System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining a current location of a user; means for registering a home address, a nearest station, and preferences of the user; means for collecting real-time information of transportation; means for calculating an optimal homecoming route based on the above information; and means for providing the calculated route information.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] This invention relates to a system that helps users find the optimal route home after the last train. When choosing how to get home after missing the last train, many users tend to choose expensive means of transportation. There is a need to solve this problem and enable users to get home quickly while keeping transportation costs down. In particular, there is a need for a system that can suggest individual routes home that take into account the user's preferences and past travel history. [Means for solving the problem]

[0005] The present invention provides a system including a means for acquiring a user's current location, a means for registering the user's home address, nearest station, and preferences, a means for collecting real-time information on transportation, a means for calculating an optimal route home based on the above information, and a means for providing the calculated route information. The system also includes a means for transmitting the user's current location information to a server, which then recalculates the route based on information on delays and congestion on transportation and notifies the user. This allows the user to make appropriate decisions based on real-time information while traveling. The system also includes artificial intelligence that learns the user's past travel history and understands the user's preferences and patterns, thereby enabling individually customized route suggestions for home. Furthermore, the system estimates travel costs for the calculated route information and presents them to the user, allowing the user to select the optimal means of returning home while minimizing costs. The system also includes a means for aggregating and temporarily storing operation information from multiple transportation modes, enabling highly accurate route suggestions.

[0006] "User" refers to individuals or corporations who use the system, especially those looking for a route home after the last train.

[0007] "Current location" is information that indicates the user's location and is obtained using location information services such as GPS.

[0008] "Home address" is information indicating the address of the place where the user mainly lives.

[0009] "Nearest station" refers to the train station closest to the user's home address.

[0010] "Preferences" refers to information that indicates a user's preferences or wishes regarding transportation and route home.

[0011] "Registration" refers to the act of a user entering and saving information such as their home address, nearest station, and preferences into the application.

[0012] "Transportation" refers to public transportation such as trains, buses, and taxis.

[0013] "Real-time information" refers to the latest operational status and congestion information at that moment, and is constantly updated.

[0014] "Collecting" refers to the act of gathering necessary information, including the server obtaining information on the operation status of public transport.

[0015] A means of calculating a "route" refers to a method or device that combines various information to derive the optimal route to the user's destination.

[0016] "Providing" refers to the act of communicating calculated results or information to the user.

[0017] "Server" refers to a computer system that receives and processes data sent by users.

[0018] "Delay information" refers to information regarding delays from the scheduled operating time.

[0019] "Crowd information" refers to information about the number of passengers and the degree of congestion on public transport.

[0020] "Notification" refers to the act of informing the user of information, and primarily includes the form of information displayed on the device.

[0021] "Artificial intelligence" refers to technology that automatically responds to user behavior patterns and preferences based on machine learning and data analysis.

[0022] "Travel history" refers to records of a user's past travel routes and the means of transportation used.

[0023] "Estimate" refers to the act of calculating the cost of a route in advance and informing the user.

[0024] "Aggregation" refers to the act of bringing together dispersed information.

[0025] "Temporary storage" refers to the act of storing necessary information for a short period of time and making it available at any time. [Brief explanation of the drawings]

[0026] [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

[0027] 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.

[0028] First, the terms used in the following description will be explained.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] [First embodiment]

[0035] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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."

[0047] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. The specific processing contents of the system and examples are explained below.

[0048] Registering user information

[0049] 1. User (device): Enter information

[0050] Users launch the app and register their home address, nearest station, commuting pattern, and preferences. For example, a user enters information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[0051] 2. Terminal: Information transmission

[0052] The device sends the entered user information to the server, confirms that a connection with the server has been established, and encrypts and transmits the data.

[0053] 3. Server: Information storage

[0054] The server stores the received user information in a database, which the AI ​​model can later use to learn the user's individual needs.

[0055] 4. Server: AI Learning

[0056] The server trains the AI ​​model based on the stored user information, building a personalized predictive model based on the user's commuting patterns and preferences.

[0057] Real-time traffic information collection

[0058] 5. User (Device): Enable GPS

[0059] The user launches the app after the last train and enables GPS, which then obtains the user's current location and sends that information to the server.

[0060] 6. Device: Send current location

[0061] The device sends the user's current location information to the server in real time and continuously transmits GPS data.

[0062] 7. Server: Traffic information collection

[0063] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, temporarily stores this information, and updates it as needed.

[0064] Optimal route suggestions

[0065] 8. Server: Optimal route calculation

[0066] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[0067] 9. Server: Route proposal generation

[0068] The server then generates a specific route home based on the calculation results, suggesting, for example, "walk to Shinjuku Station, take the train to Shibuya Station, and then take a taxi from Shibuya Station to Roppongi Station."

[0069] 10. Server: Send Proposal

[0070] The server then sends the generated route proposal to the device, including route details and a total cost estimate.

[0071] 11. Terminal: Route display

[0072] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0073] Real-time information updates

[0074] 12. User (device): Continuously send current location

[0075] Users keep the app open while they move around, and the app continuously obtains their location and sends updates to the server if there are any changes.

[0076] 13. Server: Traffic Condition Monitoring

[0077] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[0078] 14. Server: Route Recalculation

[0079] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[0080] 15. Server: Send Re-Proposal

[0081] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0082] 16. Terminal: Reroute Display

[0083] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0084] This allows users to find the best route home even after the last train, allowing them to get home quickly while keeping travel costs down.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences using a form, and then presses the "Submit" button once the information is complete.

[0088] Step 2:

[0089] The terminal sends the entered information to the server. At this time, the data is encrypted and the information is sent using a secure communication protocol. The terminal then checks the connection with the server.

[0090] Step 3:

[0091] The server stores the received user information in a database, which is then used to train the AI ​​model.

[0092] Step 4:

[0093] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[0094] Step 5:

[0095] The user (device) restarts the app after the last train and enables the GPS function, and the app obtains the user's current location.

[0096] Step 6:

[0097] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[0098] Step 7:

[0099] The server uses a traffic information API to collect real-time information on train, bus, and taxi service status and congestion information around the user's current location. The acquired information is temporarily stored in a database.

[0100] Step 8:

[0101] The server calculates the optimal route home based on the user's current location, registered user information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[0102] Step 9:

[0103] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[0104] Step 10:

[0105] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0106] Step 11:

[0107] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0108] Step 12:

[0109] The user (device) keeps the app open while moving, and the current location is continuously acquired. If there is a change, the current location information is updated and sent to the server.

[0110] Step 13:

[0111] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[0112] Step 14:

[0113] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[0114] Step 15:

[0115] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0116] Step 16:

[0117] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0118] Example 1

[0119] 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."

[0120] Currently, many users face difficulties in finding a way to get home after the last train. This is especially true at night, when transportation options are limited or when transportation is affected by delays or congestion. Furthermore, because proposed routes do not reflect the user's individual preferences or commuting patterns, this can be time-consuming and costly. This invention is proposed to solve these problems.

[0121] 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.

[0122] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, commuting pattern, and preferences, means for transmitting encrypted user information to the server, means for collecting real-time information on transportation facilities, means for calculating an optimal route home based on the user information and real-time traffic information, means for providing the calculated route information, and means for updating the traffic information in real time and recalculating the route as necessary. This allows the user to receive a real-time suggestion of an optimal route home according to traffic conditions, enabling them to return home quickly and efficiently.

[0123] "Means for obtaining the user's current location" refers to a means for accurately identifying the user's current location using location information technology such as GPS or Wi-Fi.

[0124] "Means for registering a user's home address, nearest station, commuting patterns, and preferences" means an interface or input method that allows a user to register their home address, nearest station, daily commuting patterns, and transportation preferences in the system.

[0125] "Means for sending encrypted user information to a server" refers to a technology that encrypts data to securely protect information entered by users and sends it to a server via the Internet.

[0126] "Means of collecting real-time information on transportation" refers to technology that uses transportation information services and APIs to obtain real-time information on the operation status and congestion of trains, buses, taxis, etc.

[0127] "Means for calculating the optimal route home based on user information and real-time traffic information" refers to an algorithm or calculation method that calculates the optimal route home, taking into account the information registered by the user and the collected current traffic conditions.

[0128] The "means for providing calculated route information" refers to an interface or output method for providing the calculated route home in a form that is easy for the user to understand.

[0129] "Means for updating traffic information in real time and recalculating routes as needed" refers to methods or algorithms that continuously update information in response to changing traffic conditions and recalculate a new route home as needed.

[0130] The present invention provides a system for assisting a user in finding the best route home after the last train. Specific embodiments of the system will be described below.

[0131] Overview and Structure

[0132] This system uses the user's current location, home address, nearest station, commuting pattern, and preference information to collect real-time traffic information and calculate and suggest the optimal route home. The main components of the system are the user's device, server, database, and traffic information provision API.

[0133] Hardware and software used

[0134] Device: The smartphone or tablet used by the user

[0135] Server: Cloud-based server (e.g., AWS, Microsoft Azure, Google Cloud)

[0136] Database: SQL or NoSQL database (e.g. MySQL, PostgreSQL, MongoDB)

[0137] Traffic information API: Real-time traffic information providers such as Google Maps API and OpenWeatherMap

[0138] Encryption technology: AES encryption

[0139] Data processing and calculation

[0140] 1. Enter and submit user information

[0141] Users launch a dedicated app on their smartphone and enter their home address, nearest train station, commuting pattern, and individual preferences.

[0142] The data entered is encrypted within the app and securely sent to the server.

[0143] 2. Server-side information processing

[0144] The server decrypts the received encrypted data and stores it in a database.

[0145] Using the stored data, a generative AI model learns users' commuting patterns and preferences, built using TensorFlow and PyTorch.

[0146] 3. Collection of real-time traffic information

[0147] When the user launches the app again and sends their current location information, the server calls the traffic information API to obtain real-time traffic conditions.

[0148] The retrieved information is temporarily stored in the Redis cache.

[0149] 4. Calculating the optimal route

[0150] The server calculates the optimal route home using Dijkstra's algorithm and A algorithm based on the user's current location, registered information, and collected real-time traffic information.

[0151] 5. Route presentation

[0152] The calculated route information is sent from the server to the terminal where it is presented to the user in visual and textual form.

[0153] This route information includes the mode of transportation (walking, train, taxi, etc.), travel time, cost estimates, etc.

[0154] 6. Real-time updates

[0155] The device continuously transmits its current location information, and the server monitors changes in traffic information.

[0156] If traffic conditions change, the server recalculates the optimal route and notifies the user of the new route.

[0157] Examples of specific examples and prompts

[0158] Examples:

[0159] User A lives in Roppongi, Minato-ku, Tokyo, and commutes from 9:00 to 18:00. He is heading home from Shinjuku Station after the last train has left. User A launches the app, enables GPS, and sends his current location to the server. The server uses the Google Maps API to obtain real-time traffic information and calculates the optimal route home, taking into account User A's commuting pattern and preferences. As a result, the server suggests the route: "Take the train from Shinjuku Station to Shibuya Station, then take a taxi from Shibuya Station to Roppongi." This route information is displayed on the app as a map and text.

[0160] Example prompt sentence:

[0161] "If my current location is Shinjuku Station and my home is Roppongi, Minato Ward, Tokyo, please suggest the best route to get home while keeping taxi fare to a minimum."

[0162] This allows users to efficiently obtain the optimal route home in real time.

[0163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0164] Step 1:

[0165] Input: The user enters their home address, nearest train station, commuting patterns, and preferences into the app.

[0166] How it works: The user launches the app on their smartphone and fills in the designated form with their home address (e.g., Roppongi, Minato-ku, Tokyo), nearest station (e.g., Roppongi Station), commuting pattern (e.g., 9:00 AM to 6:00 PM), and preferences (e.g., want to keep taxi fares to a minimum).

[0167] Output: User information entered on the terminal is generated.

[0168] Step 2:

[0169] Input: Home address, nearest station, commuting patterns, and preference data entered by the user.

[0170] Operation: The device encrypts the collected user information and sends it to the server using AES encryption, for example, AES-256.

[0171] Output: User information data sent to the server in encrypted form.

[0172] Step 3:

[0173] Input: Encrypted user information data.

[0174] How it works: The server decrypts the encrypted data it receives and stores it in a database. The data is decrypted using the corresponding encryption key.

[0175] Output: Decrypted user information stored in the database.

[0176] Step 4:

[0177] Input: Decrypted user information.

[0178] How it works: The server uses this user information to train a generative AI model, which is built using TensorFlow or PyTorch to learn individual patterns for each user.

[0179] Output: A trained generative AI model.

[0180] Step 5:

[0181] Input: trained generative AI model, user's current location.

[0182] How it works: The user restarts the app after the last train and enables GPS, causing the device to collect the user's location.

[0183] Output: Current location information is obtained on the device.

[0184] Step 6:

[0185] Input: User's current location.

[0186] What it does: The device starts streaming continuous GPS data to send real-time location information to the server. The data is sent using WebSockets.

