user inputs a shopping list into the reminder app and sends it to the server using the device.

[0810] The user inputs a destination into the car navigation app and searches for a route, which is then sent to the server.

[0811] The server compares the received route information with the shopping list and searches for information on the best stores to stop at along the route.

[0812] The server transmits information on the best stores to visit to the mobile terminal.

[0813] The terminal displays the suggested store information to the user.

[0814] Program processing

[0815] The hardware includes servers and mobile terminals, and it is desirable for the servers to have high-performance processors and large amounts of memory.

[0816] The software includes generative artificial intelligence (e.g., OpenAI GPT-3), tourist information API, gourmet information API, navigation system, reminder app, and more.

[0817] Data processing and data calculation include schedule generation by the server, information collection using API, and analysis of user input data.

[0818] This allows users to easily create travel plans and schedules of daily activities using a mobile device, and then give instructions to an autonomous vehicle based on that schedule to travel and make stops efficiently.

[0819] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0820] Step 1:

[0821] The user inputs the travel destination and departure date and time using the mobile device. The data (prompt sentences) input by the user are saved in the mobile device.

[0822] Step 2:

[0823] The device sends the entered destination and departure date and time data to the server. Specifically, the device creates an HTTP POST request and sends it to a specific endpoint on the server. The input in this step is the user's input data, and the output is the request data sent to the server.

[0824] Step 3:

[0825] The server uses generative artificial intelligence (e.g., OpenAI GPT-3) to create a time schedule based on the information it receives. Specifically, the server uses a generative AI model to generate an optimal schedule based on the user's input (prompt). The input in this step is the destination and departure date and time sent from the device, and the output is the generated time schedule.

[0826] Step 4:

[0827] The server collects information about the area around the destination. Specifically, it uses the tourist information API and gourmet information API to obtain information about tourist spots and restaurants. The input in this step is information about the departure point and destination, and the output is the obtained tourist information and gourmet information.

[0828] Step 5:

[0829] The server sends the generated time schedule and collected information to the mobile device. The server formats this data into a specific format and sends it to the device using an HTTP POST request. The input in this step is the generated time schedule and collected information, and the output is the schedule data sent to the mobile device.

[0830] Step 6:

[0831] The terminal displays the schedule and information to the user. The terminal analyzes the received data and displays it in a format that is easy for the user to understand. For example, it displays the names of tourist attractions, visiting times, restaurant recommendations, etc. The input in this step is the schedule data sent from the server, and the output is the schedule information displayed to the user.

[0832] Step 7:

[0833] The terminal sends route information and departure times to the autonomous vehicle based on the generated schedule. The terminal formats this data and sends it to the autonomous vehicle's control system. The input in this step is the generated time schedule, and the output is the route information and departure times sent to the autonomous vehicle.

[0834] Step 8:

[0835] The autonomous vehicle departs according to the schedule and travels along the optimal route. The vehicle's control system starts the vehicle's operation based on the received route information and departure time. The input in this step is the route information and departure time sent from the terminal, and the output is the autonomous vehicle's driving behavior.

[0836] 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.

[0837] This invention is a system that combines a mobile device, a server, and an emotion engine to provide efficient and personalized support for travel planning and daily activities. The system begins when the user inputs information such as the destination, departure date and time, and shopping list on the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stopover stores, and provides this information to the user. Furthermore, the emotion engine can recognize the user's emotions and adjust the suggestions based on that information.

[0838] When planning a trip

[0839] 1. Enter your destination and departure date and time

[0840] User: Launches the app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0841] 2. Submit your input

[0842] Terminal: Formats the input destination and departure date and time and sends a request to the server.

[0843] 3. Generate a time schedule

[0844] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[0845] 4. Collection of additional information

[0846] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[0847] 5. Emotional Information Analysis

[0848] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[0849] 6. Tailor your offers based on emotions

[0850] Server: Based on the received emotional information, the server adjusts the generated time schedule and additional information according to the user's emotional state. For example, if the user is tired, the server may suggest more rest spots.

[0851] 7. Schedule and Information Submission

[0852] Server: Formats the generated time schedule and collected additional information into a format compatible with mobile devices and sends them to the devices.

[0853] 8. Displaying Information to the User

[0854] Terminal: Displays the time schedule and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[0855] User: Review the schedule and information displayed and make any necessary adjustments.

[0856] During normal use

[0857] 1. Registering a shopping list

[0858] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0859] 2. Send your shopping list

[0860] Terminal: Sends the entered shopping list to the server.

[0861] 3. Enter your destination and search for a route

[0862] User: Opens a navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0863] 4. Sending route information

[0864] Device: Sends route information searched by the car navigation app to the server.

[0865] 5. Matching routes and shopping lists

[0866] Server: Matches the received route information with the shopping list and searches for suitable stores along the route, taking into account store inventory and opening hours.

[0867] 6. Submit your candidate stores

[0868] Server: Sends the best candidate store information to the device.

[0869] 7. Store suggestions to users

[0870] Terminal: Based on the received store information, the terminal suggests possible stores for the user to stop at (e.g., presenting the nearest supermarket that can be stopped at on the way).

[0871] 8. Emotional Information Analysis

[0872] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[0873] 9. Adjust your offers based on emotions

[0874] Server: Based on the received emotion information, the server adjusts the suggested stores to stop at and the shopping items to be purchased. For example, if the user is in a hurry, the server suggests the most efficient route and stores.

[0875] 10. Final User Confirmation

[0876] User: Review the suggested information, choose whether to include the stop as a stop, and update the route.

[0877] In this way, a system can be constructed that provides efficient and personalized services to users. In this invention, the emotion engine is used to make suggestions based on the user's emotional state, providing the user with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[0878] The processing flow will be explained below.

[0879] When planning a trip

[0880] Step 1:

[0881] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[0882] Step 2:

[0883] The terminal formats the input destination and departure date and time information and sends a request to the server.

[0884] Step 3:

[0885] The server analyzes the received request and uses generative artificial intelligence to create a time schedule, taking into account travel time from the user's departure point to their destination, as well as recommended tourist spots and dining options.

[0886] Step 4:

[0887] The server collects information about tourist spots and gourmet restaurants around the destination from the gourmet information API, tourist information API, and other databases, and reflects this information in the time schedule.

[0888] Step 5:

[0889] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice.

[0890] Step 6:

[0891] The device formats the acquired emotional information and sends it to the server.

[0892] Step 7:

[0893] Based on the emotional information received from the emotion engine, the server adjusts the generated time schedule and suggestions according to the user's emotional state. For example, if the user is tired, it will suggest more rest spots.

[0894] Step 8:

[0895] The server transmits the adjusted time schedule and additional information to the mobile terminal.

[0896] Step 9:

[0897] The device displays the schedule and information received in a user-friendly format (e.g., tourist spots and their visiting times, gourmet restaurant recommendations, etc.).

[0898] Step 10:

[0899] The user reviews the presented schedule and information and makes any necessary adjustments.

[0900] During normal use

[0901] Step 1:

[0902] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[0903] Step 2:

[0904] The terminal formats the input shopping list and sends it to the server.

[0905] Step 3:

[0906] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[0907] Step 4:

[0908] The device formats the route information searched by the car navigation app and sends it to the server.

[0909] Step 5:

[0910] The server compares the route information received with the shopping list and searches for suitable stores along the route, taking into account store inventory and business hours.

[0911] Step 6:

[0912] The server formats the best candidate store information and sends it to the terminal.

[0913] Step 7:

[0914] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, while driving, the car navigation system may say, "You seem a little tired today."

[0915] Step 8:

[0916] The device formats the acquired emotional information and sends it to the server.

[0917] Step 9:

[0918] The server adjusts the suggested stores to stop by and shopping contents based on the emotional information received from the emotion engine. For example, if the user is in a hurry, it suggests the most efficient route and stores.

[0919] Step 10:

[0920] The device displays tailored store information in a user-friendly format (e.g., showing the nearest supermarket to stop at on the way).

[0921] Step 11:

[0922] The user checks the information presented, selects whether to stop at the destination as a stopover, and updates the route.

[0923] In this way, users can plan their trips and make everyday purchases more efficient and personalized.

[0924] Example 2

[0925] 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."

[0926] Conventional travel planning and daily activity support systems do not take into account the user's emotional state when making suggestions, and therefore are often not optimized to the user's actual needs or physical condition. Furthermore, there is a problem in optimizing travel plans and shopping routes, in that they are unable to perform real-time emotional analysis and dynamic adjustments based on that analysis.

[0927] 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.

[0928] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile information processing device, means for transmitting the input destination and departure date and time to the server, means for the server to create a time schedule using generative artificial intelligence based on the information received, means for the server to collect additional information about the area around the destination, means for the server to transmit the schedule generated by the server and the collected additional information to the information processing device, means for the information processing device to display the schedule and additional information to the user, means for the information processing device to analyze the user's facial expressions and voice, and means for the server to adjust the content of suggestions based on the user's emotional information. This makes it possible to suggest optimal travel plans and shopping routes based on the user's emotional state.

[0929] A "portable information processing device" is a device with computing capabilities that is portable and allows a user to input and send and receive information.

[0930] A "destination" refers to a location where a user is heading in a trip or travel.

[0931] "Departure date and time" refers to the date and time when a user starts a specific trip or movement.

[0932] A "server" is a central processing unit that works in conjunction with information processing units via a network and receives, analyzes, and transmits information.

[0933] "Generative artificial intelligence" refers to algorithms and models that dynamically generate information to provide to users based on large amounts of data.

[0934] A "time schedule" refers to a list of activities scheduled based on time in a particular plan.

[0935] "Additional information" refers to detailed information about a destination or route, or supplementary information relevant to the user.

[0936] "Analysis of facial expressions and voice" refers to the process of analyzing the user's face and voice to estimate their emotional state.

[0937] "Emotion information" refers to data that indicates the analyzed emotional state of the user.

[0938] "Adjusting the proposal content" refers to the process of optimizing the plan and proposal content provided based on the user's emotional information.

[0939] MODE FOR CARRYING OUT THE INVENTION

[0940] This invention provides a system that uses a mobile information processing device, a server, and an emotion analysis engine to provide efficient and personalized support for travel planning and daily activities. Specific implementation methods are described below.

[0941] System configuration

[0942] The system includes the following components:

[0943] 1. Portable information processing device:

[0944] For example, a smartphone or tablet is used, and users use these devices to input information such as their destination and departure date and time.

[0945] Use trip planning apps, reminder apps, and car navigation apps to input destinations, departure dates and times, shopping lists, route information, etc.

[0946] 2. Server:

[0947] The server cooperates with the information processing device via the network to receive, analyze, and transmit information on the destination and departure date and time.

[0948] As a generative artificial intelligence, for example, we use OpenAI's GPT model, which automatically generates a time schedule.

[0949] We use external APIs such as Google Places API and Yelp API to collect information on gourmet food and tourist spots around the destination.

[0950] 3. Sentiment Analysis Engine:

[0951] To analyze the user's facial expressions and voice, the Emotion API of Microsoft's Azure Cognitive Services is used, which acquires the user's emotional information and sends it to the server.

[0952] Example: Travel Planning

[0953] The following steps are taken by a user using a travel planning app on their smartphone to plan a trip to Kyoto on August 15th.

[0954] 1. Enter your destination and departure date and time:

[0955] The user launches the app on their smartphone and enters "Destination: Kyoto, Departure date and time: August 15th, 10:00."

[0956] 2. Generate time schedule:

[0957] The server uses the received information to generate a time schedule using the GPT model, which includes travel time from the departure point to the destination and tourist spots to visit after arriving.

[0958] 3. Additional Information Collection:

[0959] The server uses the Google Places API and Yelp API to collect information about tourist attractions and restaurants around the destination and reflects this information in the timetable.

[0960] 4. Emotional information analysis:

[0961] The smartphone analyzes the user's voice and facial expressions and sends emotional information to the server. If the analysis finds that the user is "tired," that information is sent to the server.

[0962] 5. Tailor your offers based on emotions:

[0963] The server adds more rest spots to the schedule based on the emotion information and provides it to the user, enabling personalized suggestions according to the user's state.

[0964] Example: Everyday shopping

[0965] The following steps show how a user can use a smartphone reminder app and a car navigation app to efficiently shop while on the move.

[0966] 1. Registering a shopping list:

[0967] A user enters "Shopping list: milk, bread, eggs" into a Reminders app.

[0968] 2. Sending route information:

[0969] The user enters "destination: office" into the car navigation app and searches for the optimal route.

[0970] 3. Proposal of candidate stores:

[0971] The server compares the received route information with the shopping list, searches for the best store information along the route, and sends the selected store information to the user's smartphone.

[0972] 4. Emotional information analysis:

[0973] The smartphone analyzes the user's voice and facial expressions, and if it determines that the user is in a hurry, it sends that information to the server.

[0974] 5. Tailor your offers based on emotions:

[0975] The server then uses the emotion information to suggest the most efficient route and the nearest store. For example, if the user feels "in a hurry," it will suggest a convenience store on the shortest route.

[0976] Prompt Sentence Examples

[0977] Below are some examples of specific travel planning prompts:

[0978] Travel Planner

[0979] Destination: Kyoto

[0980] Departure date and time: August 15th, 10:00

[0981] User Emotion: Tired

[0982] Generate optimal travel itineraries based on user requests and include plenty of rest stops.

[0983] Below are some examples of specific prompts for everyday shopping:

[0984] Regular use

[0985] Shopping list: milk, bread, eggs

[0986] Destination: Office

[0987] Route: Home to office

[0988] User Sentiment: Urgent

[0989] Suggest the best shopping store and route.