[0187] Output: Real-time location data sent to the server.

[0188] Step 7:

[0189] Input: User location, real-time transport data.

[0190] How it works: The server uses a traffic information API (e.g., Google Maps API) to collect real-time information about train, bus, and taxi service availability and congestion information for the user's area. This information is stored in a Redis cache.

[0191] Output: Temporarily stored real-time traffic information.

[0192] Step 8:

[0193] Input: User's current location information, registered information, real-time traffic information, trained generative AI model.

[0194] How it works: The server uses Dijkstra's algorithm and the A algorithm to calculate the optimal route home based on this information, taking into account the user's preferences.

[0195] Output: The calculated optimal route home.

[0196] Step 9:

[0197] Input: The calculated optimal route home.

[0198] How it works: The server packages this route information in JSON format and sends it to the device.

[0199] Output: Optimal route home information sent from the server to the terminal.

[0200] Step 10:

[0201] Input: The best route home information submitted.

[0202] How it works: The device analyzes the route information it receives and displays it visually and textually on the user interface, and plots the optimal route on a map.

[0203] Output: Route information displayed to the user.

[0204] Step 11:

[0205] Input: The user's continuous location.

[0206] How it works: The user keeps the app open on their device while they move around, and the device continuously sends their location to the server.

[0207] Output: The latest location information sent to the server.

[0208] Step 12:

[0209] Input: Latest traffic information, latest user location information.

[0210] Operation: The server periodically checks the traffic information API to obtain and update the latest traffic status and congestion information.

[0211] Output: Updated latest traffic information.

[0212] Step 13:

[0213] Input: Updated traffic information, user's current location, trained generative AI model.

[0214] What it does: The server recalculates the optimal route based on this information and suggests a new route home if necessary.

[0215] Output: The new recalculated optimal route.

[0216] Step 14:

[0217] Input: The new recalculated optimal route.

[0218] Operation: The server sends the recalculated route in JSON format to the device and notifies the user.

[0219] Output: The new best route information sent to the device.

[0220] Step 15:

[0221] Input: New optimal route information.

[0222] What it does: The device parses the new route information and updates the user interface, notifying the user of any changes.

[0223] Output: The new route information displayed to the user.

[0224] (Application example 1)

[0225] 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."

[0226] Current home-come-home assistance systems that use traffic information are limited to proposing optimal routes home after the last train, and are unable to reflect real-time traffic conditions or road congestion, making it difficult to return home efficiently. Furthermore, they are unable to link with autonomous vehicles, requiring users to choose their own means of transportation, resulting in low convenience. Another issue is that they are unable to provide flexible suggestions that reflect the user's individual travel history and preferences.

[0227] 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.

[0228] In this invention, the server includes a means for acquiring the user's current location, a means for registering the user's home address, nearest station, and preferences, a means for collecting real-time information on transportation, and a means for instructing the autonomous vehicle on the optimal route. This allows the user to always obtain the optimal route home that reflects real-time traffic information, and by coordinating with the autonomous vehicle, the user can return home safely and efficiently. In addition, a generative AI model is used to learn past travel history, enabling flexible route suggestions that take into account the user's preferences and patterns.

[0229] "Means for obtaining the user's current location" means a device or system that measures the user's current location using GPS or other location information technology and transmits that data to the system.

[0230] "Means for registering a user's home address, nearest station, and preferences" refers to a device or system that allows a user to enter their home address, nearest public transport station, and travel preferences (e.g., preferring to avoid taxis) into the system and store this information in a database.

[0231] "Means for collecting real-time information on transportation" refers to devices or systems that use APIs or other data collection technologies to obtain the latest operational status, delay information, and congestion information of transportation services and integrate it into the system.

[0232] The "means for calculating the optimal route home" is a device or system for calculating the most efficient and economical route home for the user based on the user's current location, registered information, and real-time traffic information.

[0233] The "means for providing calculated route information" refers to a device or system that displays the optimal route home calculated by the system on the user's terminal and presents it in a visually easy-to-understand manner.

[0234] "Means for instructing an automated vehicle on the optimal route" refers to a device or system that transmits the calculated optimal route information to the automated vehicle's control system, allowing the automated vehicle to efficiently guide the user to the destination.

[0235] "Means for transmitting user's current location information to a server" refers to a device or system for obtaining user's location information in real time and transmitting it to a server via the Internet or other network.

[0236] "Means for a server to recalculate routes based on information on delays and congestion at public transport facilities and set new routes for self-driving vehicles" refers to a device or system in which a server continuously monitors the latest information on public transport facilities, recalculates the optimal route as needed, and sends that information to self-driving vehicles to set new routes.

[0237] "Artificial intelligence for learning past travel history and understanding user preferences and patterns" refers to a device or system equipped with machine learning technology that analyzes a user's past travel data, learns the user's specific behavioral patterns and preferences, and uses that information to predict future travel.

[0238] A "means for suggesting optimal routes using generative AI models" is a device or system that uses a generative artificial intelligence model to suggest optimal routes and transportation methods that take into account a user's current situation and preferences.

[0239] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. Furthermore, this system enables collaboration with autonomous vehicles and uses a generative AI model to suggest routes.

[0240] System Configuration

[0241] Hardware and Software Configuration

[0242] The system includes the following hardware and software:

[0243] The user's smartphone or smart glasses (with GPS and data communication functions)

[0244] Server (with high-performance database and AI processing capabilities)

[0245] Traffic information API (provides real-time traffic information)

[0246] Navigation systems for autonomous vehicles

[0247] How to get the user's location

[0248] The GPS module built into the device (smartphone or smart glasses) is used to obtain the user's current location. When the user launches the app and enables GPS, the device continues to obtain location information in real time.

[0249] User information registration method

[0250] Users use the app to input their home address, nearest station, commuting pattern, and preferences. For example, a user might enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum," and the information is then saved in a database.

[0251] A means of collecting real-time transportation information

[0252] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, allowing it to constantly maintain the latest information on transportation and provide it to users.

[0253] A way to calculate the best route home

[0254] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, and also takes into account the user's preferences (e.g., wanting to reduce taxi fares) and uses a generative AI model to suggest the optimal route.

[0255] A means of providing calculated route information

[0256] The server generates a specific route home based on the calculation results and sends it to the user's device, which then displays the route proposal to the user on a map and in text format.

[0257] A means of providing optimal route guidance to autonomous vehicles

[0258] The server then sends the calculated optimal route information to the autonomous vehicle's control system, and the vehicle then guides the user safely home based on that route. For example, if a route from Shinjuku Station to Roppongi Station is specified, the autonomous vehicle will use this information to select the optimal route and efficiently transport the user.

[0259] Prompt Sentence Examples

[0260] For example, if a resident of Roppongi is currently in Shinjuku, the prompt to have the generative AI model suggest a route would be:

[0261] Nearest station: Shinjuku Station

[0262] User preference: Minimize taxi fares

[0263] Current location: Shinjuku Station South Exit

[0264] Home address: Roppongi, Minato-ku, Tokyo

[0265] Please suggest the best route for the user to take home after the last train. If necessary, please consider a combination of multiple modes of transport and walking. Please also provide approximate travel costs and travel times.

[0266] Specific examples

[0267] For example, if a user misses the last train near Shinjuku Station, the system will suggest the best route home using the following steps:

[0268] 1. The device obtains its current location using GPS and sends it to the server.

[0269] 2. The server obtains real-time traffic information using the traffic information API.

[0270] 3. The server learns the user's preferences and past travel history and calculates the optimal route using a generative AI model.

[0271] 4. The server sends the calculated route to the device and displays it to the user on a map and in text format.

[0272] 5. The server instructs the autonomous vehicle on the optimal route, and the vehicle guides the user safely home.

[0273] This system allows users to return home safely and efficiently, even after the last train. For example, it can suggest a route that allows users to walk from Shinjuku Station to a nearby self-driving vehicle and go directly to their home.

[0274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0275] Step 1:

[0276] Registering user information

[0277] Users input their home address, nearest train station, commuting patterns, and preferences into their device (smartphone or smart glasses).

[0278] The terminal encrypts the input information and transmits it to the server.

[0279] The server stores the received user information in a database.

[0280] input:

[0281] Your home address, nearest train station, commuting patterns, and preferences

[0282] Data processing and calculation:

[0283] Encrypts user-entered information

[0284] output:

[0285] Encrypted user information

[0286] Specific behavior:

[0287] Enter information through the device's user interface (UI)

[0288] Secure data transmission using HTTPS protocol

[0289] Server-side data decoding and storage

[0290] Step 2:

[0291] Get and send your current location

[0292] The user launches the app and enables GPS.

[0293] The terminal obtains its current location using a GPS module and transmits that information to the server.

[0294] input:

[0295] Location data from the GPS module

[0296] Data processing and calculation:

[0297] Obtaining current location data and format conversion (longitude and latitude)

[0298] output:

[0299] Location information sent to the server

[0300] Specific behavior:

[0301] Enable the GPS function on your device and periodically update your location.

[0302] Send location information to the server in real time

[0303] Step 3:

[0304] Real-time traffic information collection

[0305] The server obtains real-time traffic information through a traffic information provision API.

[0306] The acquired traffic information is temporarily stored in a database.

[0307] input:

[0308] Traffic information API request

[0309] Data processing and calculation:

[0310] Parsing and saving data retrieved from the API

[0311] output:

[0312] Real-time traffic information

[0313] Specific behavior:

[0314] The server periodically calls the API to retrieve data.

[0315] Analyze JSON format data and save the necessary information to a database

[0316] Step 4:

[0317] Optimal Route Calculation

[0318] The server calculates the optimal route home based on the user's current location, registration information, and real-time traffic information.

[0319] Generate route suggestions using a generative AI model.

[0320] input:

[0321] User information, current location, real-time traffic information

[0322] Data processing and calculation:

[0323] Integrates multiple input data and executes route calculation algorithms

[0324] Uses generative AI models to calculate optimal routes

[0325] output:

[0326] Proposing the best route home

[0327] Specific behavior:

[0328] Algorithms on the server analyze the data and calculate the shortest time and lowest cost route.

[0329] Generate route suggestions taking into account user preferences

[0330] Step 5:

[0331] Providing route information

[0332] The server generates a specific route home based on the calculation results and sends it to the user's device.

[0333] The device displays the received route information to the user in map and text format.

[0334] input:

[0335] Data suggesting the best route home

[0336] Data processing and calculation:

[0337] Route data format conversion (map data, text data)

[0338] output:

[0339] Route information displayed on the user's device

[0340] Specific behavior:

[0341] The server sends route proposal data to the terminal

[0342] Display data received by the device as a map and text

[0343] Step 6:

[0344] Commanding autonomous vehicles

[0345] The server sends the optimal route information to the autonomous vehicle's control system, which guides the vehicle to the user.

[0346] The autonomous vehicle will drive users to their destination based on the optimal route.

[0347] input:

[0348] Data suggesting the best route home

[0349] Data processing and calculation:

[0350] Converting route data into a format suitable for the navigation system of an autonomous vehicle

[0351] output:

[0352] Route instructions for autonomous vehicles

[0353] Specific behavior:

[0354] The server sends the route information to the vehicle's control system

[0355] The vehicle's navigation system sets the route and begins operation.

[0356] Step 7:

[0357] Real-time recalculation and notifications

[0358] The server continuously monitors traffic information and recalculates the optimal route when new information becomes available.

[0359] If a route change is necessary, the device and the autonomous vehicle will be notified.

[0360] input:

[0361] Continuously updated traffic information

[0362] Data processing and calculation:

[0363] Route recalculation based on real-time information

[0364] output:

[0365] Notification of updated route information

[0366] Specific behavior:

[0367] The server periodically calls the traffic API to obtain the latest information.

[0368] Recalculate the route and notify the device and vehicle if necessary

[0369] In this way, through a series of steps, the present invention can provide users with the optimal route home in real time and, in cooperation with autonomous vehicles, support a safe and efficient journey home.

[0370] 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.

[0371] The present invention relates to a system that helps users find the best route home after the last train. The system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and suggests the best route home. Furthermore, by combining this system with an emotion engine, the system can recognize the user's emotional state and reflect it in the suggested route home. Specific processing details and examples of the system are described below.

[0372] Registering user information

[0373] 1. User (device): Enter information

[0374] Users launch the app and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[0375] 2. Terminal: Information transmission

[0376] The device sends the entered user information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[0377] 3. Server: Information storage

[0378] The server stores the received user information in a database, which is then used to train the AI ​​model.

[0379] 4. Server: AI Learning

[0380] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[0381] Real-time traffic information collection

[0382] 5. User (Device): Enable GPS

[0383] When the user restarts the app after the last train and enables the GPS function, the app obtains the user's current location and sends that information to the server.

[0384] 6. Device: Send current location

[0385] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[0386] 7. Server: Traffic information collection

[0387] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[0388] Recognizing and recording user emotions

[0389] 8. Terminal: Emotional Data Collection

[0390] The device uses microphones, cameras, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate, and sends this data to the emotion engine.

[0391] 9. Sentiment Engine: Sentiment Analysis

[0392] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as, for example, "stressed" or "relaxed."

[0393] Optimal route suggestions

[0394] 10. Server: Optimal route calculation

[0395] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine.

[0396] 11. Server: Route proposal generation

[0397] The server then generates a specific route home based on the calculation results. For example, it might suggest "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." If the stress level during the stay is high, it would suggest a more relaxing way home.

[0398] 12. Server: Send Proposal

[0399] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0400] 13. Terminal: Route display

[0401] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0402] Real-time information updates

[0403] 14. User (device): Continuously send current location

[0404] Users keep the app open while they move around, and it continually obtains their location and sends updates to the server if there are any changes.

[0405] 15. Server: Traffic Condition Monitoring

[0406] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[0407] 16. Server: Route Recalculation

[0408] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[0409] 17. Server: Send Re-Proposal

[0410] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0411] 18. Terminal: Reroute Display

[0412] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0413] This not only enables users to find the best route home even after the last train, but also allows them to receive suggestions that take their emotional state into account, making for a less stressful journey home.

[0414] The processing flow will be explained below.

[0415] Step 1:

[0416] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[0417] Step 2:

[0418] The device sends the entered information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[0419] Step 3:

[0420] The server stores the received user information in a database, which is then used to train the AI ​​model.

[0421] Step 4:

[0422] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[0423] Step 5:

[0424] The user (device) restarts the app after the last train and enables the GPS function, which then acquires the user's current location.

[0425] Step 6:

[0426] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[0427] Step 7:

[0428] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[0429] Step 8:

[0430] The device uses a microphone, camera, sensors, etc. to obtain emotional data from the user. For example, it can assess stress levels based on the user's tone of voice and measure relaxation levels using a heart rate sensor.

[0431] Step 9:

[0432] The emotion engine analyzes the acquired emotion data and determines the user's emotional state. The result is classified as "stressed" or "relaxed" and sent to the server.

[0433] Step 10:

[0434] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. For example, if a user is feeling stressed, it will prioritize a route with less congestion.

[0435] Step 11:

[0436] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[0437] Step 12:

[0438] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0439] Step 13:

[0440] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0441] Step 14:

[0442] The user (device) keeps the app open while moving, and the app continuously acquires the user's location. If there are any changes, the information is updated and sent to the server.

[0443] Step 15:

[0444] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[0445] Step 16:

[0446] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[0447] Step 17:

[0448] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0449] Step 18:

[0450] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0451] Through the above processing steps, the system can find the optimal route home after the last train, allowing the user to have a stress-free journey home that takes into account their emotional state.

[0452] Example 2

[0453] 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."

[0454] It is difficult for users to find the optimal route home after the last train, especially when considering transportation delays, congestion, and the user's emotional state. As a result, users often spend a long time finding a route home that is less stressful. This can reduce user satisfaction and lead to further stress on the way home. Therefore, there is a need for a system that can quickly suggest the optimal route home that reflects traffic information and the user's emotional state in real time.

[0455] 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.

[0456] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation facilities, means for analyzing the user's emotional state, means for calculating an optimal route home based on the above information and the results of the emotional state analysis, and means for providing the calculated route information, thereby enabling the user to quickly find an optimal route home taking into account real-time updated traffic information and the user's emotional state.

[0457] "Means for obtaining the user's current location" refers to technologies or methods that use the GPS function or location information services of the device used by the user to identify and obtain the user's current geographical location.

[0458] "Means for registering a user's home address, nearest station, and preferences" refers to the technology or method by which a user can input their home address, nearest station, and various personal preferences (such as wanting to take a taxi) through an application or terminal and store them in a database.

[0459] "Means of collecting real-time information on transportation" refers to technologies and methods that use external transportation information services and APIs to obtain the latest information on the operation status and congestion levels of trains, buses, taxis, etc.

[0460] "Means for analyzing the user's emotional state" refers to technologies and methods that collect data such as the user's voice, facial expressions, and heart rate, and then use artificial intelligence and analytical algorithms to classify the user's emotions into "stressed state," "relaxed state," etc.

[0461] "Means for calculating the optimal route home" refers to technology or methods that use a generative AI model to generate the optimal route home based on the user's current location information, registration information, real-time traffic information, and emotion analysis results.

[0462] "Means for providing calculated route information" refers to a technology or method for sending calculated information on the optimal route home to the user's device and presenting it as a map display or text description.

[0463] "Registered user information" refers to information such as home address, nearest station, and preferences that the user enters into the application or device.

[0464] An "emotion engine" refers to an artificial intelligence or analytical algorithm system that analyzes collected emotional data to identify the user's emotional state.

[0465] "Transportation Information API" refers to an application programming interface for accessing and obtaining information from external services that provide information on transportation operation status and congestion levels.

[0466] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to learn user behavior and patterns from various data and make predictions and suggestions.

[0467] This invention provides a system that allows users to find the best route home after the last train, and in particular, reflects traffic information and the user's emotional state in real time to support a more comfortable journey home.

[0468] This system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and proposes the optimal route home. It can also analyze the user's emotional state and reflect the results in the route home. A specific embodiment of the system is described below.

[0469] Registering user information

[0470] Users launch an application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum." This information is sent from the device to the server and securely stored in a database. The server uses a generative AI model based on the stored user information to perform learning. Examples of machine learning models used here include TensorFlow and PyTorch.

[0471] Real-time traffic information collection

[0472] The user restarts the application after the last train and enables the GPS function. The device obtains the user's current location and sends it to the server in real time. The server then calls a traffic information API (e.g., Google Maps API or OpenWeatherMap API) to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis. This information is temporarily stored in a database.

[0473] Recognizing and recording user emotions

[0474] The device uses a microphone, camera, and sensors to capture data such as the user's voice, facial expressions, and heart rate. This data is sent to an emotion engine (e.g., Amazon Rekognition or IBM Watson), which analyzes the user's emotional state into categories such as "stressed" or "relaxed." The analysis results are sent to a server and used to calculate the route home.

[0475] Optimal route suggestions

[0476] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. Using a generative AI model, it generates the optimal route while taking multiple factors into account. For example, it could suggest routes such as "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." The server sends the calculation results to the device, which then presents them to the user as a map display or text explanation.

[0477] Real-time information updates

[0478] By keeping the app open while the user is traveling, the current location information is continuously sent to the server. The server periodically calls the traffic information API to obtain the latest service status and congestion information. If an unexpected delay or other problem occurs, the server recalculates the route in real time, reanalyzes the user's emotional state, and sends a new route suggestion to the device. The device promptly displays the recalculated route to the user and updates the map and text information if there are any changes.

[0479] Examples of specific examples and prompts

[0480] For example, if a user in Roppongi searches for a route home after the last train, the system will suggest options such as "walk to Shinjuku Station, take the train to Shibuya Station, or take a taxi from Shibuya Station to Roppongi Station." It will suggest the optimal way to get home by taking into account traffic conditions, congestion information, and the user's emotional state.

[0481] Prompt Sentence Examples

[0482] 1. "If my current location is Roppongi, the nearest station is Roppongi Station, and my home is Shibuya, what is the best route home after the last train?"

[0483] 2. "If the user is currently in a stressful situation, please suggest a relaxing way to get home."

[0484] 3. "Calculate the best route home with real-time traffic updates."

[0485] This allows users to quickly find the best route home based on traffic information and their emotional state.

[0486] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] User: Enter information

[0489] Users launch the application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[0490] Input: Home address, nearest station, commuting pattern, preferences

[0491] Output: User information data

[0492] Step 2:

[0493] Terminal: Sending information

[0494] The device encrypts the information entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0495] Input: User information data

[0496] Output: User information sent to the server

[0497] Step 3:

[0498] Server: Information storage

[0499] The server stores the received user information in a database, which is then used to train and predict generative AI models.

[0500] Input: User information data

[0501] Output: User information stored in the database

[0502] Step 4:

[0503] Server: AI learning

[0504] The server uses the stored user information to train a generative AI model, which then makes predictions tailored to each individual user.

[0505] Input: User information stored in the database

[0506] Output: Trained generative AI model

[0507] Step 5:

[0508] User:GPS Enabled

[0509] The user restarts the application after the last train and enables the GPS function, which allows the app to automatically obtain the user's current location.

[0510] Input: None

[0511] Output: User's current location

[0512] Step 6:

[0513] Device: Send current location

[0514] The device transmits the acquired location information to the server in real time, where it is re-encrypted and transmitted securely.

[0515] Input: User's current location

[0516] Output: Location information sent to the server

[0517] Step 7:

[0518] Server: Traffic information collection

[0519] The server calls the traffic information API to collect real-time operational status and congestion information for trains, buses, taxis, etc. This information is temporarily stored in a database.

[0520] Input: Current location

[0521] Output: Traffic information stored in a database

[0522] Step 8:

[0523] Device: Emotion data collection

[0524] The device uses microphones, cameras, and sensors to capture data such as the user's voice, facial expressions, and heart rate, which are then sent to the emotion engine.

[0525] Input: Voice data, facial expression data, heart rate data

[0526] Output: Emotion data

[0527] Step 9:

[0528] Sentiment Engine: Sentiment Analysis

[0529] The emotion engine analyzes the collected data, classifies the user's emotional state as "stressed" or "relaxed," etc., and sends the results to the server.

[0530] Input: Emotion data

[0531] Output: Emotion analysis results

[0532] Step 10:

[0533] Server: Optimal route calculation

[0534] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. It uses a generative AI model to predict the optimal route.

[0535] Input: Current location information, user information, traffic information, sentiment analysis results

[0536] Output: Optimal route data

[0537] Step 11:

[0538] Server: Route proposal generation

[0539] The server generates specific route suggestions based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[0540] Input: Optimal route data

[0541] Output: Route suggestion data

[0542] Step 12:

[0543] Server: Send proposal

[0544] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0545] Input: Route suggestion data

[0546] Output: Route suggestions sent to the device

[0547] Step 13:

[0548] Terminal: Route display

[0549] The device then displays the route suggestions to the user, either as a map view or as a text description, which the user can review.

[0550] Input: Route suggestion data

[0551] Output: Route display to user

[0552] Step 14:

[0553] User:Continuously send current location

[0554] Users keep the app open while they move around, continuously sending their location information to the server.

[0555] Input: Current location

[0556] Output: Location information sent to the server

[0557] Step 15:

[0558] Server: Traffic information monitoring

[0559] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the database as necessary.

[0560] Input: Data from traffic information API

[0561] Output: Updated traffic information

[0562] Step 16:

[0563] Server: Route recalculation

[0564] The server responds to unexpected delays and troubles and recalculates the route in real time based on traffic information and emotional state.

[0565] Input: updated traffic information, sentiment analysis results

[0566] Output: Recalculated route data

[0567] Step 17:

[0568] Server: Send a second proposal

[0569] The server sends the new recalculated route proposal to the device, including any changes to the mode of transportation if necessary.

[0570] Input: Recalculated route data

[0571] Output: New route proposal sent to the device

[0572] Step 18:

[0573] Terminal: Reroute display

[0574] The device promptly displays recalculated route suggestions to the user, updates the map and text information, and notifies the user.

[0575] Input: New route proposal data

[0576] Output: Updated route displayed to user

[0577] (Application example 2)

[0578] 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."

[0579] Conventional home route suggestion systems suggest the optimal route based on the user's current location and transportation information, but because they do not take the user's emotional state into consideration, they are unable to reduce the user's stress and frustration.In addition, their ability to collect traffic information and recalculate routes in real time is limited, making them unable to respond to unexpected delays and congestion.

[0580] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation, means for analyzing the acquired emotional state of the user, means for calculating the optimal route home based on the above information and analysis results, and means for providing the calculated route information. This makes it possible to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[0581] "Means for obtaining the user's current location" refers to the means for obtaining location information about the user's current location, and typically involves using a GPS module or location information API.

[0582] "Means for registering the user's home address, nearest station, and preferences" refers to a means for inputting and saving information about the user's home location, nearest station, and preferred means and route for getting home.

[0583] "Means for collecting real-time information on transportation" refers to means for collecting information on the operation status and congestion status of transportation such as trains, buses, and taxis in real time.

[0584] The "means for analyzing the acquired emotional state of the user" refers to a means for acquiring data such as the user's facial expressions, voice, and heart rate, and analyzing that emotional state.

[0585] The "means for calculating the optimal route home" is a means for calculating a route that enables the user to safely return home via the optimal route based on information such as the user's current location, traffic conditions, the user's preferences, and emotional state.

[0586] The "means for providing calculated route information" refers to a means for providing the user with information on the calculated route home, which is usually done through a smartphone application.

[0587] "Means for the server to recalculate the route based on information on delays and congestion of public transport and notify the user" refers to a means for the server to detect changes in information on delays and congestion of public transport in real time, recalculate the route based on that information, and notify the user of the results.