[0990] As described above, this system can provide optimal suggestions based on the user's condition and needs, enabling efficient and personalized travel planning and support for daily activities.

[0991] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0992] Travel planning process

[0993] Step 1:

[0994] A user launches a travel planning app on their smartphone and inputs a destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00 AM) by typing the information into the app's text fields and pressing the submit button.

[0995] Input: Destination and departure date and time

[0996] Output: None (preparing input data)

[0997] Step 2:

[0998] The device formats the destination and departure date and time entered by the user and sends it to the server via an HTTP POST request, which is sent as JSON-formatted data.

[0999] Input: User input data (destination and departure date and time)

[1000] Output: Request sent to server

[1001] Step 3:

[1002] The server analyzes the received requests and generates a time schedule using generative artificial intelligence (GPT model). The server analyzes the received data and creates a schedule that takes into account travel time and tourist spots to visit after arrival.

[1003] Input: Request from the device (destination and departure date and time)

[1004] Output: Generated time schedule

[1005] Step 4:

[1006] The server uses the Google Places API, Yelp API, etc. to collect information on tourist spots and gourmet restaurants around the destination, analyzes the information obtained from the API, and adds it to the generated time schedule.

[1007] Input: Destination information

[1008] Output: Added tourist spots and gourmet restaurant information

[1009] Step 5:

[1010] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[1011] Input: User's voice and facial expression data

[1012] Output: Parsed emotion information

[1013] Step 6:

[1014] The server adjusts the generated time schedule and additional information based on the received emotion information. For example, if the server determines that the user is "tired," it adds more rest spots to the schedule.

[1015] Input: Emotion information

[1016] Output: Adjusted time schedule and additional information

[1017] Step 7:

[1018] The server then formats the final generated time schedule and additional information into JSON format and sends it back to the terminal.

[1019] Input: Adjusted time schedule and additional information

[1020] Output: Data sent to the terminal

[1021] Step 8:

[1022] The device displays the schedule and information received from the server in an easy-to-understand format to the user, such as a list of tourist spots to visit, the times to visit them, and recommended restaurants.

[1023] Input: Data sent from the server

[1024] Output: Schedules and information displayed to the user

[1025] Processing during normal use

[1026] Step 1:

[1027] A user enters a shopping list (e.g., milk, bread, eggs) into a reminder app, which stores the list in a local database and prepares it for sending.

[1028] Input: Shopping list item

[1029] Output: None (preparing input data)

[1030] Step 2:

[1031] The terminal sends the shopping list entered by the user to the server via an HTTP POST request. This list is sent as JSON format data.

[1032] Input: A shopping list entered by the user

[1033] Output: Request sent to server

[1034] Step 3:

[1035] A user starts a car navigation app, inputs a destination (e.g., an office), and searches for a route. This information is processed within the car navigation app, and the route information provided is generated.

[1036] Input: Destination information

[1037] Output: Route information

[1038] Step 4:

[1039] The device sends route information searched by the car navigation app to the server, which is also sent in JSON format.

[1040] Input: Route information

[1041] Output: Request sent to server

[1042] Step 5:

[1043] The server compares the received route information with the shopping list and searches for appropriate stores along the route, taking into account each store's inventory and business hours.

[1044] Input: Route information and shopping list

[1045] Output: List of optimal store candidates

[1046] Step 6:

[1047] The server formats the best candidate store information in JSON format and sends it to the terminal.

[1048] Input: List of optimal store candidates

[1049] Output: Data sent to the terminal

[1050] Step 7:

[1051] The device will then suggest possible stores to the user based on the received store information, and the app screen will display the nearest supermarkets that the user can stop at along the way.

[1052] Input: Data sent from the server

[1053] Output: Store suggestions to the user

[1054] Step 8:

[1055] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[1056] Input: User's voice and facial expression data

[1057] Output: Parsed emotion information

[1058] Step 9:

[1059] The server then adjusts the suggested stores to stop by and the shopping items to be purchased based on the received emotional information. For example, if the user is in a hurry, it will suggest the most efficient route and stores.

[1060] Input: Emotion information

[1061] Output: Adjusted proposal

[1062] Step 10:

[1063] The user reviews the proposed information, selects whether to include stops as intermediate locations, and updates the route. Once the user gives final approval, the system resends the updated information to the server.

[1064] Input: User selection and confirmation

[1065] Output: The final route

[1066] (Application example 2)

[1067] 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."

[1068] Conventional travel planning and daily shopping route guidance systems have difficulty responding to the user's emotional state and real-time environmental changes. In particular, in autonomous vehicles, optimal route suggestions and selection of stops often do not take the driver's emotional state into account, resulting in a loss of comfort and convenience for the user. Furthermore, fixed route suggestions without understanding the driver's emotional state can increase the user's stress and fatigue.

[1069] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a destination and departure date and time on a mobile terminal; a means for transmitting the input destination and departure date and time to the server; a means for the server to create a time schedule using a generation AI system based on the information received; a means for the server to collect information about the area around the destination; a means for the server to transmit the schedule generated by the server and the collected information to the mobile terminal; a means for the mobile terminal to analyze the user's emotional state; a means for the server to adjust the schedule based on the user's emotional information; and a means for the mobile terminal to display the schedule and information to the user visually or audibly. This makes it possible to grasp the user's emotional state in real time and suggest optimal routes and stops based on that state.

[1070] "Mobile terminal" refers to a relocatable, portable electronic device that allows a user to input information and exchange information with a server.

[1071] A "generative AI system" is an artificial intelligence technology that has the ability to generate and predict appropriate schedules and routes based on the information it receives.

[1072] A "time schedule" is a specific action plan that organizes the user's planned activities by date and time.

[1073] "Emotion information" is information about the emotional state obtained by analyzing data such as the user's facial expressions and voice.

[1074] "Means for visually or audibly displaying schedules and information" means the functionality of a mobile terminal to provide information to a user through a screen display or audio output.

[1075] A "stop-by store" is a store or facility that a user visits temporarily while traveling, and is a place used for shopping, resting, etc.

[1076] A "server" is a central processing unit that receives requests from clients (for example, mobile terminals) via a network and processes and provides information.

[1077] The system for implementing this invention mainly comprises a mobile terminal, a server, a generative AI system, and an emotion analysis engine. A specific implementation method will be described in detail below.

[1078] First, the user inputs the travel destination and departure date and time into a mobile device. The mobile device can be a smartphone, tablet, or in-car display. The input information is then sent to a server via a network. The server then uses a pre-designed generative AI system to create an optimal time schedule based on the received destination and departure date and time.

[1079] The generative AI system optimizes schedules in real time, taking into account information such as travel time from the user's departure point to their destination, traffic conditions, weather forecasts, and tourist spots they want to visit. It also obtains gourmet and tourist information around the destination through external APIs and provides it to users.

[1080] Next, the mobile device uses its built-in camera and microphone to analyze the user's facial expressions and voice to obtain emotional information. This process is performed using an emotion analysis engine, such as Microsoft Azure's Emotion API. The analyzed emotional information is then sent back to the server, where the schedule generated on the server side is adjusted to match the user's emotional state.

[1081] For example, if the user is tired, the system will suggest more spots for rest and relaxation, while if the user is energetic, it will dynamically adjust the places to visit, suggesting more active tourist spots.

[1082] The final time schedule is then sent to the mobile device and presented to the user visually or audibly. Visual display is provided on the smartphone screen or the vehicle display, while audio guidance is provided through the audio speaker.

[1083] As a concrete example, the following prompt sentences are input into a generative AI model to generate a schedule:

[1084] "The departure point is the current location, the destination is Tokyo Station, and the departure time is 10:00 on August 15, 2023. The user is in a calm emotional state. Based on this state, please generate the optimal route that includes a stop at the museum."

[1085] In this way, a user of the present invention can obtain an optimal travel plan that matches his or her emotional state, and can realize more comfortable and efficient travel.

[1086] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1087] Step 1:

[1088] The user inputs the travel destination and departure date and time on the mobile terminal.

[1089] Input: The destination (e.g. Tokyo Station) and departure date and time (e.g. August 15, 2023, 10:00) entered by the user.

[1090] Output: Input data stored on the mobile device.

[1091] Step 2:

[1092] The mobile terminal transmits the input destination and departure date and time to the server.

[1093] Input: Destination and departure date and time data obtained in step 1.

[1094] Output: The request data sent to the server.

[1095] Step 3:

[1096] Based on the information received by the server, a time schedule is created using a generative AI model.

[1097] Input: Destination and departure date / time data sent in step 2.

[1098] Processing: The generative AI model generates a schedule taking into account travel time from the user's origin to their destination, traffic information, and weather forecasts.

[1099] Output: The generated time schedule data.

[1100] Step 4:

[1101] The server collects information about the area around the destination.

[1102] Input: Destination information.

[1103] Processing: The server collects information on tourist spots and gourmet restaurants around the destination through the gourmet information API and tourist information API.

[1104] Output: Collected tourist spot and gourmet restaurant information.

[1105] Step 5:

[1106] The mobile device uses a built-in camera and microphone to analyze the user's emotional state and transmit it to a server.

[1107] Input: User's facial and voice data.

[1108] Processing: Analyze emotional information using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[1109] Output: Parsed emotion information is sent to the server.

[1110] Step 6:

[1111] The server-generated schedule and collected information are adjusted based on the user's emotional state.

[1112] Input: Time schedule data generated in step 3, information collected in step 4, and emotion information sent in step 5.

[1113] Processing: Dynamically adjust the time schedule and suggested tourist spots based on emotion information. For example, if the user is tired, a schedule with more breaks will be suggested.

[1114] Output: Adjusted schedule and stop data.

[1115] Step 7:

[1116] The server transmits the generated schedule and collected information to the mobile terminal.

[1117] Input: Schedule and stop data adjusted in step 6.

[1118] Output: Transmitted to mobile device.

[1119] Step 8:

[1120] The mobile terminal displays the schedule and information visually and audibly to the user.

[1121] Input: Schedule and stop-off point data received from the server in step 7.

[1122] Processing: The mobile terminal provides information to the user through a screen display and audio output.

[1123] Output: The visual and audio information provided to the user.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] [Third embodiment]

[1128] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1129] 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.

[1130] 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).

[1131] 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.

[1132] 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.

[1133] 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).

[1134] 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.

[1135] 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.

[1136] 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.

[1137] 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.

[1138] 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.

[1139] 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."

[1140] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. This system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stores to stop by, and provides this information to the user.

[1141] When planning a trip

[1142] 1. Enter your destination and departure date and time

[1143] User: Launches the app on their smartphone and enters their travel destination and departure date and time (e.g., Kyoto, departure August 15th at 10:00).

[1144] 2. Submit your input

[1145] Terminal: Creates a request to send the destination and departure date and time entered by the user to the server, and sends it to the server.

[1146] 3. Generate a time schedule

[1147] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[1148] 4. Collection of additional information

[1149] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[1150] 5. Submitting schedules and information

[1151] Server: Formats the generated time schedule and collected additional information into a format compatible with the mobile device and sends it to the device.

[1152] 6. Displaying Information to the User

[1153] Terminal: Displays the timetable and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[1154] User: Review the schedule and information displayed and adjust items as needed.

[1155] During normal use

[1156] 1. Registering a shopping list

[1157] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1158] 2. Send your shopping list

[1159] Terminal: Sends the entered shopping list to the server.

[1160] 3. Enter your destination and search for a route

[1161] User: Launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1162] 4. Sending route information

[1163] Device: The car navigation app sends the route information it has searched to the server.

[1164] 5. Matching routes and shopping lists

[1165] Server: Compares the received route information with the shopping list and searches for stores that can be visited along the way, taking into account store inventory and business hours.

[1166] 6. Submit your candidate stores

[1167] Server: Sends the best candidate store information to the device.

[1168] 7. Store suggestions to users

[1169] Terminal: Makes suggestions to the user based on the received store information (e.g., suggests the nearest supermarket to stop at on the way).

[1170] User: Review the suggested store information and decide whether to add the stop to their route.

[1171] In this way, a system can be constructed that provides efficient and convenient services to users. The present invention also provides a function that reflects weather forecasts and traffic information in schedules, providing users with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1172] The processing flow will be explained below.

[1173] When planning a trip

[1174] Step 1:

[1175] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1176] Step 2:

[1177] The terminal formats the input destination and departure date and time and sends a request to the server.

[1178] Step 3:

[1179] The server analyzes the received requests and uses generative artificial intelligence to create a time schedule, taking into account travel times, tourist attractions, and dining options.

[1180] Step 4:

[1181] The server collects the necessary information from the gourmet information API, tourist information API, and various databases and reflects it in the time schedule.

[1182] Step 5:

[1183] The server formats the generated time schedule and additional information in a format compatible with the mobile terminal and transmits them to the terminal.

[1184] Step 6:

[1185] The device displays the received time schedule and additional information to the user, such as pins on a map or visually displaying planned visits by time period.

[1186] Step 7:

[1187] The user reviews the presented schedule and information and makes any necessary adjustments.

[1188] During normal use

[1189] Step 1:

[1190] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1191] Step 2:

[1192] The terminal sends the shopping list to the server.

[1193] Step 3:

[1194] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1195] Step 4:

[1196] The device sends the route information searched by the car navigation app to the server.

[1197] Step 5:

[1198] The server analyzes the route information and shopping list received and searches for suitable stores along the route, taking into account store inventory and business hours.

[1199] Step 6:

[1200] The server transmits the best candidate store information to the terminal.

[1201] Step 7:

[1202] Based on the store information received by the device, the device suggests possible stores for the user to stop by (e.g., presenting the nearest supermarket that can be stopped at on the way).

[1203] Step 8:

[1204] The user checks the presented store information, selects whether to stop at the store as a stopover, and updates the route.

[1205] In this way, users can efficiently plan their trips and also effectively carry out their daily shopping.

[1206] Example 1

[1207] 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."

[1208] In order to efficiently plan trips and manage daily activities, a system that links mobile devices and servers is needed. However, existing systems require users to manually create schedules and individually search for information about the area around their destination, which requires time and effort. Furthermore, systems that suggest optimal stores based on shopping lists and travel routes are inadequate, making it difficult for users to shop efficiently. Furthermore, the lack of a function to dynamically adjust the generated schedule by reflecting weather forecasts and traffic information makes it difficult to create accurate plans.

[1209] 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.

[1210] In this invention, the server includes means for creating a time schedule using a generative AI model based on received information, means for collecting information about the area around the destination from an external system, and means for transmitting the generated schedule and collected information to a mobile device. This allows users to efficiently plan their trip. It also provides information on optimal stopover stores based on the shopping list and route information entered into the navigation app, enabling efficient support for daily activities. Furthermore, by dynamically adjusting the generated time schedule to reflect weather forecasts and traffic information, it is possible to provide highly accurate plans.

[1211] A "destination" is a geographical location that a user sets as a destination in their travel plans or daily activities.

[1212] The "departure date and time" is the date and time that the user sets as the start time of the travel plan or activity.

[1213] A "mobile terminal" is an electronic device that a user can carry around, and includes smartphones, tablets, and the like.

[1214] A "server" is a computer system on a network that processes and stores data and communicates with external systems.

[1215] A "generative AI model" is an artificial intelligence system that automatically generates time schedules and proposals based on input information from users.

[1216] A "time schedule" is a timetable that a user plans to follow when traveling or performing daily activities.

[1217] An "external system" is an information providing system managed by a third party that the server accesses and uses to collect information.

[1218] "Gourmet information" is information about restaurants, including types of food, ratings, addresses, etc.

[1219] "Tourist information" refers to information about tourist spots, including the characteristics of tourist spots and access information.

[1220] A "shopping list" is a list of items that a user wishes to purchase.

[1221] A "navigation app" is software that users use to search for and navigate their way to a destination.

[1222] "Route information" refers to information about the route from the departure point to the destination provided by the navigation app.

[1223] "Store information" refers to information about the location, opening hours, stock status, etc. of shops and supermarkets.

[1224] A "weather forecast" is information predicting future weather conditions in a particular area.

[1225] "Traffic information" refers to information about road congestion and the operation status of public transportation.

[1226] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. The system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server uses a generative AI model to propose optimal schedules and stopover stores, and provides this information to the user.

[1227] When planning a trip

[1228] First, the user launches a travel planning app on their smartphone and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00). This information is sent from the device to the server. The server analyzes the received information and uses a generative AI model (e.g., GPT-4) to create an optimal time schedule for the user's travel plans. Specifically, the following prompt sentence is input into the generative AI model:

[1229] Example prompt sentence:

[1230] "I'm planning a trip to Kyoto. I'll be leaving on August 15th at 10:00. Can you suggest some tourist spots and good restaurants?"

[1231] Next, the server collects information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule. This information includes details of planned tourist spots and restaurants. The server sends the generated time schedule and collected information to the mobile device, which displays it to the user in an easy-to-understand format. The user can check the displayed schedule and adjust each item as necessary.

[1232] During normal use

[1233] The user enters a shopping list (e.g., milk, bread, eggs) into a reminder app (e.g., Google Keep). This shopping list is sent from the device to the server. Next, the user launches a navigation app (e.g., Google Maps), enters a destination (e.g., from home to office), and searches for a route. This route information is also sent from the device to the server. The server compares the received route information with the shopping list and searches for the best stores to stop at along the route. Specifically, it generates a prompt sentence like the following:

[1234] Example prompt sentence:

[1235] "I've added milk, bread, and eggs to my shopping list. Can you tell me the best store where I can buy these items on my way from my home to the office?"

[1236] The server uses an external system (e.g., store information API) to check inventory information and business status and select the most suitable candidate store. This store information is sent to the mobile device, which then makes suggestions to the user. The user can then check the suggested store information and decide which store to visit.

[1237] In this way, the system of the present invention provides efficient and convenient travel planning and daily activity support for users. The server also has the ability to reflect weather forecasts and traffic information in the time schedule it generates, providing even more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1238] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1239] Process steps when planning a trip

[1240] Step 1:

[1241] User: Launches a travel planning app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1242] Input: Destination and departure date and time information

[1243] Output: Input data ready to send

[1244] Specific operation: The user enters the required information into the input form and presses the "Submit" button.

[1245] Step 2:

[1246] Terminal: Generates request data including the input destination and departure date and time, and sends it to the server.

[1247] Input: User-entered destination and departure date and time information

[1248] Output: Request for destination and departure date and time sent to server

[1249] What happens: The app formats the request data into JSON format and sends it as a POST request to the API endpoint.

[1250] Step 3:

[1251] Server: Analyzes the received request data and creates and inputs prompts to the generative AI model (e.g., GPT-4) to generate the optimal time schedule.

[1252] Input: Request data in JSON format

[1253] Output: Prompt sentences to be input to the generative AI model and the generated time schedule

[1254] Specific operation: The server analyzes the request data and generates a prompt such as, "I'm planning a trip to Kyoto. I'll be leaving at 10:00 on August 15th. Please suggest tourist spots and good restaurants." and inputs this into the AI ​​model.

[1255] Step 4:

[1256] Server: Collects additional information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule.

[1257] Input: Generated time schedule

[1258] Output: Appended time schedule

[1259] Specific operation: The server calls an API from an external system, obtains information about tourist spots and gourmet restaurants, and adds it to the time schedule.

[1260] Step 5:

[1261] Server: The created time schedule and collected additional information are formatted into a format that can be displayed on a mobile device (e.g., JSON format) and sent to the device.

[1262] Input: Added time schedule and additional information

[1263] Output: JSON formatted time schedule with additional information ready to send

[1264] Specific operation: The server formats the data into JSON format and returns it to the mobile device as a response.

[1265] Step 6:

[1266] Terminal: Displays the received time schedule and information on tourist spots and gourmet restaurants in a format that is easy for the user to understand.

[1267] Input: JSON format time schedule and additional information

[1268] Output: A timetable display including information on tourist attractions and places to eat

[1269] What it does: The app parses the JSON data and displays it to the user in a list or map format.

[1270] Step 7:

[1271] User: Review the schedule and information displayed and adjust items as needed.

[1272] Input: Displayed time schedule and information

[1273] Output: Modified time schedule

[1274] Specific operation: The user checks each item in the schedule and, if necessary, presses the edit button to make changes.

[1275] Normal usage process steps

[1276] Step 1:

[1277] User: Enters a shopping list (e.g., milk, bread, eggs) into a reminders app.

[1278] Input: Shopping list

[1279] Output: Input data saved

[1280] Specific behavior: The user enters the name of the desired product into the input field of the Reminders app and presses the "Save" button.

[1281] Step 2:

[1282] Terminal: Generates request data including the input shopping list and sends it to the server.

[1283] Input: Shopping list

[1284] Output: Shopping list request data ready to send

[1285] What happens: The app formats the reminder data into JSON format and sends it to the server as a POST request.

[1286] Step 3:

[1287] User: Opens a navigation app and enters a route from home to office to search.

[1288] Input: Origin and destination information

[1289] Output: Route information search results

[1290] Specific action: A user enters the locations of "home" and "office" in the search bar of a navigation app and presses the "Search" button.

[1291] Step 4:

[1292] Terminal: The route information is converted into JSON format and sent to the server.

[1293] Input: Route information

[1294] Output: Route information request data ready to send

[1295] Specific behavior: The app formats the route information of the search results into JSON format and sends it to the server as a POST request.

[1296] Step 5:

[1297] Server: Matches the received route information with the shopping list and searches for the best stores to stop at.

[1298] Input: Route information and shopping list

[1299] Output: List of candidate stores

[1300] Specific operation: Based on the route information and shopping list, the server uses an external store information API to search for the most suitable store and checks inventory and business status.

[1301] Step 6:

[1302] Server: Generates and sends JSON data to send information on the best candidate stores to the mobile device.

[1303] Input: List of potential stores

[1304] Output: Potential store data in JSON format

[1305] Specific operation: The server formats the information about the selected store into JSON format and sends it as a response to the mobile device.

[1306] Step 7:

[1307] Terminal: Analyzes the received information on candidate stores and makes suggestions to the user.

[1308] Input: JSON format store candidate data

[1309] Output: Display of candidate stores

[1310] Specific behavior: The app parses the JSON data and displays candidate stores to the user in map and list format.

[1311] Step 8:

[1312] User: Review the suggested store information and decide whether to add the stop to their route.

[1313] Input: Information about the candidate store

[1314] Output: Final route decision

[1315] Specific operation: The user checks the displayed store information and, if necessary, presses the "Stop" button to add the location to the route.

[1316] This concretely executes the processing steps of the entire system, allowing users to efficiently manage their travel plans and daily activities.

[1317] (Application example 1)

[1318] 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."

[1319] Currently, there are many travel planning support systems and daily activity support systems, but these systems only generate static schedules based on data entered by the user. Furthermore, systems that can provide efficient route guidance and schedule adjustments in real time when users travel using autonomous vehicles are not yet widely available. Therefore, the challenge is to efficiently optimize travel plans and daily activities in cooperation with autonomous vehicles.

[1320] 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.

[1321] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile device, means for transmitting the input destination and departure date and time to the server, means for generating a time schedule using generative artificial intelligence based on the information received by the server, means for the server to collect information about the area around the destination, means for transmitting the schedule generated by the server and the collected information to the mobile device, means for the mobile device to display the schedule and information to the user, and means for transmitting route information and departure times to the autonomously driven vehicle based on the generated schedule. This enables a user to easily create a travel plan or schedule of daily activities using the mobile device and give instructions to the autonomously driven vehicle based on the schedule to efficiently travel and make stops.

[1322] A "mobile terminal" refers to an electronic device that a user can carry around and that can input and display information.

[1323] "Destination" refers to the final location where a user travels or moves.

[1324] "Departure date and time" refers to the date and time when the user starts traveling or moving.

[1325] "Server" refers to a central computer system that receives and processes data sent by users.

[1326] "Generative artificial intelligence" refers to artificial intelligence that can generate new information and schedules based on large amounts of data.

[1327] A "time schedule" refers to a plan that organizes a user's movements and activities along a time axis.

[1328] "Route information" refers to the route from the departure point to the destination and detailed information on the route.

[1329] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[1330] "Weather forecast" refers to a service that predicts future weather and provides that information.

[1331] "Traffic information" refers to real-time information about travel, such as road congestion, accident information, and public transportation operation status.

[1332] The present invention provides a system that efficiently supports users in planning their travels and daily activities by linking a mobile terminal and a server. Specific embodiments of the system are described below.

[1333] System configuration

[1334] 1. Mobile devices

[1335] An electronic device that users can carry around and that allows them to input and display information, such as a smartphone or tablet.

[1336] 2. Server

[1337] It is a central computer system that receives and processes data sent by users, and uses generative artificial intelligence to create time schedules and collect information.

[1338] 3. Generative Artificial Intelligence

[1339] It is an artificial intelligence that can generate new information and schedules based on large amounts of data. Specifically, OpenAI GPT-3 and other models are used.

[1340] 4. Self-driving vehicles

[1341] It is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[1342] Data entry and processing

[1343] 1. Travel Planning

[1344] The user uses the mobile terminal to input the travel destination and departure date and time.

[1345] The terminal transmits the input destination and departure date and time to the server.

[1346] The server uses generative artificial intelligence to create a time schedule based on the received information, including travel time and tourist spots and restaurants to visit after arriving at the destination.

[1347] The server collects additional information using tourist information APIs and gourmet information APIs around the destination and reflects this information in the time schedule.

[1348] The server transmits the generated schedule and the collected information to the mobile terminal.

[1349] The terminal displays schedules and information in a user-friendly format.

[1350] The terminal transmits route information and departure times to the autonomous vehicle based on the generated schedule, so that the autonomous vehicle departs according to the schedule.

[1351] Specific examples

[1352] If a user selects "Tokyo Disneyland" as their destination and sets the departure date and time as "September 10th, 9:00 AM," the system will optimize the user's travel plan based on that information and send instructions to the self-driving vehicle. An example of the prompt text is as follows:

[1353] Destination: Tokyo Disneyland

[1354] Departure date and time: September 10, 2023 09:00

[1355] Data entry and processing (shopping)

[1356] 1. Shopping plan

[1357] The user inputs a shopping list into the reminder app and sends it to the server using the device.

[1358] The user inputs a destination into the car navigation app and searches for a route, which is then sent to the server.

[1359] The server compares the received route information with the shopping list and searches for information on the best stores to stop at along the route.

[1360] The server transmits information on the best stores to visit to the mobile terminal.

[1361] The terminal displays the suggested store information to the user.

[1362] Program processing

[1363] The hardware includes servers and mobile terminals, and it is desirable for the servers to have high-performance processors and large amounts of memory.

[1364] The software includes generative artificial intelligence (e.g., OpenAI GPT-3), tourist information API, gourmet information API, navigation system, reminder app, and more.