[0588] The "means for recalculating a route according to the acquired emotional state" is a means for recalculating a route based on the emotional state of the user for the purpose of reducing stress, etc.

[0589] "Artificial intelligence that learns from a user's past movement history and understands their preferences and patterns" refers to artificial intelligence that uses machine learning algorithms to analyze past movement history data and learn and understand the user's preferences and patterns.

[0590] A specific embodiment for carrying out the present invention will be described below. The following system is used to propose an optimal route home based on the user's current location and emotional state.

[0591] First, the user registers basic information such as their home address, nearest station, and preferences through a smartphone application. The system then obtains the user's current location using a GPS module and Google Maps API. Real-time transportation information is collected using the Google Transit API and OpenWeatherMap API. Furthermore, the system uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze the user's emotional state based on data such as facial images, voice, and heart rate.

[0592] The server aggregates this data and uses machine learning algorithms to learn about the user's past travel history and emotional state. Based on this information, it calculates the optimal route for the user to return home comfortably. The calculated route information is provided to the user via a smartphone application.

[0593] For example, assume a user is in Roppongi, and their home is in Roppongi, Minato Ward, Tokyo. If the user is returning home at night and experiencing high stress, the system will suggest the fastest and least stressful route. Specifically, it might suggest walking to Shinjuku Station, then taking a train to Shibuya Station, and finally taking a taxi from Shibuya Station to Roppongi Station. The route is dynamically recalculated based on real-time updates to traffic information and the user's emotional state.

[0594] Examples of prompt sentences include the following:

[0595] "Consider the current traffic situation and suggest the best way home based on future stress levels. The current location is Roppongi, and the destination is Roppongi, Minato-ku, Tokyo. Sentiment analysis indicates the user is in a high stress state. Given this, what is the best way home?"

[0596] As a result, this system is able to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[0597] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0598] Step 1:

[0599] The user launches the smartphone application and inputs their home address, nearest station, commuting pattern, and preferences (input: user information). The device then sends the input user information to the server (output: upload of user information data).

[0600] Step 2:

[0601] The server stores the received user information in a database. This data is used for training the AI ​​model later (input: user information, output: user information stored in the database).

[0602] Step 3:

[0603] The server uses machine learning to learn the user's commuting patterns and preferences based on the stored user information, enabling it to make predictions that are optimized for each individual user (input: user information, output: learning model).

[0604] Step 4:

[0605] When the user restarts the app after the last train and enables the GPS function, the device acquires the user's current location and sends that information to the server (input: GPS location information, output: upload of current location data).

[0606] Step 5:

[0607] The server uses a traffic information API to collect real-time operating status and congestion information for nearby trains, buses, and taxis. This information is temporarily stored in a database (input: current location data, traffic information API, output: real-time traffic data).

[0608] Step 6:

[0609] The device uses a microphone, camera, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate. This data is sent to the emotion engine (input: voice, facial expressions, heart rate data; output: emotion data).

[0610] Step 7:

[0611] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as "stressed state" or "relaxed state" (input: emotion data, output: emotion analysis results).

[0612] Step 8:

[0613] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine (input: current location information, user information, traffic information, emotion analysis results, output: optimal route).

[0614] Step 9:

[0615] The server generates a specific route home based on the calculation results and sends it to the terminal (input: optimal route, output: route proposal data).

[0616] Step 10:

[0617] The terminal displays the optimal route proposal to the user, visually displaying the proposed route on a map and providing detailed textual explanations (input: route proposal data, output: route display).

[0618] Step 11:

[0619] The user keeps the app open while moving, and the app continuously obtains their location. If there are any changes, the app updates the location and sends it to the server (Input: updated location data, Output: continuous location data upload).

[0620] Step 12:

[0621] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is updated when necessary (input: traffic information API, output: continuous traffic information).

[0622] Step 13:

[0623] The server recalculates the route in real time to accommodate unexpected delays and other issues, taking into account the user's emotional state (input: updated traffic information, emotion analysis results, output: recalculated route).

[0624] Step 14:

[0625] The server sends the recalculated new route proposal to the terminal (input: recalculated route, output: reroute proposal data).

[0626] Step 15:

[0627] The device promptly displays new route suggestions to the user, and if there are any changes, updates the map and text information and notifies the user (input: reroute suggestion data, output: reroute display).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] [Second embodiment]

[0632] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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."

[0644] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. The specific processing contents of the system and examples are explained below.

[0645] Registering user information

[0646] 1. User (device): Enter information

[0647] Users launch the app and register their home address, nearest station, commuting pattern, and preferences. For example, a user enters information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[0648] 2. Terminal: Information transmission

[0649] The device sends the entered user information to the server, confirms that a connection with the server has been established, and encrypts and transmits the data.

[0650] 3. Server: Information storage

[0651] The server stores the received user information in a database, which the AI ​​model can later use to learn the user's individual needs.

[0652] 4. Server: AI Learning

[0653] The server trains the AI ​​model based on the stored user information, building a personalized predictive model based on the user's commuting patterns and preferences.

[0654] Real-time traffic information collection

[0655] 5. User (Device): Enable GPS

[0656] The user launches the app after the last train and enables GPS, which then obtains the user's current location and sends that information to the server.

[0657] 6. Device: Send current location

[0658] The device sends the user's current location information to the server in real time and continuously transmits GPS data.

[0659] 7. Server: Traffic information collection

[0660] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, temporarily stores this information, and updates it as needed.

[0661] Optimal route suggestions

[0662] 8. Server: Optimal route calculation

[0663] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[0664] 9. Server: Route proposal generation

[0665] The server then generates a specific route home based on the calculation results, suggesting, for example, "walk to Shinjuku Station, take the train to Shibuya Station, and then take a taxi from Shibuya Station to Roppongi Station."

[0666] 10. Server: Send Proposal

[0667] The server then sends the generated route proposal to the device, including route details and a total cost estimate.

[0668] 11. Terminal: Route display

[0669] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0670] Real-time information updates

[0671] 12. User (device): Continuously send current location

[0672] Users keep the app open while they move around, and the app continuously obtains their location and sends updates to the server if there are any changes.

[0673] 13. Server: Traffic Condition Monitoring

[0674] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[0675] 14. Server: Route Recalculation

[0676] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[0677] 15. Server: Send Re-Proposal

[0678] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0679] 16. Terminal: Reroute Display

[0680] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0681] This allows users to find the best route home even after the last train, allowing them to get home quickly while keeping travel costs down.

[0682] The processing flow will be explained below.

[0683] Step 1:

[0684] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences using a form, and then presses the "Submit" button once the information is complete.

[0685] Step 2:

[0686] The terminal sends the entered information to the server. At this time, the data is encrypted and the information is sent using a secure communication protocol. The terminal then checks the connection with the server.

[0687] Step 3:

[0688] The server stores the received user information in a database, which is then used to train the AI ​​model.

[0689] Step 4:

[0690] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[0691] Step 5:

[0692] The user (device) restarts the app after the last train and enables the GPS function, and the app obtains the user's current location.

[0693] Step 6:

[0694] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[0695] Step 7:

[0696] The server uses a traffic information API to collect real-time information on train, bus, and taxi service status and congestion information around the user's current location. The acquired information is temporarily stored in a database.

[0697] Step 8:

[0698] The server calculates the optimal route home based on the user's current location, registered user information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[0699] Step 9:

[0700] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[0701] Step 10:

[0702] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0703] Step 11:

[0704] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0705] Step 12:

[0706] The user (device) keeps the app open while moving, and the current location is continuously acquired. If there is a change, the current location information is updated and sent to the server.

[0707] Step 13:

[0708] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[0709] Step 14:

[0710] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[0711] Step 15:

[0712] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[0713] Step 16:

[0714] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[0715] Example 1

[0716] 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."

[0717] Currently, many users face difficulties in finding a way to get home after the last train. This is especially true at night, when transportation options are limited or when transportation is affected by delays or congestion. Furthermore, because proposed routes do not reflect the user's individual preferences or commuting patterns, this can be time-consuming and costly. This invention is proposed to solve these problems.

[0718] 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.

[0719] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, commuting pattern, and preferences, means for transmitting encrypted user information to the server, means for collecting real-time information on transportation facilities, means for calculating an optimal route home based on the user information and real-time traffic information, means for providing the calculated route information, and means for updating the traffic information in real time and recalculating the route as necessary. This allows the user to receive a real-time suggestion of an optimal route home according to traffic conditions, enabling them to return home quickly and efficiently.

[0720] "Means for obtaining the user's current location" refers to a means for accurately identifying the user's current location using location information technology such as GPS or Wi-Fi.

[0721] "Means for registering a user's home address, nearest station, commuting patterns, and preferences" means an interface or input method that allows a user to register their home address, nearest station, daily commuting patterns, and transportation preferences in the system.

[0722] "Means for sending encrypted user information to a server" refers to a technology that encrypts data to securely protect information entered by users and sends it to a server via the Internet.

[0723] "Means of collecting real-time information on transportation" refers to technology that uses transportation information services and APIs to obtain real-time information on the operation status and congestion of trains, buses, taxis, etc.

[0724] "Means for calculating the optimal route home based on user information and real-time traffic information" refers to an algorithm or calculation method that calculates the optimal route home, taking into account the information registered by the user and the collected current traffic conditions.

[0725] The "means for providing calculated route information" refers to an interface or output method for providing the calculated route home in a form that is easy for the user to understand.

[0726] "Means for updating traffic information in real time and recalculating routes as needed" refers to methods or algorithms that continuously update information in response to changing traffic conditions and recalculate a new route home as needed.

[0727] The present invention provides a system for assisting a user in finding the best route home after the last train. Specific embodiments of the system will be described below.

[0728] Overview and Structure

[0729] This system uses the user's current location, home address, nearest station, commuting pattern, and preference information to collect real-time traffic information and calculate and suggest the optimal route home. The main components of the system are the user's device, server, database, and traffic information provision API.

[0730] Hardware and software used

[0731] Device: The smartphone or tablet used by the user

[0732] Server: Cloud-based server (e.g., AWS, Microsoft Azure, Google Cloud)

[0733] Database: SQL or NoSQL database (e.g. MySQL, PostgreSQL, MongoDB)

[0734] Traffic information API: Real-time traffic information providers such as Google Maps API and OpenWeatherMap

[0735] Encryption technology: AES encryption

[0736] Data processing and calculation

[0737] 1. Enter and submit user information

[0738] Users launch a dedicated app on their smartphone and enter their home address, nearest train station, commuting pattern, and individual preferences.

[0739] The data entered is encrypted within the app and securely sent to the server.

[0740] 2. Server-side information processing

[0741] The server decrypts the received encrypted data and stores it in a database.

[0742] Using the stored data, a generative AI model learns users' commuting patterns and preferences, built using TensorFlow and PyTorch.

[0743] 3. Collection of real-time traffic information

[0744] When the user launches the app again and sends their current location information, the server calls the traffic information API to obtain real-time traffic conditions.

[0745] The retrieved information is temporarily stored in the Redis cache.

[0746] 4. Calculating the optimal route

[0747] The server calculates the optimal route home using Dijkstra's algorithm and A algorithm based on the user's current location, registered information, and collected real-time traffic information.

[0748] 5. Route presentation

[0749] The calculated route information is sent from the server to the terminal where it is presented to the user in visual and textual form.

[0750] This route information includes the mode of transportation (walking, train, taxi, etc.), travel time, cost estimates, etc.

[0751] 6. Real-time updates

[0752] The device continuously transmits its current location information, and the server monitors changes in traffic information.

[0753] If traffic conditions change, the server recalculates the optimal route and notifies the user of the new route.

[0754] Examples of specific examples and prompts

[0755] Examples:

[0756] User A lives in Roppongi, Minato-ku, Tokyo, and commutes from 9:00 to 18:00. He is heading home from Shinjuku Station after the last train has left. User A launches the app, enables GPS, and sends his current location to the server. The server uses the Google Maps API to obtain real-time traffic information and calculates the optimal route home, taking into account User A's commuting pattern and preferences. As a result, the server suggests the route: "Take the train from Shinjuku Station to Shibuya Station, then take a taxi from Shibuya Station to Roppongi." This route information is displayed on the app as a map and text.

[0757] Example prompt sentence:

[0758] "If my current location is Shinjuku Station and my home is Roppongi, Minato Ward, Tokyo, please suggest the best route to get home while keeping taxi fare to a minimum."

[0759] This allows users to efficiently obtain the optimal route home in real time.

[0760] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0761] Step 1:

[0762] Input: The user enters their home address, nearest train station, commuting patterns, and preferences into the app.

[0763] How it works: The user launches the app on their smartphone and fills in the designated form with their home address (e.g., Roppongi, Minato-ku, Tokyo), nearest station (e.g., Roppongi Station), commuting pattern (e.g., 9:00 AM to 6:00 PM), and preferences (e.g., want to keep taxi fares to a minimum).