[1365] Data processing and data calculation include schedule generation by the server, information collection using API, and analysis of user input data.

[1366] This allows users to easily create travel plans and schedules of daily activities using a mobile device, and then give instructions to an autonomous vehicle based on that schedule to travel and make stops efficiently.

[1367] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1368] Step 1:

[1369] The user inputs the travel destination and departure date and time using the mobile device. The data (prompt sentences) input by the user are saved in the mobile device.

[1370] Step 2:

[1371] The device sends the entered destination and departure date and time data to the server. Specifically, the device creates an HTTP POST request and sends it to a specific endpoint on the server. The input in this step is the user's input data, and the output is the request data sent to the server.

[1372] Step 3:

[1373] The server uses generative artificial intelligence (e.g., OpenAI GPT-3) to create a time schedule based on the information it receives. Specifically, the server uses a generative AI model to generate an optimal schedule based on the user's input (prompt). The input in this step is the destination and departure date and time sent from the device, and the output is the generated time schedule.

[1374] Step 4:

[1375] The server collects information about the area around the destination. Specifically, it uses the tourist information API and gourmet information API to obtain information about tourist spots and restaurants. The input in this step is information about the departure point and destination, and the output is the obtained tourist information and gourmet information.

[1376] Step 5:

[1377] The server sends the generated time schedule and collected information to the mobile device. The server formats this data into a specific format and sends it to the device using an HTTP POST request. The input in this step is the generated time schedule and collected information, and the output is the schedule data sent to the mobile device.

[1378] Step 6:

[1379] The terminal displays the schedule and information to the user. The terminal analyzes the received data and displays it in a format that is easy for the user to understand. For example, it displays the names of tourist attractions, visiting times, restaurant recommendations, etc. The input in this step is the schedule data sent from the server, and the output is the schedule information displayed to the user.

[1380] Step 7:

[1381] The terminal sends route information and departure times to the autonomous vehicle based on the generated schedule. The terminal formats this data and sends it to the autonomous vehicle's control system. The input in this step is the generated time schedule, and the output is the route information and departure times sent to the autonomous vehicle.

[1382] Step 8:

[1383] The autonomous vehicle departs according to the schedule and travels along the optimal route. The vehicle's control system starts the vehicle's operation based on the received route information and departure time. The input in this step is the route information and departure time sent from the terminal, and the output is the autonomous vehicle's driving behavior.

[1384] 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.

[1385] This invention is a system that combines a mobile device, a server, and an emotion engine to provide efficient and personalized support for travel planning and daily activities. The system begins when the user inputs information such as the destination, departure date and time, and shopping list on the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stopover stores, and provides this information to the user. Furthermore, the emotion engine can recognize the user's emotions and adjust the suggestions based on that information.

[1386] When planning a trip

[1387] 1. Enter your destination and departure date and time

[1388] User: Launches the app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1389] 2. Submit your input

[1390] Terminal: Formats the input destination and departure date and time and sends a request to the server.

[1391] 3. Generate a time schedule

[1392] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[1393] 4. Collection of additional information

[1394] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[1395] 5. Emotional Information Analysis

[1396] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[1397] 6. Tailor your offers based on emotions

[1398] Server: Based on the received emotional information, the server adjusts the generated time schedule and additional information according to the user's emotional state. For example, if the user is tired, the server may suggest more rest spots.

[1399] 7. Schedule and Information Submission

[1400] Server: Formats the generated time schedule and collected additional information into a format compatible with mobile devices and sends them to the devices.

[1401] 8. Displaying Information to the User

[1402] Terminal: Displays the time schedule and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[1403] User: Review the schedule and information displayed and make any necessary adjustments.

[1404] During normal use

[1405] 1. Registering a shopping list

[1406] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1407] 2. Send your shopping list

[1408] Terminal: Sends the entered shopping list to the server.

[1409] 3. Enter your destination and search for a route

[1410] User: Opens a navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1411] 4. Sending route information

[1412] Device: Sends route information searched by the car navigation app to the server.

[1413] 5. Matching routes and shopping lists

[1414] Server: Matches the received route information with the shopping list and searches for suitable stores along the route, taking into account store inventory and opening hours.

[1415] 6. Submit your candidate stores

[1416] Server: Sends the best candidate store information to the device.

[1417] 7. Store suggestions to users

[1418] Terminal: Based on the received store information, the terminal suggests possible stores for the user to stop at (e.g., presenting the nearest supermarket that can be stopped at on the way).

[1419] 8. Emotional Information Analysis

[1420] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[1421] 9. Adjust your offers based on emotions

[1422] Server: Based on the received emotion information, the server adjusts the suggested stores to stop at and the shopping items to be purchased. For example, if the user is in a hurry, the server suggests the most efficient route and stores.

[1423] 10. Final User Confirmation

[1424] User: Review the suggested information, choose whether to include the stop as a stop, and update the route.

[1425] In this way, a system can be constructed that provides efficient and personalized services to users. In this invention, the emotion engine is used to make suggestions based on the user's emotional state, providing the user with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1426] The processing flow will be explained below.

[1427] When planning a trip

[1428] Step 1:

[1429] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1430] Step 2:

[1431] The terminal formats the input destination and departure date and time information and sends a request to the server.

[1432] Step 3:

[1433] The server analyzes the received request and uses generative artificial intelligence to create a time schedule, taking into account travel time from the user's departure point to their destination, as well as recommended tourist spots and dining options.

[1434] Step 4:

[1435] The server collects information about tourist spots and gourmet restaurants around the destination from the gourmet information API, tourist information API, and other databases, and reflects this information in the time schedule.

[1436] Step 5:

[1437] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice.

[1438] Step 6:

[1439] The device formats the acquired emotional information and sends it to the server.

[1440] Step 7:

[1441] Based on the emotional information received from the emotion engine, the server adjusts the generated time schedule and suggestions according to the user's emotional state. For example, if the user is tired, it will suggest more rest spots.

[1442] Step 8:

[1443] The server transmits the adjusted time schedule and additional information to the mobile terminal.

[1444] Step 9:

[1445] The device displays the schedule and information received in a user-friendly format (e.g., tourist spots and their visiting times, gourmet restaurant recommendations, etc.).

[1446] Step 10:

[1447] The user reviews the presented schedule and information and makes any necessary adjustments.

[1448] During normal use

[1449] Step 1:

[1450] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1451] Step 2:

[1452] The terminal formats the input shopping list and sends it to the server.

[1453] Step 3:

[1454] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1455] Step 4:

[1456] The device formats the route information searched by the car navigation app and sends it to the server.

[1457] Step 5:

[1458] The server compares the route information received with the shopping list and searches for suitable stores along the route, taking into account store inventory and business hours.

[1459] Step 6:

[1460] The server formats the best candidate store information and sends it to the terminal.

[1461] Step 7:

[1462] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, while driving, the car navigation system may say, "You seem a little tired today."

[1463] Step 8:

[1464] The device formats the acquired emotional information and sends it to the server.

[1465] Step 9:

[1466] The server adjusts the suggested stores to stop by and shopping contents based on the emotional information received from the emotion engine. For example, if the user is in a hurry, it suggests the most efficient route and stores.

[1467] Step 10:

[1468] The device displays tailored store information in a user-friendly format (e.g., showing the nearest supermarket to stop at on the way).

[1469] Step 11:

[1470] The user checks the information presented, selects whether to stop at the destination as a stopover, and updates the route.

[1471] In this way, users can plan their trips and make everyday purchases more efficient and personalized.

[1472] Example 2

[1473] 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."

[1474] Conventional travel planning and daily activity support systems do not take into account the user's emotional state when making suggestions, and therefore are often not optimized to the user's actual needs or physical condition. Furthermore, there is a problem in optimizing travel plans and shopping routes, in that they are unable to perform real-time emotional analysis and dynamic adjustments based on that analysis.

[1475] 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.

[1476] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile information processing device, means for transmitting the input destination and departure date and time to the server, means for the server to create a time schedule using generative artificial intelligence based on the information received, means for the server to collect additional information about the area around the destination, means for the server to transmit the schedule generated by the server and the collected additional information to the information processing device, means for the information processing device to display the schedule and additional information to the user, means for the information processing device to analyze the user's facial expressions and voice, and means for the server to adjust the content of suggestions based on the user's emotional information. This makes it possible to suggest optimal travel plans and shopping routes based on the user's emotional state.

[1477] A "portable information processing device" is a device with computing capabilities that is portable and allows a user to input and send and receive information.

[1478] A "destination" refers to a location where a user is heading in a trip or travel.

[1479] "Departure date and time" refers to the date and time when a user starts a specific trip or movement.

[1480] A "server" is a central processing unit that works in conjunction with information processing units via a network and receives, analyzes, and transmits information.

[1481] "Generative artificial intelligence" refers to algorithms and models that dynamically generate information to provide to users based on large amounts of data.

[1482] A "time schedule" refers to a list of activities scheduled based on time in a particular plan.

[1483] "Additional information" refers to detailed information about a destination or route, or supplementary information relevant to the user.

[1484] "Analysis of facial expressions and voice" refers to the process of analyzing the user's face and voice to estimate their emotional state.

[1485] "Emotion information" refers to data that indicates the analyzed emotional state of the user.

[1486] "Adjusting the proposal content" refers to the process of optimizing the plan and proposal content provided based on the user's emotional information.

[1487] MODE FOR CARRYING OUT THE INVENTION

[1488] This invention provides a system that uses a mobile information processing device, a server, and an emotion analysis engine to provide efficient and personalized support for travel planning and daily activities. Specific implementation methods are described below.

[1489] System configuration

[1490] The system includes the following components:

[1491] 1. Portable information processing device:

[1492] For example, a smartphone or tablet is used, and users use these devices to input information such as their destination and departure date and time.

[1493] Use trip planning apps, reminder apps, and car navigation apps to input destinations, departure dates and times, shopping lists, route information, etc.

[1494] 2. Server:

[1495] The server cooperates with the information processing device via the network to receive, analyze, and transmit information on the destination and departure date and time.

[1496] As a generative artificial intelligence, for example, we use OpenAI's GPT model, which automatically generates a time schedule.

[1497] We use external APIs such as Google Places API and Yelp API to collect information on gourmet food and tourist spots around the destination.

[1498] 3. Sentiment Analysis Engine:

[1499] To analyze the user's facial expressions and voice, the Emotion API of Microsoft's Azure Cognitive Services is used, which acquires the user's emotional information and sends it to the server.

[1500] Example: Travel Planning

[1501] The following steps are taken by a user using a travel planning app on their smartphone to plan a trip to Kyoto on August 15th.

[1502] 1. Enter your destination and departure date and time:

[1503] The user launches the app on their smartphone and enters "Destination: Kyoto, Departure date and time: August 15th, 10:00."

[1504] 2. Generate time schedule:

[1505] The server uses the received information to generate a time schedule using the GPT model, which includes travel time from the departure point to the destination and tourist spots to visit after arriving.

[1506] 3. Additional Information Collection:

[1507] The server uses the Google Places API and Yelp API to collect information about tourist attractions and restaurants around the destination and reflects this information in the timetable.

[1508] 4. Emotional information analysis:

[1509] The smartphone analyzes the user's voice and facial expressions and sends emotional information to the server. If the analysis finds that the user is "tired," that information is sent to the server.

[1510] 5. Tailor your offers based on emotions:

[1511] The server adds more rest spots to the schedule based on the emotion information and provides it to the user, enabling personalized suggestions according to the user's state.

[1512] Example: Everyday shopping

[1513] The following steps show how a user can use a smartphone reminder app and a car navigation app to efficiently shop while on the move.

[1514] 1. Registering a shopping list:

[1515] A user enters "Shopping list: milk, bread, eggs" into a Reminders app.

[1516] 2. Sending route information:

[1517] The user enters "destination: office" into the car navigation app and searches for the optimal route.

[1518] 3. Proposal of candidate stores:

[1519] The server compares the received route information with the shopping list, searches for the best store information along the route, and sends the selected store information to the user's smartphone.

[1520] 4. Emotional information analysis:

[1521] The smartphone analyzes the user's voice and facial expressions, and if it determines that the user is in a hurry, it sends that information to the server.

[1522] 5. Tailor your offers based on emotions:

[1523] The server then uses the emotion information to suggest the most efficient route and the nearest store. For example, if the user feels "in a hurry," it will suggest a convenience store on the shortest route.

[1524] Prompt Sentence Examples

[1525] Below are some examples of specific travel planning prompts:

[1526] Travel Planner

[1527] Destination: Kyoto

[1528] Departure date and time: August 15th, 10:00

[1529] User Emotion: Tired

[1530] Generate optimal travel itineraries based on user requests and include plenty of rest stops.

[1531] Below are some examples of specific prompts for everyday shopping:

[1532] Regular use

[1533] Shopping list: milk, bread, eggs

[1534] Destination: Office

[1535] Route: Home to office

[1536] User Sentiment: Urgent

[1537] Suggest the best shopping store and route.

[1538] As described above, this system can provide optimal suggestions based on the user's condition and needs, enabling efficient and personalized travel planning and support for daily activities.

[1539] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1540] Travel planning process

[1541] Step 1:

[1542] A user launches a travel planning app on their smartphone and inputs a destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00 AM) by typing the information into the app's text fields and pressing the submit button.

[1543] Input: Destination and departure date and time

[1544] Output: None (preparing input data)

[1545] Step 2:

[1546] The device formats the destination and departure date and time entered by the user and sends it to the server via an HTTP POST request, which is sent as JSON-formatted data.

[1547] Input: User input data (destination and departure date and time)

[1548] Output: Request sent to server

[1549] Step 3:

[1550] The server analyzes the received requests and generates a time schedule using generative artificial intelligence (GPT model). The server analyzes the received data and creates a schedule that takes into account travel time and tourist spots to visit after arrival.

[1551] Input: Request from the device (destination and departure date and time)

[1552] Output: Generated time schedule

[1553] Step 4:

[1554] The server uses the Google Places API, Yelp API, etc. to collect information on tourist spots and gourmet restaurants around the destination, analyzes the information obtained from the API, and adds it to the generated time schedule.

[1555] Input: Destination information

[1556] Output: Added tourist spots and gourmet restaurant information

[1557] Step 5:

[1558] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[1559] Input: User's voice and facial expression data

[1560] Output: Parsed emotion information

[1561] Step 6:

[1562] The server adjusts the generated time schedule and additional information based on the received emotion information. For example, if the server determines that the user is "tired," it adds more rest spots to the schedule.

[1563] Input: Emotion information

[1564] Output: Adjusted time schedule and additional information

[1565] Step 7:

[1566] The server then formats the final generated time schedule and additional information into JSON format and sends it back to the terminal.

[1567] Input: Adjusted time schedule and additional information

[1568] Output: Data sent to the terminal

[1569] Step 8:

[1570] The device displays the schedule and information received from the server in an easy-to-understand format to the user, such as a list of tourist spots to visit, the times to visit them, and recommended restaurants.

[1571] Input: Data sent from the server

[1572] Output: Schedules and information displayed to the user

[1573] Processing during normal use

[1574] Step 1:

[1575] A user enters a shopping list (e.g., milk, bread, eggs) into a reminder app, which stores the list in a local database and prepares it for sending.

[1576] Input: Shopping list item

[1577] Output: None (preparing input data)

[1578] Step 2:

[1579] The terminal sends the shopping list entered by the user to the server via an HTTP POST request. This list is sent as JSON format data.

[1580] Input: A shopping list entered by the user

[1581] Output: Request sent to server

[1582] Step 3:

[1583] A user starts a car navigation app, inputs a destination (e.g., an office), and searches for a route. This information is processed within the car navigation app, and the route information provided is generated.

[1584] Input: Destination information

[1585] Output: Route information

[1586] Step 4:

[1587] The device sends route information searched by the car navigation app to the server, which is also sent in JSON format.

[1588] Input: Route information

[1589] Output: Request sent to server

[1590] Step 5:

[1591] The server compares the received route information with the shopping list and searches for appropriate stores along the route, taking into account each store's inventory and business hours.

[1592] Input: Route information and shopping list

[1593] Output: List of optimal store candidates

[1594] Step 6:

[1595] The server formats the best candidate store information in JSON format and sends it to the terminal.

[1596] Input: List of optimal store candidates

[1597] Output: Data sent to the terminal

[1598] Step 7:

[1599] The device will then suggest possible stores to the user based on the received store information, and the app screen will display the nearest supermarkets that the user can stop at along the way.

[1600] Input: Data sent from the server

[1601] Output: Store suggestions to the user

[1602] Step 8:

[1603] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[1604] Input: User's voice and facial expression data

[1605] Output: Parsed emotion information

[1606] Step 9:

[1607] The server then adjusts the suggested stores to stop by and the shopping items to be purchased based on the received emotional information. For example, if the user is in a hurry, it will suggest the most efficient route and stores.

[1608] Input: Emotion information

[1609] Output: Adjusted proposal

[1610] Step 10:

[1611] The user reviews the proposed information, selects whether to include stops as intermediate locations, and updates the route. Once the user gives final approval, the system resends the updated information to the server.

[1612] Input: User selection and confirmation

[1613] Output: The final route

[1614] (Application example 2)

[1615] 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."

[1616] Conventional travel planning and daily shopping route guidance systems have difficulty responding to the user's emotional state and real-time environmental changes. In particular, in autonomous vehicles, optimal route suggestions and selection of stops often do not take the driver's emotional state into account, resulting in a loss of comfort and convenience for the user. Furthermore, fixed route suggestions without understanding the driver's emotional state can increase the user's stress and fatigue.

[1617] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a destination and departure date and time on a mobile terminal; a means for transmitting the input destination and departure date and time to the server; a means for the server to create a time schedule using a generation AI system based on the information received; a means for the server to collect information about the area around the destination; a means for the server to transmit the schedule generated by the server and the collected information to the mobile terminal; a means for the mobile terminal to analyze the user's emotional state; a means for the server to adjust the schedule based on the user's emotional information; and a means for the mobile terminal to display the schedule and information to the user visually or audibly. This makes it possible to grasp the user's emotional state in real time and suggest optimal routes and stops based on that state.

[1618] "Mobile terminal" refers to a relocatable, portable electronic device that allows a user to input information and exchange information with a server.

[1619] A "generative AI system" is an artificial intelligence technology that has the ability to generate and predict appropriate schedules and routes based on the information it receives.

[1620] A "time schedule" is a specific action plan that organizes the user's planned activities by date and time.

[1621] "Emotion information" is information about the emotional state obtained by analyzing data such as the user's facial expressions and voice.

[1622] "Means for visually or audibly displaying schedules and information" means the functionality of a mobile terminal to provide information to a user through a screen display or audio output.

[1623] A "stop-by store" is a store or facility that a user visits temporarily while traveling, and is a place used for shopping, resting, etc.

[1624] A "server" is a central processing unit that receives requests from clients (for example, mobile terminals) via a network and processes and provides information.

[1625] The system for implementing this invention mainly comprises a mobile terminal, a server, a generative AI system, and an emotion analysis engine. A specific implementation method will be described in detail below.

[1626] First, the user inputs the travel destination and departure date and time into a mobile device. The mobile device can be a smartphone, tablet, or in-car display. The input information is then sent to a server via a network. The server then uses a pre-designed generative AI system to create an optimal time schedule based on the received destination and departure date and time.

[1627] The generative AI system optimizes schedules in real time, taking into account information such as travel time from the user's departure point to their destination, traffic conditions, weather forecasts, and tourist spots they want to visit. It also obtains gourmet and tourist information around the destination through external APIs and provides it to users.

[1628] Next, the mobile device uses its built-in camera and microphone to analyze the user's facial expressions and voice to obtain emotional information. This process is performed using an emotion analysis engine, such as Microsoft Azure's Emotion API. The analyzed emotional information is then sent back to the server, where the schedule generated on the server side is adjusted to match the user's emotional state.

[1629] For example, if the user is tired, the system will suggest more spots for rest and relaxation, while if the user is energetic, it will dynamically adjust the places to visit, suggesting more active tourist spots.

[1630] The final time schedule is then sent to the mobile device and presented to the user visually or audibly. Visual display is provided on the smartphone screen or the vehicle display, while audio guidance is provided through the audio speaker.

[1631] As a concrete example, the following prompt sentences are input into a generative AI model to generate a schedule:

[1632] "The departure point is the current location, the destination is Tokyo Station, and the departure time is 10:00 on August 15, 2023. The user is in a calm emotional state. Based on this state, please generate the optimal route that includes a stop at the museum."

[1633] In this way, a user of the present invention can obtain an optimal travel plan that matches his or her emotional state, and can realize more comfortable and efficient travel.

[1634] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1635] Step 1:

[1636] The user inputs the travel destination and departure date and time on the mobile terminal.

[1637] Input: The destination (e.g. Tokyo Station) and departure date and time (e.g. August 15, 2023, 10:00) entered by the user.

[1638] Output: Input data stored on the mobile device.

[1639] Step 2:

[1640] The mobile terminal transmits the input destination and departure date and time to the server.

[1641] Input: Destination and departure date and time data obtained in step 1.

[1642] Output: The request data sent to the server.

[1643] Step 3:

[1644] Based on the information received by the server, a time schedule is created using a generative AI model.

[1645] Input: Destination and departure date / time data sent in step 2.

[1646] Processing: The generative AI model generates a schedule taking into account travel time from the user's origin to their destination, traffic information, and weather forecasts.

[1647] Output: The generated time schedule data.

[1648] Step 4:

[1649] The server collects information about the area around the destination.

[1650] Input: Destination information.

[1651] Processing: The server collects information on tourist spots and gourmet restaurants around the destination through the gourmet information API and tourist information API.

[1652] Output: Collected tourist spot and gourmet restaurant information.

[1653] Step 5:

[1654] The mobile device uses a built-in camera and microphone to analyze the user's emotional state and transmit it to a server.

[1655] Input: User's facial and voice data.

[1656] Processing: Analyze emotional information using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[1657] Output: Parsed emotion information is sent to the server.

[1658] Step 6:

[1659] The server-generated schedule and collected information are adjusted based on the user's emotional state.

[1660] Input: Time schedule data generated in step 3, information collected in step 4, and emotion information sent in step 5.

[1661] Processing: Dynamically adjust the time schedule and suggested tourist spots based on emotion information. For example, if the user is tired, a schedule with more breaks will be suggested.

[1662] Output: Adjusted schedule and stop data.

[1663] Step 7:

[1664] The server transmits the generated schedule and collected information to the mobile terminal.

[1665] Input: Schedule and stop data adjusted in step 6.

[1666] Output: Transmitted to mobile device.

[1667] Step 8:

[1668] The mobile terminal displays the schedule and information visually and audibly to the user.

[1669] Input: Schedule and stop-off point data received from the server in step 7.

[1670] Processing: The mobile terminal provides information to the user through a screen display and audio output.

[1671] Output: The visual and audio information provided to the user.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] [Fourth embodiment]

[1676] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1677] 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.

[1678] 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).

[1679] 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.

[1680] 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.

[1681] 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).

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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."

[1689] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. This system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stores to stop by, and provides this information to the user.

[1690] When planning a trip

[1691] 1. Enter your destination and departure date and time

[1692] User: Launches the app on their smartphone and enters their travel destination and departure date and time (e.g., Kyoto, departure August 15th at 10:00).

[1693] 2. Submit your input

[1694] Terminal: Creates a request to send the destination and departure date and time entered by the user to the server, and sends it to the server.

[1695] 3. Generate a time schedule

[1696] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[1697] 4. Collection of additional information

[1698] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[1699] 5. Submitting schedules and information

[1700] Server: Formats the generated time schedule and collected additional information into a format compatible with the mobile device and sends it to the device.

[1701] 6. Displaying Information to the User

[1702] Terminal: Displays the timetable and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[1703] User: Review the schedule and information displayed and adjust items as needed.

[1704] During normal use

[1705] 1. Registering a shopping list

[1706] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1707] 2. Send your shopping list

[1708] Terminal: Sends the entered shopping list to the server.

[1709] 3. Enter your destination and search for a route

[1710] User: Launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1711] 4. Sending route information

[1712] Device: The car navigation app sends the route information it has searched to the server.

[1713] 5. Matching routes and shopping lists

[1714] Server: Compares the received route information with the shopping list and searches for stores that can be visited along the way, taking into account store inventory and business hours.

[1715] 6. Submit your candidate stores

[1716] Server: Sends the best candidate store information to the device.

[1717] 7. Store suggestions to users

[1718] Terminal: Makes suggestions to the user based on the received store information (e.g., suggests the nearest supermarket to stop at on the way).

[1719] User: Review the suggested store information and decide whether to add the stop to their route.

[1720] In this way, a system can be constructed that provides efficient and convenient services to users. The present invention also provides a function that reflects weather forecasts and traffic information in schedules, providing users with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1721] The processing flow will be explained below.

[1722] When planning a trip

[1723] Step 1:

[1724] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1725] Step 2:

[1726] The terminal formats the input destination and departure date and time and sends a request to the server.

[1727] Step 3:

[1728] The server analyzes the received requests and uses generative artificial intelligence to create a time schedule, taking into account travel times, tourist attractions, and dining options.

[1729] Step 4:

[1730] The server collects the necessary information from the gourmet information API, tourist information API, and various databases and reflects it in the time schedule.

[1731] Step 5:

[1732] The server formats the generated time schedule and additional information in a format compatible with the mobile terminal and transmits them to the terminal.

[1733] Step 6:

[1734] The device displays the received time schedule and additional information to the user, such as pins on a map or visually displaying planned visits by time period.

[1735] Step 7:

[1736] The user reviews the presented schedule and information and makes any necessary adjustments.

[1737] During normal use

[1738] Step 1:

[1739] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1740] Step 2:

[1741] The terminal sends the shopping list to the server.

[1742] Step 3:

[1743] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1744] Step 4:

[1745] The device sends the route information searched by the car navigation app to the server.

[1746] Step 5:

[1747] The server analyzes the route information and shopping list received and searches for suitable stores along the route, taking into account store inventory and business hours.

[1748] Step 6:

[1749] The server transmits the best candidate store information to the terminal.

[1750] Step 7:

[1751] Based on the store information received by the device, the device suggests possible stores for the user to stop by (e.g., presenting the nearest supermarket that can be stopped at on the way).

[1752] Step 8:

[1753] The user checks the presented store information, selects whether to stop at the store as a stopover, and updates the route.

[1754] In this way, users can efficiently plan their trips and also effectively carry out their daily shopping.

[1755] Example 1

[1756] 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."