[0764] Output: User information entered on the terminal is generated.

[0765] Step 2:

[0766] Input: Home address, nearest station, commuting patterns, and preference data entered by the user.

[0767] Operation: The device encrypts the collected user information and sends it to the server using AES encryption, for example, AES-256.

[0768] Output: User information data sent to the server in encrypted form.

[0769] Step 3:

[0770] Input: Encrypted user information data.

[0771] How it works: The server decrypts the encrypted data it receives and stores it in a database. The data is decrypted using the corresponding encryption key.

[0772] Output: Decrypted user information stored in the database.

[0773] Step 4:

[0774] Input: Decrypted user information.

[0775] How it works: The server uses this user information to train a generative AI model, which is built using TensorFlow or PyTorch to learn individual patterns for each user.

[0776] Output: A trained generative AI model.

[0777] Step 5:

[0778] Input: trained generative AI model, user's current location.

[0779] How it works: The user restarts the app after the last train and enables GPS, causing the device to collect the user's location.

[0780] Output: Current location information is obtained on the device.

[0781] Step 6:

[0782] Input: User's current location.

[0783] What it does: The device starts streaming continuous GPS data to send real-time location information to the server. The data is sent using WebSockets.

[0784] Output: Real-time location data sent to the server.

[0785] Step 7:

[0786] Input: User location, real-time transport data.

[0787] How it works: The server uses a traffic information API (e.g., Google Maps API) to collect real-time information about train, bus, and taxi service availability and congestion information for the user's area. This information is stored in a Redis cache.

[0788] Output: Temporarily stored real-time traffic information.

[0789] Step 8:

[0790] Input: User's current location information, registered information, real-time traffic information, trained generative AI model.

[0791] How it works: The server uses Dijkstra's algorithm and the A algorithm to calculate the optimal route home based on this information, taking into account the user's preferences.

[0792] Output: The calculated optimal route home.

[0793] Step 9:

[0794] Input: The calculated optimal route home.

[0795] How it works: The server packages this route information in JSON format and sends it to the device.

[0796] Output: Optimal route home information sent from the server to the terminal.

[0797] Step 10:

[0798] Input: The best route home information submitted.

[0799] How it works: The device analyzes the route information it receives and displays it visually and textually on the user interface, and plots the optimal route on a map.

[0800] Output: Route information displayed to the user.

[0801] Step 11:

[0802] Input: The user's continuous location.

[0803] How it works: The user keeps the app open on their device while they move around, and the device continuously sends their location to the server.

[0804] Output: The latest location information sent to the server.

[0805] Step 12:

[0806] Input: Latest traffic information, latest user location information.

[0807] Operation: The server periodically checks the traffic information API to obtain and update the latest traffic status and congestion information.

[0808] Output: Updated latest traffic information.

[0809] Step 13:

[0810] Input: Updated traffic information, user's current location, trained generative AI model.

[0811] What it does: The server recalculates the optimal route based on this information and suggests a new route home if necessary.

[0812] Output: The new recalculated optimal route.

[0813] Step 14:

[0814] Input: The new recalculated optimal route.

[0815] Operation: The server sends the recalculated route in JSON format to the device and notifies the user.

[0816] Output: The new best route information sent to the device.

[0817] Step 15:

[0818] Input: New optimal route information.

[0819] What it does: The device parses the new route information and updates the user interface, notifying the user of any changes.

[0820] Output: The new route information displayed to the user.

[0821] (Application example 1)

[0822] 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."

[0823] Current home-come-home assistance systems that use traffic information are limited to proposing optimal routes home after the last train, and are unable to reflect real-time traffic conditions or road congestion, making it difficult to return home efficiently. Furthermore, they are unable to link with autonomous vehicles, requiring users to choose their own means of transportation, resulting in low convenience. Another issue is that they are unable to provide flexible suggestions that reflect the user's individual travel history and preferences.

[0824] 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.

[0825] In this invention, the server includes a means for acquiring the user's current location, a means for registering the user's home address, nearest station, and preferences, a means for collecting real-time information on transportation, and a means for instructing the autonomous vehicle on the optimal route. This allows the user to always obtain the optimal route home that reflects real-time traffic information, and by coordinating with the autonomous vehicle, the user can return home safely and efficiently. In addition, a generative AI model is used to learn past travel history, enabling flexible route suggestions that take into account the user's preferences and patterns.

[0826] "Means for obtaining the user's current location" means a device or system that measures the user's current location using GPS or other location information technology and transmits that data to the system.

[0827] "Means for registering a user's home address, nearest station, and preferences" refers to a device or system that allows a user to enter their home address, nearest public transport station, and travel preferences (e.g., preferring to avoid taxis) into the system and store this information in a database.

[0828] "Means for collecting real-time information on transportation" refers to devices or systems that use APIs or other data collection technologies to obtain the latest operational status, delay information, and congestion information of transportation services and integrate it into the system.

[0829] The "means for calculating the optimal route home" is a device or system for calculating the most efficient and economical route home for the user based on the user's current location, registered information, and real-time traffic information.

[0830] The "means for providing calculated route information" refers to a device or system that displays the optimal route home calculated by the system on the user's terminal and presents it in a visually easy-to-understand manner.

[0831] "Means for instructing an automated vehicle on the optimal route" refers to a device or system that transmits the calculated optimal route information to the automated vehicle's control system, allowing the automated vehicle to efficiently guide the user to the destination.

[0832] "Means for transmitting user's current location information to a server" refers to a device or system for obtaining user's location information in real time and transmitting it to a server via the Internet or other network.

[0833] "Means for a server to recalculate routes based on information on delays and congestion at public transport facilities and set new routes for self-driving vehicles" refers to a device or system in which a server continuously monitors the latest information on public transport facilities, recalculates the optimal route as needed, and sends that information to self-driving vehicles to set new routes.

[0834] "Artificial intelligence for learning past travel history and understanding user preferences and patterns" refers to a device or system equipped with machine learning technology that analyzes a user's past travel data, learns the user's specific behavioral patterns and preferences, and uses that information to predict future travel.

[0835] A "means for suggesting optimal routes using generative AI models" is a device or system that uses a generative artificial intelligence model to suggest optimal routes and transportation methods that take into account a user's current situation and preferences.

[0836] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. Furthermore, this system enables collaboration with autonomous vehicles and uses a generative AI model to suggest routes.

[0837] System Configuration

[0838] Hardware and Software Configuration

[0839] The system includes the following hardware and software:

[0840] The user's smartphone or smart glasses (with GPS and data communication functions)

[0841] Server (with high-performance database and AI processing capabilities)

[0842] Traffic information API (provides real-time traffic information)

[0843] Navigation systems for autonomous vehicles

[0844] How to get the user's location

[0845] The GPS module built into the device (smartphone or smart glasses) is used to obtain the user's current location. When the user launches the app and enables GPS, the device continues to obtain location information in real time.

[0846] User information registration method

[0847] Users use the app to input their home address, nearest station, commuting pattern, and preferences. For example, a user might enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum," and the information is then saved in a database.

[0848] A means of collecting real-time transportation information

[0849] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, allowing it to constantly maintain the latest information on transportation and provide it to users.

[0850] A way to calculate the best route home

[0851] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, and also takes into account the user's preferences (e.g., wanting to reduce taxi fares) and uses a generative AI model to suggest the optimal route.

[0852] A means of providing calculated route information

[0853] The server generates a specific route home based on the calculation results and sends it to the user's device, which then displays the route proposal to the user on a map and in text format.

[0854] A means of providing optimal route guidance to autonomous vehicles

[0855] The server then sends the calculated optimal route information to the autonomous vehicle's control system, and the vehicle then guides the user safely home based on that route. For example, if a route from Shinjuku Station to Roppongi Station is specified, the autonomous vehicle will use this information to select the optimal route and efficiently transport the user.

[0856] Prompt Sentence Examples

[0857] For example, if a resident of Roppongi is currently in Shinjuku, the prompt to have the generative AI model suggest a route would be:

[0858] Nearest station: Shinjuku Station

[0859] User preference: Minimize taxi fares

[0860] Current location: Shinjuku Station South Exit

[0861] Home address: Roppongi, Minato-ku, Tokyo

[0862] Please suggest the best route for the user to take home after the last train. If necessary, please consider a combination of multiple modes of transport and walking. Please also provide approximate travel costs and travel times.

[0863] Specific examples

[0864] For example, if a user misses the last train near Shinjuku Station, the system will suggest the best route home using the following steps:

[0865] 1. The device obtains its current location using GPS and sends it to the server.

[0866] 2. The server obtains real-time traffic information using the traffic information API.

[0867] 3. The server learns the user's preferences and past travel history and calculates the optimal route using a generative AI model.

[0868] 4. The server sends the calculated route to the device and displays it to the user on a map and in text format.

[0869] 5. The server instructs the autonomous vehicle on the optimal route, and the vehicle guides the user safely home.

[0870] This system allows users to return home safely and efficiently, even after the last train. For example, it can suggest a route that allows users to walk from Shinjuku Station to a nearby self-driving vehicle and go directly to their home.

[0871] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0872] Step 1:

[0873] Registering user information

[0874] Users input their home address, nearest train station, commuting patterns, and preferences into their device (smartphone or smart glasses).

[0875] The terminal encrypts the input information and transmits it to the server.

[0876] The server stores the received user information in a database.

[0877] input:

[0878] Your home address, nearest train station, commuting patterns, and preferences

[0879] Data processing and calculation:

[0880] Encrypts user-entered information

[0881] output:

[0882] Encrypted user information

[0883] Specific behavior:

[0884] Enter information through the device's user interface (UI)

[0885] Secure data transmission using HTTPS protocol

[0886] Server-side data decoding and storage

[0887] Step 2:

[0888] Get and send your current location

[0889] The user launches the app and enables GPS.

[0890] The terminal obtains its current location using a GPS module and transmits that information to the server.

[0891] input:

[0892] Location data from the GPS module

[0893] Data processing and calculation:

[0894] Obtaining current location data and format conversion (longitude and latitude)

[0895] output:

[0896] Location information sent to the server

[0897] Specific behavior:

[0898] Enable the GPS function on your device and periodically update your location.

[0899] Send location information to the server in real time

[0900] Step 3:

[0901] Real-time traffic information collection

[0902] The server obtains real-time traffic information through a traffic information provision API.

[0903] The acquired traffic information is temporarily stored in a database.

[0904] input:

[0905] Traffic information API request

[0906] Data processing and calculation:

[0907] Parsing and saving data retrieved from the API

[0908] output:

[0909] Real-time traffic information

[0910] Specific behavior:

[0911] The server periodically calls the API to retrieve data.

[0912] Analyze JSON format data and save the necessary information to a database

[0913] Step 4:

[0914] Optimal Route Calculation

[0915] The server calculates the optimal route home based on the user's current location, registration information, and real-time traffic information.

[0916] Generate route suggestions using a generative AI model.

[0917] input:

[0918] User information, current location, real-time traffic information

[0919] Data processing and calculation:

[0920] Integrates multiple input data and executes route calculation algorithms

[0921] Uses generative AI models to calculate optimal routes

[0922] output:

[0923] Proposing the best route home

[0924] Specific behavior:

[0925] Algorithms on the server analyze the data and calculate the shortest time and lowest cost route.

[0926] Generate route suggestions taking into account user preferences

[0927] Step 5:

[0928] Providing route information

[0929] The server generates a specific route home based on the calculation results and sends it to the user's device.

[0930] The device displays the received route information to the user in map and text format.

[0931] input:

[0932] Data suggesting the best route home

[0933] Data processing and calculation:

[0934] Route data format conversion (map data, text data)

[0935] output:

[0936] Route information displayed on the user's device

[0937] Specific behavior:

[0938] The server sends route proposal data to the terminal

[0939] Display data received by the device as a map and text

[0940] Step 6:

[0941] Commanding autonomous vehicles

[0942] The server sends the optimal route information to the autonomous vehicle's control system, which guides the vehicle to the user.

[0943] The autonomous vehicle will drive users to their destination based on the optimal route.

[0944] input:

[0945] Data suggesting the best route home

[0946] Data processing and calculation:

[0947] Converting route data into a format suitable for the navigation system of an autonomous vehicle

[0948] output:

[0949] Route instructions for autonomous vehicles

[0950] Specific behavior:

[0951] The server sends the route information to the vehicle's control system

[0952] The vehicle's navigation system sets the route and begins operation.

[0953] Step 7:

[0954] Real-time recalculation and notifications

[0955] The server continuously monitors traffic information and recalculates the optimal route when new information becomes available.

[0956] If a route change is necessary, the device and the autonomous vehicle will be notified.

[0957] input:

[0958] Continuously updated traffic information

[0959] Data processing and calculation:

[0960] Route recalculation based on real-time information

[0961] output:

[0962] Notification of updated route information

[0963] Specific behavior:

[0964] The server periodically calls the traffic API to obtain the latest information.

[0965] Recalculate the route and notify the device and vehicle if necessary

[0966] In this way, through a series of steps, the present invention can provide users with the optimal route home in real time and, in cooperation with autonomous vehicles, support a safe and efficient journey home.

[0967] 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.

[0968] The present invention relates to a system that helps users find the best route home after the last train. The system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and suggests the best route home. Furthermore, by combining this system with an emotion engine, the system can recognize the user's emotional state and reflect it in the suggested route home. Specific processing details and examples of the system are described below.

[0969] Registering user information

[0970] 1. User (device): Enter information

[0971] Users launch the app and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[0972] 2. Terminal: Information transmission

[0973] The device sends the entered user information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[0974] 3. Server: Information storage

[0975] The server stores the received user information in a database, which is then used to train the AI ​​model.

[0976] 4. Server: AI Learning

[0977] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[0978] Real-time traffic information collection

[0979] 5. User (Device): Enable GPS

[0980] When the user restarts the app after the last train and enables the GPS function, the app obtains the user's current location and sends that information to the server.

[0981] 6. Device: Send current location

[0982] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[0983] 7. Server: Traffic information collection

[0984] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[0985] Recognizing and recording user emotions

[0986] 8. Terminal: Emotional Data Collection

[0987] The device uses microphones, cameras, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate, and sends this data to the emotion engine.

[0988] 9. Sentiment Engine: Sentiment Analysis

[0989] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as, for example, "stressed" or "relaxed."

[0990] Optimal route suggestions

[0991] 10. Server: Optimal route calculation

[0992] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine.

[0993] 11. Server: Route proposal generation

[0994] The server then generates a specific route home based on the calculation results. For example, it might suggest "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." If the stress level during the stay is high, it would suggest a more relaxing way home.

[0995] 12. Server: Send Proposal

[0996] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[0997] 13. Terminal: Route display

[0998] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[0999] Real-time information updates

[1000] 14. User (device): Continuously send current location

[1001] Users keep the app open while they move around, and it continually obtains their location and sends updates to the server if there are any changes.

[1002] 15. Server: Traffic Condition Monitoring

[1003] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[1004] 16. Server: Route Recalculation

[1005] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[1006] 17. Server: Send Re-Proposal

[1007] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1008] 18. Terminal: Reroute Display

[1009] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1010] This not only enables users to find the best route home even after the last train, but also allows them to receive suggestions that take their emotional state into account, making for a less stressful journey home.

[1011] The processing flow will be explained below.

[1012] Step 1:

[1013] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[1014] Step 2:

[1015] The device sends the entered information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[1016] Step 3:

[1017] The server stores the received user information in a database, which is then used to train the AI ​​model.

[1018] Step 4:

[1019] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[1020] Step 5:

[1021] The user (device) restarts the app after the last train and enables the GPS function, which then acquires the user's current location.

[1022] Step 6:

[1023] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[1024] Step 7:

[1025] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[1026] Step 8:

[1027] The device uses a microphone, camera, sensors, etc. to obtain emotional data from the user. For example, it can assess stress levels based on the user's tone of voice and measure relaxation levels using a heart rate sensor.

[1028] Step 9:

[1029] The emotion engine analyzes the acquired emotion data and determines the user's emotional state. The result is classified as "stressed" or "relaxed" and sent to the server.

[1030] Step 10:

[1031] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. For example, if a user is feeling stressed, it will prioritize a route with less congestion.

[1032] Step 11:

[1033] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1034] Step 12:

[1035] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1036] Step 13:

[1037] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1038] Step 14:

[1039] The user (device) keeps the app open while moving, and the app continuously acquires the user's location. If there are any changes, the information is updated and sent to the server.

[1040] Step 15:

[1041] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[1042] Step 16:

[1043] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[1044] Step 17:

[1045] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1046] Step 18:

[1047] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1048] Through the above processing steps, the system can find the optimal route home after the last train, allowing the user to have a stress-free journey home that takes into account their emotional state.

[1049] Example 2

[1050] 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."

[1051] It is difficult for users to find the optimal route home after the last train, especially when considering transportation delays, congestion, and the user's emotional state. As a result, users often spend a long time finding a route home that is less stressful. This can reduce user satisfaction and lead to further stress on the way home. Therefore, there is a need for a system that can quickly suggest the optimal route home that reflects traffic information and the user's emotional state in real time.

[1052] 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.

[1053] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation facilities, means for analyzing the user's emotional state, means for calculating an optimal route home based on the above information and the results of the emotional state analysis, and means for providing the calculated route information, thereby enabling the user to quickly find an optimal route home taking into account real-time updated traffic information and the user's emotional state.

[1054] "Means for obtaining the user's current location" refers to technologies or methods that use the GPS function or location information services of the device used by the user to identify and obtain the user's current geographical location.

[1055] "Means for registering a user's home address, nearest station, and preferences" refers to the technology or method by which a user can input their home address, nearest station, and various personal preferences (such as wanting to take a taxi) through an application or terminal and store them in a database.

[1056] "Means of collecting real-time information on transportation" refers to technologies and methods that use external transportation information services and APIs to obtain the latest information on the operation status and congestion levels of trains, buses, taxis, etc.

[1057] "Means for analyzing the user's emotional state" refers to technologies and methods that collect data such as the user's voice, facial expressions, and heart rate, and then use artificial intelligence and analytical algorithms to classify the user's emotions into "stressed state," "relaxed state," etc.

[1058] "Means for calculating the optimal route home" refers to technology or methods that use a generative AI model to generate the optimal route home based on the user's current location information, registration information, real-time traffic information, and emotion analysis results.

[1059] "Means for providing calculated route information" refers to a technology or method for sending calculated information on the optimal route home to the user's device and presenting it as a map display or text description.

[1060] "Registered user information" refers to information such as home address, nearest station, and preferences that the user enters into the application or device.

[1061] An "emotion engine" refers to an artificial intelligence or analytical algorithm system that analyzes collected emotional data to identify the user's emotional state.

[1062] "Transportation Information API" refers to an application programming interface for accessing and obtaining information from external services that provide information on transportation operation status and congestion levels.

[1063] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to learn user behavior and patterns from various data and make predictions and suggestions.

[1064] This invention provides a system that allows users to find the best route home after the last train, and in particular, reflects traffic information and the user's emotional state in real time to support a more comfortable journey home.

[1065] This system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and proposes the optimal route home. It can also analyze the user's emotional state and reflect the results in the route home. A specific embodiment of the system is described below.

[1066] Registering user information

[1067] Users launch an application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum." This information is sent from the device to the server and securely stored in a database. The server uses a generative AI model based on the stored user information to perform learning. Examples of machine learning models used here include TensorFlow and PyTorch.

[1068] Real-time traffic information collection

[1069] The user restarts the application after the last train and enables the GPS function. The device obtains the user's current location and sends it to the server in real time. The server then calls a traffic information API (e.g., Google Maps API or OpenWeatherMap API) to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis. This information is temporarily stored in a database.

[1070] Recognizing and recording user emotions

[1071] The device uses a microphone, camera, and sensors to capture data such as the user's voice, facial expressions, and heart rate. This data is sent to an emotion engine (e.g., Amazon Rekognition or IBM Watson), which analyzes the user's emotional state into categories such as "stressed" or "relaxed." The analysis results are sent to a server and used to calculate the route home.

[1072] Optimal route suggestions

[1073] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. Using a generative AI model, it generates the optimal route while taking multiple factors into account. For example, it could suggest routes such as "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." The server sends the calculation results to the device, which then presents them to the user as a map display or text explanation.

[1074] Real-time information updates

[1075] By keeping the app open while the user is traveling, the current location information is continuously sent to the server. The server periodically calls the traffic information API to obtain the latest service status and congestion information. If an unexpected delay or other problem occurs, the server recalculates the route in real time, reanalyzes the user's emotional state, and sends a new route suggestion to the device. The device promptly displays the recalculated route to the user and updates the map and text information if there are any changes.

[1076] Examples of specific examples and prompts

[1077] For example, if a user in Roppongi searches for a route home after the last train, the system will suggest options such as "walk to Shinjuku Station, take the train to Shibuya Station, or take a taxi from Shibuya Station to Roppongi Station." It will suggest the optimal way to get home by taking into account traffic conditions, congestion information, and the user's emotional state.

[1078] Prompt Sentence Examples

[1079] 1. "If my current location is Roppongi, the nearest station is Roppongi Station, and my home is Shibuya, what is the best route home after the last train?"

[1080] 2. "If the user is currently in a stressful situation, please suggest a relaxing way to get home."

[1081] 3. "Calculate the best route home with real-time traffic updates."

[1082] This allows users to quickly find the best route home based on traffic information and their emotional state.

[1083] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] User: Enter information

[1086] Users launch the application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[1087] Input: Home address, nearest station, commuting pattern, preferences

[1088] Output: User information data

[1089] Step 2:

[1090] Terminal: Sending information

[1091] The device encrypts the information entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1092] Input: User information data

[1093] Output: User information sent to the server

[1094] Step 3:

[1095] Server: Information storage

[1096] The server stores the received user information in a database, which is then used to train and predict generative AI models.

[1097] Input: User information data

[1098] Output: User information stored in the database

[1099] Step 4:

[1100] Server: AI learning

[1101] The server uses the stored user information to train a generative AI model, which then makes predictions tailored to each individual user.

[1102] Input: User information stored in the database

[1103] Output: Trained generative AI model

[1104] Step 5:

[1105] User:GPS Enabled

[1106] The user restarts the application after the last train and enables the GPS function, which allows the app to automatically obtain the user's current location.

[1107] Input: None

[1108] Output: User's current location

[1109] Step 6:

[1110] Device: Send current location

[1111] The device transmits the acquired location information to the server in real time, where it is re-encrypted and transmitted securely.

[1112] Input: User's current location

[1113] Output: Location information sent to the server

[1114] Step 7:

[1115] Server: Traffic information collection

[1116] The server calls the traffic information API to collect real-time operational status and congestion information for trains, buses, taxis, etc. This information is temporarily stored in a database.

[1117] Input: Current location

[1118] Output: Traffic information stored in a database

[1119] Step 8:

[1120] Device: Emotion data collection

[1121] The device uses microphones, cameras, and sensors to capture data such as the user's voice, facial expressions, and heart rate, which are then sent to the emotion engine.

[1122] Input: Voice data, facial expression data, heart rate data

[1123] Output: Emotion data

[1124] Step 9:

[1125] Sentiment Engine: Sentiment Analysis

[1126] The emotion engine analyzes the collected data, classifies the user's emotional state as "stressed" or "relaxed," etc., and sends the results to the server.

[1127] Input: Emotion data

[1128] Output: Emotion analysis results

[1129] Step 10:

[1130] Server: Optimal route calculation

[1131] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. It uses a generative AI model to predict the optimal route.

[1132] Input: Current location information, user information, traffic information, sentiment analysis results

[1133] Output: Optimal route data

[1134] Step 11:

[1135] Server: Route proposal generation

[1136] The server generates specific route suggestions based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1137] Input: Optimal route data

[1138] Output: Route suggestion data

[1139] Step 12:

[1140] Server: Send proposal

[1141] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1142] Input: Route suggestion data

[1143] Output: Route suggestions sent to the device

[1144] Step 13:

[1145] Terminal: Route display

[1146] The device then displays the route suggestions to the user, either as a map view or as a text description, which the user can review.

[1147] Input: Route suggestion data

[1148] Output: Route display to user

[1149] Step 14:

[1150] User:Continuously send current location

[1151] Users keep the app open while they move around, continuously sending their location information to the server.

[1152] Input: Current location

[1153] Output: Location information sent to the server

[1154] Step 15:

[1155] Server: Traffic information monitoring

[1156] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the database as necessary.

[1157] Input: Data from traffic information API

[1158] Output: Updated traffic information

[1159] Step 16:

[1160] Server: Route recalculation

[1161] The server responds to unexpected delays and troubles and recalculates the route in real time based on traffic information and emotional state.

[1162] Input: updated traffic information, sentiment analysis results

[1163] Output: Recalculated route data

[1164] Step 17:

[1165] Server: Send a second proposal

[1166] The server sends the new recalculated route proposal to the device, including any changes to the mode of transportation if necessary.

[1167] Input: Recalculated route data

[1168] Output: New route proposal sent to the device

[1169] Step 18:

[1170] Terminal: Reroute display

[1171] The device promptly displays recalculated route suggestions to the user, updates the map and text information, and notifies the user.

[1172] Input: New route proposal data

[1173] Output: Updated route displayed to user

[1174] (Application example 2)

[1175] 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."

[1176] Conventional home route suggestion systems suggest the optimal route based on the user's current location and transportation information, but because they do not take the user's emotional state into consideration, they are unable to reduce the user's stress and frustration.In addition, their ability to collect traffic information and recalculate routes in real time is limited, making them unable to respond to unexpected delays and congestion.