[1757] In order to efficiently plan trips and manage daily activities, a system that links mobile devices and servers is needed. However, existing systems require users to manually create schedules and individually search for information about the area around their destination, which requires time and effort. Furthermore, systems that suggest optimal stores based on shopping lists and travel routes are inadequate, making it difficult for users to shop efficiently. Furthermore, the lack of a function to dynamically adjust the generated schedule by reflecting weather forecasts and traffic information makes it difficult to create accurate plans.

[1758] 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.

[1759] In this invention, the server includes means for creating a time schedule using a generative AI model based on received information, means for collecting information about the area around the destination from an external system, and means for transmitting the generated schedule and collected information to a mobile device. This allows users to efficiently plan their trip. It also provides information on optimal stopover stores based on the shopping list and route information entered into the navigation app, enabling efficient support for daily activities. Furthermore, by dynamically adjusting the generated time schedule to reflect weather forecasts and traffic information, it is possible to provide highly accurate plans.

[1760] A "destination" is a geographical location that a user sets as a destination in their travel plans or daily activities.

[1761] The "departure date and time" is the date and time that the user sets as the start time of the travel plan or activity.

[1762] A "mobile terminal" is an electronic device that a user can carry around, and includes smartphones, tablets, and the like.

[1763] A "server" is a computer system on a network that processes and stores data and communicates with external systems.

[1764] A "generative AI model" is an artificial intelligence system that automatically generates time schedules and proposals based on input information from users.

[1765] A "time schedule" is a timetable that a user plans to follow when traveling or performing daily activities.

[1766] An "external system" is an information providing system managed by a third party that the server accesses and uses to collect information.

[1767] "Gourmet information" is information about restaurants, including types of food, ratings, addresses, etc.

[1768] "Tourist information" refers to information about tourist spots, including the characteristics of tourist spots and access information.

[1769] A "shopping list" is a list of items that a user wishes to purchase.

[1770] A "navigation app" is software that users use to search for and navigate their way to a destination.

[1771] "Route information" refers to information about the route from the departure point to the destination provided by the navigation app.

[1772] "Store information" refers to information about the location, opening hours, stock status, etc. of shops and supermarkets.

[1773] A "weather forecast" is information predicting future weather conditions in a particular area.

[1774] "Traffic information" refers to information about road congestion and the operation status of public transportation.

[1775] This invention is a system that efficiently supports travel planning and daily activities by linking a mobile device with a server. The system starts when the user inputs information such as the destination, departure date and time, and shopping list into the mobile device. The server uses a generative AI model to propose optimal schedules and stopover stores, and provides this information to the user.

[1776] When planning a trip

[1777] First, the user launches a travel planning app on their smartphone and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00). This information is sent from the device to the server. The server analyzes the received information and uses a generative AI model (e.g., GPT-4) to create an optimal time schedule for the user's travel plans. Specifically, the following prompt sentence is input into the generative AI model:

[1778] Example prompt sentence:

[1779] "I'm planning a trip to Kyoto. I'll be leaving on August 15th at 10:00. Can you suggest some tourist spots and good restaurants?"

[1780] Next, the server collects information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule. This information includes details of planned tourist spots and restaurants. The server sends the generated time schedule and collected information to the mobile device, which displays it to the user in an easy-to-understand format. The user can check the displayed schedule and adjust each item as necessary.

[1781] During normal use

[1782] The user enters a shopping list (e.g., milk, bread, eggs) into a reminder app (e.g., Google Keep). This shopping list is sent from the device to the server. Next, the user launches a navigation app (e.g., Google Maps), enters a destination (e.g., from home to office), and searches for a route. This route information is also sent from the device to the server. The server compares the received route information with the shopping list and searches for the best stores to stop at along the route. Specifically, it generates a prompt sentence like the following:

[1783] Example prompt sentence:

[1784] "I've added milk, bread, and eggs to my shopping list. Can you tell me the best store where I can buy these items on my way from my home to the office?"

[1785] The server uses an external system (e.g., store information API) to check inventory information and business status and select the most suitable candidate store. This store information is sent to the mobile device, which then makes suggestions to the user. The user can then check the suggested store information and decide which store to visit.

[1786] In this way, the system of the present invention provides efficient and convenient travel planning and daily activity support for users. The server also has the ability to reflect weather forecasts and traffic information in the time schedule it generates, providing even more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1787] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1788] Process steps when planning a trip

[1789] Step 1:

[1790] User: Launches a travel planning app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1791] Input: Destination and departure date and time information

[1792] Output: Input data ready to send

[1793] Specific operation: The user enters the required information into the input form and presses the "Submit" button.

[1794] Step 2:

[1795] Terminal: Generates request data including the input destination and departure date and time, and sends it to the server.

[1796] Input: User-entered destination and departure date and time information

[1797] Output: Request for destination and departure date and time sent to server

[1798] What happens: The app formats the request data into JSON format and sends it as a POST request to the API endpoint.

[1799] Step 3:

[1800] Server: Analyzes the received request data and creates and inputs prompts to the generative AI model (e.g., GPT-4) to generate the optimal time schedule.

[1801] Input: Request data in JSON format

[1802] Output: Prompt sentences to be input to the generative AI model and the generated time schedule

[1803] Specific operation: The server analyzes the request data and generates a prompt such as, "I'm planning a trip to Kyoto. I'll be leaving at 10:00 on August 15th. Please suggest tourist spots and good restaurants." and inputs this into the AI ​​model.

[1804] Step 4:

[1805] Server: Collects additional information from external travel-related systems (e.g., gourmet information API, tourist information API) and reflects it in the time schedule.

[1806] Input: Generated time schedule

[1807] Output: Appended time schedule

[1808] Specific operation: The server calls an API from an external system, obtains information about tourist spots and gourmet restaurants, and adds it to the time schedule.

[1809] Step 5:

[1810] Server: The created time schedule and collected additional information are formatted into a format that can be displayed on a mobile device (e.g., JSON format) and sent to the device.

[1811] Input: Added time schedule and additional information

[1812] Output: JSON formatted time schedule with additional information ready to send

[1813] Specific operation: The server formats the data into JSON format and returns it to the mobile device as a response.

[1814] Step 6:

[1815] Terminal: Displays the received time schedule and information on tourist spots and gourmet restaurants in a format that is easy for the user to understand.

[1816] Input: JSON format time schedule and additional information

[1817] Output: A timetable display including information on tourist attractions and places to eat

[1818] What it does: The app parses the JSON data and displays it to the user in a list or map format.

[1819] Step 7:

[1820] User: Review the schedule and information displayed and adjust items as needed.

[1821] Input: Displayed time schedule and information

[1822] Output: Modified time schedule

[1823] Specific operation: The user checks each item in the schedule and, if necessary, presses the edit button to make changes.

[1824] Normal usage process steps

[1825] Step 1:

[1826] User: Enters a shopping list (e.g., milk, bread, eggs) into a reminders app.

[1827] Input: Shopping list

[1828] Output: Input data saved

[1829] Specific behavior: The user enters the name of the desired product into the input field of the Reminders app and presses the "Save" button.

[1830] Step 2:

[1831] Terminal: Generates request data including the input shopping list and sends it to the server.

[1832] Input: Shopping list

[1833] Output: Shopping list request data ready to send

[1834] What happens: The app formats the reminder data into JSON format and sends it to the server as a POST request.

[1835] Step 3:

[1836] User: Opens a navigation app and enters a route from home to office to search.

[1837] Input: Origin and destination information

[1838] Output: Route information search results

[1839] Specific action: A user enters the locations of "home" and "office" in the search bar of a navigation app and presses the "Search" button.

[1840] Step 4:

[1841] Terminal: The route information is converted into JSON format and sent to the server.

[1842] Input: Route information

[1843] Output: Route information request data ready to send

[1844] Specific behavior: The app formats the route information of the search results into JSON format and sends it to the server as a POST request.

[1845] Step 5:

[1846] Server: Matches the received route information with the shopping list and searches for the best stores to stop at.

[1847] Input: Route information and shopping list

[1848] Output: List of candidate stores

[1849] Specific operation: Based on the route information and shopping list, the server uses an external store information API to search for the most suitable store and checks inventory and business status.

[1850] Step 6:

[1851] Server: Generates and sends JSON data to send information on the best candidate stores to the mobile device.

[1852] Input: List of potential stores

[1853] Output: Potential store data in JSON format

[1854] Specific operation: The server formats the information about the selected store into JSON format and sends it as a response to the mobile device.

[1855] Step 7:

[1856] Terminal: Analyzes the received information on candidate stores and makes suggestions to the user.

[1857] Input: JSON format store candidate data

[1858] Output: Display of candidate stores

[1859] Specific behavior: The app parses the JSON data and displays candidate stores to the user in map and list format.

[1860] Step 8:

[1861] User: Review the suggested store information and decide whether to add the stop to their route.

[1862] Input: Information about the candidate store

[1863] Output: Final route decision

[1864] Specific operation: The user checks the displayed store information and, if necessary, presses the "Stop" button to add the location to the route.

[1865] This concretely executes the processing steps of the entire system, allowing users to efficiently manage their travel plans and daily activities.

[1866] (Application example 1)

[1867] 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."

[1868] Currently, there are many travel planning support systems and daily activity support systems, but these systems only generate static schedules based on data entered by the user. Furthermore, systems that can provide efficient route guidance and schedule adjustments in real time when users travel using autonomous vehicles are not yet widely available. Therefore, the challenge is to efficiently optimize travel plans and daily activities in cooperation with autonomous vehicles.

[1869] 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.

[1870] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile device, means for transmitting the input destination and departure date and time to the server, means for generating a time schedule using generative artificial intelligence based on the information received by the server, means for the server to collect information about the area around the destination, means for transmitting the schedule generated by the server and the collected information to the mobile device, means for the mobile device to display the schedule and information to the user, and means for transmitting route information and departure times to the autonomously driven vehicle based on the generated schedule. This enables a user to easily create a travel plan or schedule of daily activities using the mobile device and give instructions to the autonomously driven vehicle based on the schedule to efficiently travel and make stops.

[1871] A "mobile terminal" refers to an electronic device that a user can carry around and that can input and display information.

[1872] "Destination" refers to the final location where a user travels or moves.

[1873] "Departure date and time" refers to the date and time when the user starts traveling or moving.

[1874] "Server" refers to a central computer system that receives and processes data sent by users.

[1875] "Generative artificial intelligence" refers to artificial intelligence that can generate new information and schedules based on large amounts of data.

[1876] A "time schedule" refers to a plan that organizes a user's movements and activities along a time axis.

[1877] "Route information" refers to the route from the departure point to the destination and detailed information on the route.

[1878] An "autonomous vehicle" is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[1879] "Weather forecast" refers to a service that predicts future weather and provides that information.

[1880] "Traffic information" refers to real-time information about travel, such as road congestion, accident information, and public transportation operation status.

[1881] The present invention provides a system that efficiently supports users in planning their travels and daily activities by linking a mobile terminal and a server. Specific embodiments of the system are described below.

[1882] System configuration

[1883] 1. Mobile devices

[1884] An electronic device that users can carry around and that allows them to input and display information, such as a smartphone or tablet.

[1885] 2. Server

[1886] It is a central computer system that receives and processes data sent by users, and uses generative artificial intelligence to create time schedules and collect information.

[1887] 3. Generative Artificial Intelligence

[1888] It is an artificial intelligence that can generate new information and schedules based on large amounts of data. Specifically, OpenAI GPT-3 and other models are used.

[1889] 4. Self-driving vehicles

[1890] It is a vehicle that uses artificial intelligence and sensor technology to drive autonomously without a human driver.

[1891] Data entry and processing

[1892] 1. Travel Planning

[1893] The user uses the mobile terminal to input the travel destination and departure date and time.

[1894] The terminal transmits the input destination and departure date and time to the server.

[1895] The server uses generative artificial intelligence to create a time schedule based on the received information, including travel time and tourist spots and restaurants to visit after arriving at the destination.

[1896] The server collects additional information using tourist information APIs and gourmet information APIs around the destination and reflects this information in the time schedule.

[1897] The server transmits the generated schedule and the collected information to the mobile terminal.

[1898] The terminal displays schedules and information in a user-friendly format.

[1899] The terminal transmits route information and departure times to the autonomous vehicle based on the generated schedule, so that the autonomous vehicle departs according to the schedule.

[1900] Specific examples

[1901] If a user selects "Tokyo Disneyland" as their destination and sets the departure date and time as "September 10th, 9:00 AM," the system will optimize the user's travel plan based on that information and send instructions to the self-driving vehicle. An example of the prompt text is as follows:

[1902] Destination: Tokyo Disneyland

[1903] Departure date and time: September 10, 2023 09:00

[1904] Data entry and processing (shopping)

[1905] 1. Shopping plan

[1906] The user inputs a shopping list into the reminder app and sends it to the server using the device.

[1907] The user inputs a destination into the car navigation app and searches for a route, which is then sent to the server.

[1908] The server compares the received route information with the shopping list and searches for information on the best stores to stop at along the route.

[1909] The server transmits information on the best stores to visit to the mobile terminal.

[1910] The terminal displays the suggested store information to the user.

[1911] Program processing

[1912] The hardware includes servers and mobile terminals, and it is desirable for the servers to have high-performance processors and large amounts of memory.

[1913] The software includes generative artificial intelligence (e.g., OpenAI GPT-3), tourist information API, gourmet information API, navigation system, reminder app, and more.

[1914] Data processing and data calculation include schedule generation by the server, information collection using API, and analysis of user input data.

[1915] This allows users to easily create travel plans and schedules of daily activities using a mobile device, and then give instructions to an autonomous vehicle based on that schedule to travel and make stops efficiently.