[1177] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation, means for analyzing the acquired emotional state of the user, means for calculating the optimal route home based on the above information and analysis results, and means for providing the calculated route information. This makes it possible to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[1178] "Means for obtaining the user's current location" refers to the means for obtaining location information about the user's current location, and typically involves using a GPS module or location information API.

[1179] "Means for registering the user's home address, nearest station, and preferences" refers to a means for inputting and saving information about the user's home location, nearest station, and preferred means and route for getting home.

[1180] "Means for collecting real-time information on transportation" refers to means for collecting information on the operation status and congestion status of transportation such as trains, buses, and taxis in real time.

[1181] The "means for analyzing the acquired emotional state of the user" refers to a means for acquiring data such as the user's facial expressions, voice, and heart rate, and analyzing that emotional state.

[1182] The "means for calculating the optimal route home" is a means for calculating a route that enables the user to safely return home via the optimal route based on information such as the user's current location, traffic conditions, the user's preferences, and emotional state.

[1183] The "means for providing calculated route information" refers to a means for providing the user with information on the calculated route home, which is usually done through a smartphone application.

[1184] "Means for the server to recalculate the route based on information on delays and congestion of public transport and notify the user" refers to a means for the server to detect changes in information on delays and congestion of public transport in real time, recalculate the route based on that information, and notify the user of the results.

[1185] The "means for recalculating a route according to the acquired emotional state" is a means for recalculating a route based on the emotional state of the user for the purpose of reducing stress, etc.

[1186] "Artificial intelligence that learns from a user's past movement history and understands their preferences and patterns" refers to artificial intelligence that uses machine learning algorithms to analyze past movement history data and learn and understand the user's preferences and patterns.

[1187] A specific embodiment for carrying out the present invention will be described below. The following system is used to propose an optimal route home based on the user's current location and emotional state.

[1188] First, the user registers basic information such as their home address, nearest station, and preferences through a smartphone application. The system then obtains the user's current location using a GPS module and Google Maps API. Real-time transportation information is collected using the Google Transit API and OpenWeatherMap API. Furthermore, the system uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze the user's emotional state based on data such as facial images, voice, and heart rate.

[1189] The server aggregates this data and uses machine learning algorithms to learn about the user's past travel history and emotional state. Based on this information, it calculates the optimal route for the user to return home comfortably. The calculated route information is provided to the user via a smartphone application.

[1190] For example, assume a user is in Roppongi, and their home is in Roppongi, Minato Ward, Tokyo. If the user is returning home at night and experiencing high stress, the system will suggest the fastest and least stressful route. Specifically, it might suggest walking to Shinjuku Station, then taking a train to Shibuya Station, and finally taking a taxi from Shibuya Station to Roppongi Station. The route is dynamically recalculated based on real-time updates to traffic information and the user's emotional state.

[1191] Examples of prompt sentences include the following:

[1192] "Consider the current traffic situation and suggest the best way home based on future stress levels. The current location is Roppongi, and the destination is Roppongi, Minato-ku, Tokyo. Sentiment analysis indicates the user is in a high stress state. Given this, what is the best way home?"

[1193] As a result, this system is able to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[1194] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1195] Step 1:

[1196] The user launches the smartphone application and inputs their home address, nearest station, commuting pattern, and preferences (input: user information). The device then sends the input user information to the server (output: upload of user information data).

[1197] Step 2:

[1198] The server stores the received user information in a database. This data is used for training the AI ​​model later (input: user information, output: user information stored in the database).

[1199] Step 3:

[1200] The server uses machine learning to learn the user's commuting patterns and preferences based on the stored user information, enabling it to make predictions that are optimized for each individual user (input: user information, output: learning model).

[1201] Step 4:

[1202] When the user restarts the app after the last train and enables the GPS function, the device acquires the user's current location and sends that information to the server (input: GPS location information, output: upload of current location data).

[1203] Step 5:

[1204] The server uses a traffic information API to collect real-time operating status and congestion information for nearby trains, buses, and taxis. This information is temporarily stored in a database (input: current location data, traffic information API, output: real-time traffic data).

[1205] Step 6:

[1206] The device uses a microphone, camera, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate. This data is sent to the emotion engine (input: voice, facial expressions, heart rate data; output: emotion data).

[1207] Step 7:

[1208] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as "stressed state" or "relaxed state" (input: emotion data, output: emotion analysis results).

[1209] Step 8:

[1210] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine (input: current location information, user information, traffic information, emotion analysis results, output: optimal route).

[1211] Step 9:

[1212] The server generates a specific route home based on the calculation results and sends it to the terminal (input: optimal route, output: route proposal data).

[1213] Step 10:

[1214] The terminal displays the optimal route proposal to the user, visually displaying the proposed route on a map and providing detailed textual explanations (input: route proposal data, output: route display).

[1215] Step 11:

[1216] The user keeps the app open while moving, and the app continuously obtains their location. If there are any changes, the app updates the location and sends it to the server (Input: updated location data, Output: continuous location data upload).

[1217] Step 12:

[1218] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is updated when necessary (input: traffic information API, output: continuous traffic information).

[1219] Step 13:

[1220] The server recalculates the route in real time to accommodate unexpected delays and other issues, taking into account the user's emotional state (input: updated traffic information, emotion analysis results, output: recalculated route).

[1221] Step 14:

[1222] The server sends the recalculated new route proposal to the terminal (input: recalculated route, output: reroute proposal data).

[1223] Step 15:

[1224] The device promptly displays new route suggestions to the user, and if there are any changes, updates the map and text information and notifies the user (input: reroute suggestion data, output: reroute display).

[1225] 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.

[1226] 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.

[1227] 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.

[1228] [Third embodiment]

[1229] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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).

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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."

[1241] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. The specific processing contents of the system and examples are explained below.

[1242] Registering user information

[1243] 1. User (device): Enter information

[1244] Users launch the app and register their home address, nearest station, commuting pattern, and preferences. For example, a user enters information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[1245] 2. Terminal: Information transmission

[1246] The device sends the entered user information to the server, confirms that a connection with the server has been established, and encrypts and transmits the data.

[1247] 3. Server: Information storage

[1248] The server stores the received user information in a database, which the AI ​​model can later use to learn the user's individual needs.

[1249] 4. Server: AI Learning

[1250] The server trains the AI ​​model based on the stored user information, building a personalized predictive model based on the user's commuting patterns and preferences.

[1251] Real-time traffic information collection

[1252] 5. User (Device): Enable GPS

[1253] The user launches the app after the last train and enables GPS, which then obtains the user's current location and sends that information to the server.

[1254] 6. Device: Send current location

[1255] The device sends the user's current location information to the server in real time and continuously transmits GPS data.

[1256] 7. Server: Traffic information collection

[1257] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, temporarily stores this information, and updates it as needed.

[1258] Optimal route suggestions

[1259] 8. Server: Optimal route calculation

[1260] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[1261] 9. Server: Route proposal generation

[1262] The server then generates a specific route home based on the calculation results, suggesting, for example, "walk to Shinjuku Station, take the train to Shibuya Station, and then take a taxi from Shibuya Station to Roppongi Station."

[1263] 10. Server: Send Proposal

[1264] The server then sends the generated route proposal to the device, including route details and a total cost estimate.

[1265] 11. Terminal: Route display

[1266] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1267] Real-time information updates

[1268] 12. User (device): Continuously send current location

[1269] Users keep the app open while they move around, and the app continuously obtains their location and sends updates to the server if there are any changes.

[1270] 13. Server: Traffic Condition Monitoring

[1271] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[1272] 14. Server: Route Recalculation

[1273] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[1274] 15. Server: Send Re-Proposal

[1275] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1276] 16. Terminal: Reroute Display

[1277] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1278] This allows users to find the best route home even after the last train, allowing them to get home quickly while keeping travel costs down.

[1279] The processing flow will be explained below.

[1280] Step 1:

[1281] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences using a form, and then presses the "Submit" button once the information is complete.

[1282] Step 2:

[1283] The terminal sends the entered information to the server. At this time, the data is encrypted and the information is sent using a secure communication protocol. The terminal then checks the connection with the server.

[1284] Step 3:

[1285] The server stores the received user information in a database, which is then used to train the AI ​​model.

[1286] Step 4:

[1287] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[1288] Step 5:

[1289] The user (device) restarts the app after the last train and enables the GPS function, and the app obtains the user's current location.

[1290] Step 6:

[1291] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[1292] Step 7:

[1293] The server uses a traffic information API to collect real-time information on train, bus, and taxi service status and congestion information around the user's current location. The acquired information is temporarily stored in a database.

[1294] Step 8:

[1295] The server calculates the optimal route home based on the user's current location, registered user information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[1296] Step 9:

[1297] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1298] Step 10:

[1299] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1300] Step 11:

[1301] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1302] Step 12:

[1303] The user (device) keeps the app open while moving, and the current location is continuously acquired. If there is a change, the current location information is updated and sent to the server.

[1304] Step 13:

[1305] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[1306] Step 14:

[1307] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[1308] Step 15:

[1309] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1310] Step 16:

[1311] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1312] Example 1

[1313] 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."

[1314] Currently, many users face difficulties in finding a way to get home after the last train. This is especially true at night, when transportation options are limited or when transportation is affected by delays or congestion. Furthermore, because proposed routes do not reflect the user's individual preferences or commuting patterns, this can be time-consuming and costly. This invention is proposed to solve these problems.

[1315] 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.

[1316] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, commuting pattern, and preferences, means for transmitting encrypted user information to the server, means for collecting real-time information on transportation facilities, means for calculating an optimal route home based on the user information and real-time traffic information, means for providing the calculated route information, and means for updating the traffic information in real time and recalculating the route as necessary. This allows the user to receive a real-time suggestion of an optimal route home according to traffic conditions, enabling them to return home quickly and efficiently.

[1317] "Means for obtaining the user's current location" refers to a means for accurately identifying the user's current location using location information technology such as GPS or Wi-Fi.

[1318] "Means for registering a user's home address, nearest station, commuting patterns, and preferences" means an interface or input method that allows a user to register their home address, nearest station, daily commuting patterns, and transportation preferences in the system.

[1319] "Means for sending encrypted user information to a server" refers to a technology that encrypts data to securely protect information entered by users and sends it to a server via the Internet.

[1320] "Means of collecting real-time information on transportation" refers to technology that uses transportation information services and APIs to obtain real-time information on the operation status and congestion of trains, buses, taxis, etc.

[1321] "Means for calculating the optimal route home based on user information and real-time traffic information" refers to an algorithm or calculation method that calculates the optimal route home, taking into account the information registered by the user and the collected current traffic conditions.

[1322] The "means for providing calculated route information" refers to an interface or output method for providing the calculated route home in a form that is easy for the user to understand.

[1323] "Means for updating traffic information in real time and recalculating routes as needed" refers to methods or algorithms that continuously update information in response to changing traffic conditions and recalculate a new route home as needed.

[1324] The present invention provides a system for assisting a user in finding the best route home after the last train. Specific embodiments of the system will be described below.

[1325] Overview and Structure

[1326] This system uses the user's current location, home address, nearest station, commuting pattern, and preference information to collect real-time traffic information and calculate and suggest the optimal route home. The main components of the system are the user's device, server, database, and traffic information provision API.

[1327] Hardware and software used

[1328] Device: The smartphone or tablet used by the user

[1329] Server: Cloud-based server (e.g., AWS, Microsoft Azure, Google Cloud)

[1330] Database: SQL or NoSQL database (e.g. MySQL, PostgreSQL, MongoDB)

[1331] Traffic information API: Real-time traffic information providers such as Google Maps API and OpenWeatherMap

[1332] Encryption technology: AES encryption

[1333] Data processing and calculation

[1334] 1. Enter and submit user information

[1335] Users launch a dedicated app on their smartphone and enter their home address, nearest train station, commuting pattern, and individual preferences.

[1336] The data entered is encrypted within the app and securely sent to the server.

[1337] 2. Server-side information processing

[1338] The server decrypts the received encrypted data and stores it in a database.

[1339] Using the stored data, a generative AI model learns users' commuting patterns and preferences, built using TensorFlow and PyTorch.

[1340] 3. Collection of real-time traffic information

[1341] When the user launches the app again and sends their current location information, the server calls the traffic information API to obtain real-time traffic conditions.

[1342] The retrieved information is temporarily stored in the Redis cache.

[1343] 4. Calculating the optimal route

[1344] The server calculates the optimal route home using Dijkstra's algorithm and A algorithm based on the user's current location, registered information, and collected real-time traffic information.

[1345] 5. Route presentation

[1346] The calculated route information is sent from the server to the terminal where it is presented to the user in visual and textual form.

[1347] This route information includes the mode of transportation (walking, train, taxi, etc.), travel time, cost estimates, etc.

[1348] 6. Real-time updates

[1349] The device continuously transmits its current location information, and the server monitors changes in traffic information.

[1350] If traffic conditions change, the server recalculates the optimal route and notifies the user of the new route.