[1916] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1917] Step 1:

[1918] The user inputs the travel destination and departure date and time using the mobile device. The data (prompt sentences) input by the user are saved in the mobile device.

[1919] Step 2:

[1920] The device sends the entered destination and departure date and time data to the server. Specifically, the device creates an HTTP POST request and sends it to a specific endpoint on the server. The input in this step is the user's input data, and the output is the request data sent to the server.

[1921] Step 3:

[1922] The server uses generative artificial intelligence (e.g., OpenAI GPT-3) to create a time schedule based on the information it receives. Specifically, the server uses a generative AI model to generate an optimal schedule based on the user's input (prompt). The input in this step is the destination and departure date and time sent from the device, and the output is the generated time schedule.

[1923] Step 4:

[1924] The server collects information about the area around the destination. Specifically, it uses the tourist information API and gourmet information API to obtain information about tourist spots and restaurants. The input in this step is information about the departure point and destination, and the output is the obtained tourist information and gourmet information.

[1925] Step 5:

[1926] The server sends the generated time schedule and collected information to the mobile device. The server formats this data into a specific format and sends it to the device using an HTTP POST request. The input in this step is the generated time schedule and collected information, and the output is the schedule data sent to the mobile device.

[1927] Step 6:

[1928] The terminal displays the schedule and information to the user. The terminal analyzes the received data and displays it in a format that is easy for the user to understand. For example, it displays the names of tourist attractions, visiting times, restaurant recommendations, etc. The input in this step is the schedule data sent from the server, and the output is the schedule information displayed to the user.

[1929] Step 7:

[1930] The terminal sends route information and departure times to the autonomous vehicle based on the generated schedule. The terminal formats this data and sends it to the autonomous vehicle's control system. The input in this step is the generated time schedule, and the output is the route information and departure times sent to the autonomous vehicle.

[1931] Step 8:

[1932] The autonomous vehicle departs according to the schedule and travels along the optimal route. The vehicle's control system starts the vehicle's operation based on the received route information and departure time. The input in this step is the route information and departure time sent from the terminal, and the output is the autonomous vehicle's driving behavior.

[1933] 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.

[1934] This invention is a system that combines a mobile device, a server, and an emotion engine to provide efficient and personalized support for travel planning and daily activities. The system begins when the user inputs information such as the destination, departure date and time, and shopping list on the mobile device. The server then uses generative artificial intelligence to suggest optimal schedules and stopover stores, and provides this information to the user. Furthermore, the emotion engine can recognize the user's emotions and adjust the suggestions based on that information.

[1935] When planning a trip

[1936] 1. Enter your destination and departure date and time

[1937] User: Launches the app on their smartphone and enters their travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1938] 2. Submit your input

[1939] Terminal: Formats the input destination and departure date and time and sends a request to the server.

[1940] 3. Generate a time schedule

[1941] Server: Analyzes the received request and uses generative artificial intelligence to create a time schedule that takes into account the travel time from the user's departure point to their destination, as well as tourist spots and restaurants to visit after arriving.

[1942] 4. Collection of additional information

[1943] Server: Collects information about tourist spots and restaurants around the destination from the Gourmet Information API and Tourism Information API, and reflects this information in the time schedule.

[1944] 5. Emotional Information Analysis

[1945] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[1946] 6. Tailor your offers based on emotions

[1947] Server: Based on the received emotional information, the server adjusts the generated time schedule and additional information according to the user's emotional state. For example, if the user is tired, the server may suggest more rest spots.

[1948] 7. Schedule and Information Submission

[1949] Server: Formats the generated time schedule and collected additional information into a format compatible with mobile devices and sends them to the devices.

[1950] 8. Displaying Information to the User

[1951] Terminal: Displays the time schedule and additional information in a user-friendly format (e.g., tourist attractions and their visiting times, recommendations for gourmet restaurants, etc.).

[1952] User: Review the schedule and information displayed and make any necessary adjustments.

[1953] During normal use

[1954] 1. Registering a shopping list

[1955] User: Enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[1956] 2. Send your shopping list

[1957] Terminal: Sends the entered shopping list to the server.

[1958] 3. Enter your destination and search for a route

[1959] User: Opens a navigation app, enters a destination, and searches for a route (e.g., from home to office).

[1960] 4. Sending route information

[1961] Device: Sends route information searched by the car navigation app to the server.

[1962] 5. Matching routes and shopping lists

[1963] Server: Matches the received route information with the shopping list and searches for suitable stores along the route, taking into account store inventory and opening hours.

[1964] 6. Submit your candidate stores

[1965] Server: Sends the best candidate store information to the device.

[1966] 7. Store suggestions to users

[1967] Terminal: Based on the received store information, the terminal suggests possible stores for the user to stop at (e.g., presenting the nearest supermarket that can be stopped at on the way).

[1968] 8. Emotional Information Analysis

[1969] Terminal: The emotion engine analyzes the user's voice and facial expressions and sends the user's emotional information to the server.

[1970] 9. Adjust your offers based on emotions

[1971] Server: Based on the received emotion information, the server adjusts the suggested stores to stop at and the shopping items to be purchased. For example, if the user is in a hurry, the server suggests the most efficient route and stores.

[1972] 10. Final User Confirmation

[1973] User: Review the suggested information, choose whether to include the stop as a stop, and update the route.

[1974] In this way, a system can be constructed that provides efficient and personalized services to users. In this invention, the emotion engine is used to make suggestions based on the user's emotional state, providing the user with more accurate plans. Users can optimize their travel plans and daily shopping, saving time and effort.

[1975] The processing flow will be explained below.

[1976] When planning a trip

[1977] Step 1:

[1978] The user launches the smartphone app and enters the travel destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00).

[1979] Step 2:

[1980] The terminal formats the input destination and departure date and time information and sends a request to the server.

[1981] Step 3:

[1982] The server analyzes the received request and uses generative artificial intelligence to create a time schedule, taking into account travel time from the user's departure point to their destination, as well as recommended tourist spots and dining options.

[1983] Step 4:

[1984] The server collects information about tourist spots and gourmet restaurants around the destination from the gourmet information API, tourist information API, and other databases, and reflects this information in the time schedule.

[1985] Step 5:

[1986] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, it uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice.

[1987] Step 6:

[1988] The device formats the acquired emotional information and sends it to the server.

[1989] Step 7:

[1990] Based on the emotional information received from the emotion engine, the server adjusts the generated time schedule and suggestions according to the user's emotional state. For example, if the user is tired, it will suggest more rest spots.

[1991] Step 8:

[1992] The server transmits the adjusted time schedule and additional information to the mobile terminal.

[1993] Step 9:

[1994] The device displays the schedule and information received in a user-friendly format (e.g., tourist spots and their visiting times, gourmet restaurant recommendations, etc.).

[1995] Step 10:

[1996] The user reviews the presented schedule and information and makes any necessary adjustments.

[1997] During normal use

[1998] Step 1:

[1999] A user enters a shopping list into a reminders app (e.g., buy milk, bread, and eggs).

[2000] Step 2:

[2001] The terminal formats the input shopping list and sends it to the server.

[2002] Step 3:

[2003] A user launches a car navigation app, enters a destination, and searches for a route (e.g., from home to office).

[2004] Step 4:

[2005] The device formats the route information searched by the car navigation app and sends it to the server.

[2006] Step 5:

[2007] The server compares the route information received with the shopping list and searches for suitable stores along the route, taking into account store inventory and business hours.

[2008] Step 6:

[2009] The server formats the best candidate store information and sends it to the terminal.

[2010] Step 7:

[2011] The device's emotion engine analyzes the user's voice and facial expressions to obtain emotional information. For example, while driving, the car navigation system may say, "You seem a little tired today."

[2012] Step 8:

[2013] The device formats the acquired emotional information and sends it to the server.

[2014] Step 9:

[2015] The server adjusts the suggested stores to stop by and shopping contents based on the emotional information received from the emotion engine. For example, if the user is in a hurry, it suggests the most efficient route and stores.

[2016] Step 10:

[2017] The device displays tailored store information in a user-friendly format (e.g., showing the nearest supermarket to stop at on the way).

[2018] Step 11:

[2019] The user checks the information presented, selects whether to stop at the destination as a stopover, and updates the route.

[2020] In this way, users can plan their trips and make everyday purchases more efficient and personalized.

[2021] Example 2

[2022] 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."

[2023] Conventional travel planning and daily activity support systems do not take into account the user's emotional state when making suggestions, and therefore are often not optimized to the user's actual needs or physical condition. Furthermore, there is a problem in optimizing travel plans and shopping routes, in that they are unable to perform real-time emotional analysis and dynamic adjustments based on that analysis.

[2024] 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.

[2025] In this invention, the server includes means for a user to input a destination and departure date and time on a mobile information processing device, means for transmitting the input destination and departure date and time to the server, means for the server to create a time schedule using generative artificial intelligence based on the information received, means for the server to collect additional information about the area around the destination, means for the server to transmit the schedule generated by the server and the collected additional information to the information processing device, means for the information processing device to display the schedule and additional information to the user, means for the information processing device to analyze the user's facial expressions and voice, and means for the server to adjust the content of suggestions based on the user's emotional information. This makes it possible to suggest optimal travel plans and shopping routes based on the user's emotional state.

[2026] A "portable information processing device" is a device with computing capabilities that is portable and allows a user to input and send and receive information.

[2027] A "destination" refers to a location where a user is heading in a trip or travel.

[2028] "Departure date and time" refers to the date and time when a user starts a specific trip or movement.

[2029] A "server" is a central processing unit that works in conjunction with information processing units via a network and receives, analyzes, and transmits information.

[2030] "Generative artificial intelligence" refers to algorithms and models that dynamically generate information to provide to users based on large amounts of data.

[2031] A "time schedule" refers to a list of activities scheduled based on time in a particular plan.

[2032] "Additional information" refers to detailed information about a destination or route, or supplementary information relevant to the user.

[2033] "Analysis of facial expressions and voice" refers to the process of analyzing the user's face and voice to estimate their emotional state.

[2034] "Emotion information" refers to data that indicates the analyzed emotional state of the user.

[2035] "Adjusting the proposal content" refers to the process of optimizing the plan and proposal content provided based on the user's emotional information.

[2036] MODE FOR CARRYING OUT THE INVENTION

[2037] This invention provides a system that uses a mobile information processing device, a server, and an emotion analysis engine to provide efficient and personalized support for travel planning and daily activities. Specific implementation methods are described below.

[2038] System configuration

[2039] The system includes the following components:

[2040] 1. Portable information processing device:

[2041] For example, a smartphone or tablet is used, and users use these devices to input information such as their destination and departure date and time.

[2042] Use trip planning apps, reminder apps, and car navigation apps to input destinations, departure dates and times, shopping lists, route information, etc.

[2043] 2. Server:

[2044] The server cooperates with the information processing device via the network to receive, analyze, and transmit information on the destination and departure date and time.

[2045] As a generative artificial intelligence, for example, we use OpenAI's GPT model, which automatically generates a time schedule.

[2046] We use external APIs such as Google Places API and Yelp API to collect information on gourmet food and tourist spots around the destination.

[2047] 3. Sentiment Analysis Engine:

[2048] To analyze the user's facial expressions and voice, the Emotion API of Microsoft's Azure Cognitive Services is used, which acquires the user's emotional information and sends it to the server.

[2049] Example: Travel Planning

[2050] The following steps are taken by a user using a travel planning app on their smartphone to plan a trip to Kyoto on August 15th.

[2051] 1. Enter your destination and departure date and time:

[2052] The user launches the app on their smartphone and enters "Destination: Kyoto, Departure date and time: August 15th, 10:00."

[2053] 2. Generate time schedule:

[2054] The server uses the received information to generate a time schedule using the GPT model, which includes travel time from the departure point to the destination and tourist spots to visit after arriving.

[2055] 3. Additional Information Collection:

[2056] The server uses the Google Places API and Yelp API to collect information about tourist attractions and restaurants around the destination and reflects this information in the timetable.

[2057] 4. Emotional information analysis:

[2058] The smartphone analyzes the user's voice and facial expressions and sends emotional information to the server. If the analysis finds that the user is "tired," that information is sent to the server.

[2059] 5. Tailor your offers based on emotions:

[2060] The server adds more rest spots to the schedule based on the emotion information and provides it to the user, enabling personalized suggestions according to the user's state.

[2061] Example: Everyday shopping

[2062] The following steps show how a user can use a smartphone reminder app and a car navigation app to efficiently shop while on the move.

[2063] 1. Registering a shopping list:

[2064] A user enters "Shopping list: milk, bread, eggs" into a Reminders app.

[2065] 2. Sending route information:

[2066] The user enters "destination: office" into the car navigation app and searches for the optimal route.

[2067] 3. Proposal of candidate stores:

[2068] The server compares the received route information with the shopping list, searches for the best store information along the route, and sends the selected store information to the user's smartphone.

[2069] 4. Emotional information analysis:

[2070] The smartphone analyzes the user's voice and facial expressions, and if it determines that the user is in a hurry, it sends that information to the server.

[2071] 5. Tailor your offers based on emotions:

[2072] The server then uses the emotion information to suggest the most efficient route and the nearest store. For example, if the user feels "in a hurry," it will suggest a convenience store on the shortest route.

[2073] Prompt Sentence Examples

[2074] Below are some examples of specific travel planning prompts:

[2075] Travel Planner

[2076] Destination: Kyoto

[2077] Departure date and time: August 15th, 10:00

[2078] User Emotion: Tired

[2079] Generate optimal travel itineraries based on user requests and include plenty of rest stops.

[2080] Below are some examples of specific prompts for everyday shopping:

[2081] Regular use

[2082] Shopping list: milk, bread, eggs

[2083] Destination: Office

[2084] Route: Home to office

[2085] User Sentiment: Urgent

[2086] Suggest the best shopping store and route.

[2087] As described above, this system can provide optimal suggestions based on the user's condition and needs, enabling efficient and personalized travel planning and support for daily activities.

[2088] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2089] Travel planning process

[2090] Step 1:

[2091] A user launches a travel planning app on their smartphone and inputs a destination (e.g., Kyoto) and departure date and time (e.g., August 15th, 10:00 AM) by typing the information into the app's text fields and pressing the submit button.

[2092] Input: Destination and departure date and time

[2093] Output: None (preparing input data)

[2094] Step 2:

[2095] The device formats the destination and departure date and time entered by the user and sends it to the server via an HTTP POST request, which is sent as JSON-formatted data.

[2096] Input: User input data (destination and departure date and time)

[2097] Output: Request sent to server

[2098] Step 3:

[2099] The server analyzes the received requests and generates a time schedule using generative artificial intelligence (GPT model). The server analyzes the received data and creates a schedule that takes into account travel time and tourist spots to visit after arrival.

[2100] Input: Request from the device (destination and departure date and time)

[2101] Output: Generated time schedule

[2102] Step 4:

[2103] The server uses the Google Places API, Yelp API, etc. to collect information on tourist spots and gourmet restaurants around the destination, analyzes the information obtained from the API, and adds it to the generated time schedule.

[2104] Input: Destination information

[2105] Output: Added tourist spots and gourmet restaurant information

[2106] Step 5:

[2107] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[2108] Input: User's voice and facial expression data

[2109] Output: Parsed emotion information

[2110] Step 6:

[2111] The server adjusts the generated time schedule and additional information based on the received emotion information. For example, if the server determines that the user is "tired," it adds more rest spots to the schedule.

[2112] Input: Emotion information

[2113] Output: Adjusted time schedule and additional information

[2114] Step 7:

[2115] The server then formats the final generated time schedule and additional information into JSON format and sends it back to the terminal.

[2116] Input: Adjusted time schedule and additional information

[2117] Output: Data sent to the terminal

[2118] Step 8:

[2119] The device displays the schedule and information received from the server in an easy-to-understand format to the user, such as a list of tourist spots to visit, the times to visit them, and recommended restaurants.

[2120] Input: Data sent from the server

[2121] Output: Schedules and information displayed to the user

[2122] Processing during normal use

[2123] Step 1:

[2124] A user enters a shopping list (e.g., milk, bread, eggs) into a reminder app, which stores the list in a local database and prepares it for sending.

[2125] Input: Shopping list item

[2126] Output: None (preparing input data)

[2127] Step 2:

[2128] The terminal sends the shopping list entered by the user to the server via an HTTP POST request. This list is sent as JSON format data.

[2129] Input: A shopping list entered by the user

[2130] Output: Request sent to server

[2131] Step 3:

[2132] A user starts a car navigation app, inputs a destination (e.g., an office), and searches for a route. This information is processed within the car navigation app, and the route information provided is generated.

[2133] Input: Destination information

[2134] Output: Route information

[2135] Step 4:

[2136] The device sends route information searched by the car navigation app to the server, which is also sent in JSON format.

[2137] Input: Route information

[2138] Output: Request sent to server

[2139] Step 5:

[2140] The server compares the received route information with the shopping list and searches for appropriate stores along the route, taking into account each store's inventory and business hours.

[2141] Input: Route information and shopping list

[2142] Output: List of optimal store candidates

[2143] Step 6:

[2144] The server formats the best candidate store information in JSON format and sends it to the terminal.

[2145] Input: List of optimal store candidates

[2146] Output: Data sent to the terminal

[2147] Step 7:

[2148] The device will then suggest possible stores to the user based on the received store information, and the app screen will display the nearest supermarkets that the user can stop at along the way.

[2149] Input: Data sent from the server

[2150] Output: Store suggestions to the user

[2151] Step 8:

[2152] The device uses an emotion analysis engine to analyze the user's voice and facial expressions, and the analysis results are formatted in JSON format and sent to the server.

[2153] Input: User's voice and facial expression data

[2154] Output: Parsed emotion information

[2155] Step 9:

[2156] The server then adjusts the suggested stores to stop by and the shopping items to be purchased based on the received emotional information. For example, if the user is in a hurry, it will suggest the most efficient route and stores.

[2157] Input: Emotion information

[2158] Output: Adjusted proposal

[2159] Step 10:

[2160] The user reviews the proposed information, selects whether to include stops as intermediate locations, and updates the route. Once the user gives final approval, the system resends the updated information to the server.

[2161] Input: User selection and confirmation

[2162] Output: The final route

[2163] (Application example 2)

[2164] 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."

[2165] Conventional travel planning and daily shopping route guidance systems have difficulty responding to the user's emotional state and real-time environmental changes. In particular, in autonomous vehicles, optimal route suggestions and selection of stops often do not take the driver's emotional state into account, resulting in a loss of comfort and convenience for the user. Furthermore, fixed route suggestions without understanding the driver's emotional state can increase the user's stress and fatigue.

[2166] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input a destination and departure date and time on a mobile terminal; a means for transmitting the input destination and departure date and time to the server; a means for the server to create a time schedule using a generation AI system based on the information received; a means for the server to collect information about the area around the destination; a means for the server to transmit the schedule generated by the server and the collected information to the mobile terminal; a means for the mobile terminal to analyze the user's emotional state; a means for the server to adjust the schedule based on the user's emotional information; and a means for the mobile terminal to display the schedule and information to the user visually or audibly. This makes it possible to grasp the user's emotional state in real time and suggest optimal routes and stops based on that state.

[2167] "Mobile terminal" refers to a relocatable, portable electronic device that allows a user to input information and exchange information with a server.

[2168] A "generative AI system" is an artificial intelligence technology that has the ability to generate and predict appropriate schedules and routes based on the information it receives.

[2169] A "time schedule" is a specific action plan that organizes the user's planned activities by date and time.

[2170] "Emotion information" is information about the emotional state obtained by analyzing data such as the user's facial expressions and voice.

[2171] "Means for visually or audibly displaying schedules and information" means the functionality of a mobile terminal to provide information to a user through a screen display or audio output.

[2172] A "stop-by store" is a store or facility that a user visits temporarily while traveling, and is a place used for shopping, resting, etc.

[2173] A "server" is a central processing unit that receives requests from clients (for example, mobile terminals) via a network and processes and provides information.

[2174] The system for implementing this invention mainly comprises a mobile terminal, a server, a generative AI system, and an emotion analysis engine. A specific implementation method will be described in detail below.

[2175] First, the user inputs the travel destination and departure date and time into a mobile device. The mobile device can be a smartphone, tablet, or in-car display. The input information is then sent to a server via a network. The server then uses a pre-designed generative AI system to create an optimal time schedule based on the received destination and departure date and time.

[2176] The generative AI system optimizes schedules in real time, taking into account information such as travel time from the user's departure point to their destination, traffic conditions, weather forecasts, and tourist spots they want to visit. It also obtains gourmet and tourist information around the destination through external APIs and provides it to users.

[2177] Next, the mobile device uses its built-in camera and microphone to analyze the user's facial expressions and voice to obtain emotional information. This process is performed using an emotion analysis engine, such as Microsoft Azure's Emotion API. The analyzed emotional information is then sent back to the server, where the schedule generated on the server side is adjusted to match the user's emotional state.

[2178] For example, if the user is tired, the system will suggest more spots for rest and relaxation, while if the user is energetic, it will dynamically adjust the places to visit, suggesting more active tourist spots.

[2179] The final time schedule is then sent to the mobile device and presented to the user visually or audibly. Visual display is provided on the smartphone screen or the vehicle display, while audio guidance is provided through the audio speaker.

[2180] As a concrete example, the following prompt sentences are input into a generative AI model to generate a schedule:

[2181] "The departure point is the current location, the destination is Tokyo Station, and the departure time is 10:00 on August 15, 2023. The user is in a calm emotional state. Based on this state, please generate the optimal route that includes a stop at the museum."

[2182] In this way, a user of the present invention can obtain an optimal travel plan that matches his or her emotional state, and can realize more comfortable and efficient travel.

[2183] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2184] Step 1:

[2185] The user inputs the travel destination and departure date and time on the mobile terminal.

[2186] Input: The destination (e.g. Tokyo Station) and departure date and time (e.g. August 15, 2023, 10:00) entered by the user.

[2187] Output: Input data stored on the mobile device.

[2188] Step 2:

[2189] The mobile terminal transmits the input destination and departure date and time to the server.

[2190] Input: Destination and departure date and time data obtained in step 1.

[2191] Output: The request data sent to the server.

[2192] Step 3:

[2193] Based on the information received by the server, a time schedule is created using a generative AI model.

[2194] Input: Destination and departure date / time data sent in step 2.

[2195] Processing: The generative AI model generates a schedule taking into account travel time from the user's origin to their destination, traffic information, and weather forecasts.

[2196] Output: The generated time schedule data.

[2197] Step 4:

[2198] The server collects information about the area around the destination.

[2199] Input: Destination information.

[2200] Processing: The server collects information on tourist spots and gourmet restaurants around the destination through the gourmet information API and tourist information API.

[2201] Output: Collected tourist spot and gourmet restaurant information.

[2202] Step 5:

[2203] The mobile device uses a built-in camera and microphone to analyze the user's emotional state and transmit it to a server.

[2204] Input: User's facial and voice data.

[2205] Processing: Analyze emotional information using a sentiment analysis engine (e.g., Microsoft Azure Emotion API).

[2206] Output: Parsed emotion information is sent to the server.

[2207] Step 6:

[2208] The server-generated schedule and collected information are adjusted based on the user's emotional state.

[2209] Input: Time schedule data generated in step 3, information collected in step 4, and emotion information sent in step 5.

[2210] Processing: Dynamically adjust the time schedule and suggested tourist spots based on emotion information. For example, if the user is tired, a schedule with more breaks will be suggested.

[2211] Output: Adjusted schedule and stop data.

[2212] Step 7:

[2213] The server transmits the generated schedule and collected information to the mobile terminal.

[2214] Input: Schedule and stop data adjusted in step 6.

[2215] Output: Transmitted to mobile device.

[2216] Step 8:

[2217] The mobile terminal displays the schedule and information visually and audibly to the user.

[2218] Input: Schedule and stop-off point data received from the server in step 7.

[2219] Processing: The mobile terminal provides information to the user through a screen display and audio output.

[2220] Output: The visual and audio information provided to the user.

[2221] 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.

[2222] 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.

[2223] 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.

[2224] 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.

[2225] 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.

[2226] 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.

[2227] 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).

[2228] 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.

[2229] 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."

[2230] 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.

[2231] 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).

[2232] 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.

[2233] 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.

[2234] 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.

[2235] 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.

[2236] 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.

[2237] 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.

[2238] 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.

[2239] 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.

[2240] 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.

[2241] 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.

[2242] The following is further disclosed regarding the above embodiment.

[2243] (Claim 1)

[2244] A means for a user to input a destination and departure date and time on a mobile terminal;

[2245] means for transmitting the input destination and departure date and time to a server;

[2246] A means for generating a time schedule using generative artificial intelligence based on the information received by the server;

[2247] A means for the server to collect information about the area surrounding the destination;

[2248] means for transmitting the schedule generated by the server and the collected information to the mobile terminal;

[2249] means for the mobile device to display schedules and information to the user;

[2250] A system including:

[2251] (Claim 2)

[2252] A means for transmitting a shopping list registered by a user to a server;

[2253] A means for transmitting route information input by a user to a car navigation application to a server;

[2254] A means for searching for store information along the route based on the route information and shopping list received by the server;

[2255] A means for the server to transmit information on the best store to visit to the mobile device;

[2256] A means for the mobile terminal to suggest store information to a user;

[2257] 10. The system of claim 1, comprising:

[2258] (Claim 3)

[2259] A means for reflecting weather forecasts and traffic information in the time schedule generated by the server;

[2260] A means for dynamically adjusting tourist spots and restaurants to be visited based on information received from the user by the server;

[2261] a means for displaying the schedule to the user in a visually easy-to-understand format on the mobile terminal;

[2262] 10. The system of claim 1, comprising:

[2263] "Example 1"

[2264] (Claim 1)

[2265] A means for a user to input a destination and departure date and time on...

Claims

1. A means for a user to input a destination and departure date and time on a mobile terminal; means for transmitting the input destination and departure date and time to a server; A means for generating a time schedule using generative artificial intelligence based on the information received by the server; A means for the server to collect information about the area surrounding the destination; means for transmitting the schedule generated by the server and the collected information to the mobile terminal; means for the mobile device to display schedules and information to the user; A system including:

2. A means for transmitting a shopping list registered by a user to a server; A means for transmitting route information input by a user to a car navigation application to a server; A means for searching for store information along the route based on the route information and shopping list received by the server; A means for the server to transmit information on the best store to visit to the mobile device; A means for the mobile terminal to suggest store information to a user; The system of claim 1 , comprising:

3. A means for reflecting weather forecasts and traffic information in the time schedule generated by the server; A means for dynamically adjusting tourist spots and restaurants to be visited based on information received from the user by the server; a means for displaying the schedule to the user in a visually easy-to-understand format on the mobile terminal; The system of claim 1 , comprising:

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