[1351] Examples of specific examples and prompts

[1352] Examples:

[1353] User A lives in Roppongi, Minato-ku, Tokyo, and commutes from 9:00 to 18:00. He is heading home from Shinjuku Station after the last train has left. User A launches the app, enables GPS, and sends his current location to the server. The server uses the Google Maps API to obtain real-time traffic information and calculates the optimal route home, taking into account User A's commuting pattern and preferences. As a result, the server suggests the route: "Take the train from Shinjuku Station to Shibuya Station, then take a taxi from Shibuya Station to Roppongi." This route information is displayed on the app as a map and text.

[1354] Example prompt sentence:

[1355] "If my current location is Shinjuku Station and my home is Roppongi, Minato Ward, Tokyo, please suggest the best route to get home while keeping taxi fare to a minimum."

[1356] This allows users to efficiently obtain the optimal route home in real time.

[1357] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1358] Step 1:

[1359] Input: The user enters their home address, nearest train station, commuting patterns, and preferences into the app.

[1360] How it works: The user launches the app on their smartphone and fills in the designated form with their home address (e.g., Roppongi, Minato-ku, Tokyo), nearest station (e.g., Roppongi Station), commuting pattern (e.g., 9:00 AM to 6:00 PM), and preferences (e.g., want to keep taxi fares to a minimum).

[1361] Output: User information entered on the terminal is generated.

[1362] Step 2:

[1363] Input: Home address, nearest station, commuting patterns, and preference data entered by the user.

[1364] Operation: The device encrypts the collected user information and sends it to the server using AES encryption, for example, AES-256.

[1365] Output: User information data sent to the server in encrypted form.

[1366] Step 3:

[1367] Input: Encrypted user information data.

[1368] How it works: The server decrypts the encrypted data it receives and stores it in a database. The data is decrypted using the corresponding encryption key.

[1369] Output: Decrypted user information stored in the database.

[1370] Step 4:

[1371] Input: Decrypted user information.

[1372] How it works: The server uses this user information to train a generative AI model, which is built using TensorFlow or PyTorch to learn individual patterns for each user.

[1373] Output: A trained generative AI model.

[1374] Step 5:

[1375] Input: trained generative AI model, user's current location.

[1376] How it works: The user restarts the app after the last train and enables GPS, causing the device to collect the user's location.

[1377] Output: Current location information is obtained on the device.

[1378] Step 6:

[1379] Input: User's current location.

[1380] What it does: The device starts streaming continuous GPS data to send real-time location information to the server. The data is sent using WebSockets.

[1381] Output: Real-time location data sent to the server.

[1382] Step 7:

[1383] Input: User location, real-time transport data.

[1384] How it works: The server uses a traffic information API (e.g., Google Maps API) to collect real-time information about train, bus, and taxi service availability and congestion information for the user's area. This information is stored in a Redis cache.

[1385] Output: Temporarily stored real-time traffic information.

[1386] Step 8:

[1387] Input: User's current location information, registered information, real-time traffic information, trained generative AI model.

[1388] How it works: The server uses Dijkstra's algorithm and the A algorithm to calculate the optimal route home based on this information, taking into account the user's preferences.

[1389] Output: The calculated optimal route home.

[1390] Step 9:

[1391] Input: The calculated optimal route home.

[1392] How it works: The server packages this route information in JSON format and sends it to the device.

[1393] Output: Optimal route home information sent from the server to the terminal.

[1394] Step 10:

[1395] Input: The best route home information submitted.

[1396] How it works: The device analyzes the route information it receives and displays it visually and textually on the user interface, and plots the optimal route on a map.

[1397] Output: Route information displayed to the user.

[1398] Step 11:

[1399] Input: The user's continuous location.

[1400] How it works: The user keeps the app open on their device while they move around, and the device continuously sends their location to the server.

[1401] Output: The latest location information sent to the server.

[1402] Step 12:

[1403] Input: Latest traffic information, latest user location information.

[1404] Operation: The server periodically checks the traffic information API to obtain and update the latest traffic status and congestion information.

[1405] Output: Updated latest traffic information.

[1406] Step 13:

[1407] Input: Updated traffic information, user's current location, trained generative AI model.

[1408] What it does: The server recalculates the optimal route based on this information and suggests a new route home if necessary.

[1409] Output: The new recalculated optimal route.

[1410] Step 14:

[1411] Input: The new recalculated optimal route.

[1412] Operation: The server sends the recalculated route in JSON format to the device and notifies the user.

[1413] Output: The new best route information sent to the device.

[1414] Step 15:

[1415] Input: New optimal route information.

[1416] What it does: The device parses the new route information and updates the user interface, notifying the user of any changes.

[1417] Output: The new route information displayed to the user.

[1418] (Application example 1)

[1419] 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."

[1420] Current home-come-home assistance systems that use traffic information are limited to proposing optimal routes home after the last train, and are unable to reflect real-time traffic conditions or road congestion, making it difficult to return home efficiently. Furthermore, they are unable to link with autonomous vehicles, requiring users to choose their own means of transportation, resulting in low convenience. Another issue is that they are unable to provide flexible suggestions that reflect the user's individual travel history and preferences.

[1421] 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.

[1422] In this invention, the server includes a means for acquiring the user's current location, a means for registering the user's home address, nearest station, and preferences, a means for collecting real-time information on transportation, and a means for instructing the autonomous vehicle on the optimal route. This allows the user to always obtain the optimal route home that reflects real-time traffic information, and by coordinating with the autonomous vehicle, the user can return home safely and efficiently. In addition, a generative AI model is used to learn past travel history, enabling flexible route suggestions that take into account the user's preferences and patterns.

[1423] "Means for obtaining the user's current location" means a device or system that measures the user's current location using GPS or other location information technology and transmits that data to the system.

[1424] "Means for registering a user's home address, nearest station, and preferences" refers to a device or system that allows a user to enter their home address, nearest public transport station, and travel preferences (e.g., preferring to avoid taxis) into the system and store this information in a database.

[1425] "Means for collecting real-time information on transportation" refers to devices or systems that use APIs or other data collection technologies to obtain the latest operational status, delay information, and congestion information of transportation services and integrate it into the system.

[1426] The "means for calculating the optimal route home" is a device or system for calculating the most efficient and economical route home for the user based on the user's current location, registered information, and real-time traffic information.

[1427] The "means for providing calculated route information" refers to a device or system that displays the optimal route home calculated by the system on the user's terminal and presents it in a visually easy-to-understand manner.

[1428] "Means for instructing an automated vehicle on the optimal route" refers to a device or system that transmits the calculated optimal route information to the automated vehicle's control system, allowing the automated vehicle to efficiently guide the user to the destination.

[1429] "Means for transmitting user's current location information to a server" refers to a device or system for obtaining user's location information in real time and transmitting it to a server via the Internet or other network.

[1430] "Means for a server to recalculate routes based on information on delays and congestion at public transport facilities and set new routes for self-driving vehicles" refers to a device or system in which a server continuously monitors the latest information on public transport facilities, recalculates the optimal route as needed, and sends that information to self-driving vehicles to set new routes.

[1431] "Artificial intelligence for learning past travel history and understanding user preferences and patterns" refers to a device or system equipped with machine learning technology that analyzes a user's past travel data, learns the user's specific behavioral patterns and preferences, and uses that information to predict future travel.

[1432] A "means for suggesting optimal routes using generative AI models" is a device or system that uses a generative artificial intelligence model to suggest optimal routes and transportation methods that take into account a user's current situation and preferences.

[1433] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. Furthermore, this system enables collaboration with autonomous vehicles and uses a generative AI model to suggest routes.

[1434] System Configuration

[1435] Hardware and Software Configuration

[1436] The system includes the following hardware and software:

[1437] The user's smartphone or smart glasses (with GPS and data communication functions)

[1438] Server (with high-performance database and AI processing capabilities)

[1439] Traffic information API (provides real-time traffic information)

[1440] Navigation systems for autonomous vehicles

[1441] How to get the user's location

[1442] The GPS module built into the device (smartphone or smart glasses) is used to obtain the user's current location. When the user launches the app and enables GPS, the device continues to obtain location information in real time.

[1443] User information registration method

[1444] Users use the app to input their home address, nearest station, commuting pattern, and preferences. For example, a user might enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum," and the information is then saved in a database.

[1445] A means of collecting real-time transportation information

[1446] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, allowing it to constantly maintain the latest information on transportation and provide it to users.

[1447] A way to calculate the best route home

[1448] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, and also takes into account the user's preferences (e.g., wanting to reduce taxi fares) and uses a generative AI model to suggest the optimal route.

[1449] A means of providing calculated route information

[1450] The server generates a specific route home based on the calculation results and sends it to the user's device, which then displays the route proposal to the user on a map and in text format.

[1451] A means of providing optimal route guidance to autonomous vehicles

[1452] The server then sends the calculated optimal route information to the autonomous vehicle's control system, and the vehicle then guides the user safely home based on that route. For example, if a route from Shinjuku Station to Roppongi Station is specified, the autonomous vehicle will use this information to select the optimal route and efficiently transport the user.

[1453] Prompt Sentence Examples

[1454] For example, if a resident of Roppongi is currently in Shinjuku, the prompt to have the generative AI model suggest a route would be:

[1455] Nearest station: Shinjuku Station

[1456] User preference: Minimize taxi fares

[1457] Current location: Shinjuku Station South Exit

[1458] Home address: Roppongi, Minato-ku, Tokyo

[1459] Please suggest the best route for the user to take home after the last train. If necessary, please consider a combination of multiple modes of transport and walking. Please also provide approximate travel costs and travel times.

[1460] Specific examples

[1461] For example, if a user misses the last train near Shinjuku Station, the system will suggest the best route home using the following steps:

[1462] 1. The device obtains its current location using GPS and sends it to the server.

[1463] 2. The server obtains real-time traffic information using the traffic information API.

[1464] 3. The server learns the user's preferences and past travel history and calculates the optimal route using a generative AI model.

[1465] 4. The server sends the calculated route to the device and displays it to the user on a map and in text format.

[1466] 5. The server instructs the autonomous vehicle on the optimal route, and the vehicle guides the user safely home.

[1467] This system allows users to return home safely and efficiently, even after the last train. For example, it can suggest a route that allows users to walk from Shinjuku Station to a nearby self-driving vehicle and go directly to their home.

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] Registering user information

[1471] Users input their home address, nearest train station, commuting patterns, and preferences into their device (smartphone or smart glasses).

[1472] The terminal encrypts the input information and transmits it to the server.

[1473] The server stores the received user information in a database.

[1474] input:

[1475] Your home address, nearest train station, commuting patterns, and preferences

[1476] Data processing and calculation:

[1477] Encrypts user-entered information

[1478] output:

[1479] Encrypted user information

[1480] Specific behavior:

[1481] Enter information through the device's user interface (UI)

[1482] Secure data transmission using HTTPS protocol

[1483] Server-side data decoding and storage

[1484] Step 2:

[1485] Get and send your current location

[1486] The user launches the app and enables GPS.

[1487] The terminal obtains its current location using a GPS module and transmits that information to the server.

[1488] input:

[1489] Location data from the GPS module

[1490] Data processing and calculation:

[1491] Obtaining current location data and format conversion (longitude and latitude)

[1492] output:

[1493] Location information sent to the server

[1494] Specific behavior:

[1495] Enable the GPS function on your device and periodically update your location.

[1496] Send location information to the server in real time

[1497] Step 3:

[1498] Real-time traffic information collection

[1499] The server obtains real-time traffic information through a traffic information provision API.

[1500] The acquired traffic information is temporarily stored in a database.

[1501] input:

[1502] Traffic information API request

[1503] Data processing and calculation:

[1504] Parsing and saving data retrieved from the API

[1505] output:

[1506] Real-time traffic information

[1507] Specific behavior:

[1508] The server periodically calls the API to retrieve data.

[1509] Analyze JSON format data and save the necessary information to a database

[1510] Step 4:

[1511] Optimal Route Calculation

[1512] The server calculates the optimal route home based on the user's current location, registration information, and real-time traffic information.

[1513] Generate route suggestions using a generative AI model.

[1514] input:

[1515] User information, current location, real-time traffic information

[1516] Data processing and calculation:

[1517] Integrates multiple input data and executes route calculation algorithms

[1518] Uses generative AI models to calculate optimal routes

[1519] output:

[1520] Proposing the best route home

[1521] Specific behavior:

[1522] Algorithms on the server analyze the data and calculate the shortest time and lowest cost route.

[1523] Generate route suggestions taking into account user preferences

[1524] Step 5:

[1525] Providing route information

[1526] The server generates a specific route home based on the calculation results and sends it to the user's device.

[1527] The device displays the received route information to the user in map and text format.

[1528] input:

[1529] Data suggesting the best route home

[1530] Data processing and calculation:

[1531] Route data format conversion (map data, text data)

[1532] output:

[1533] Route information displayed on the user's device

[1534] Specific behavior:

[1535] The server sends route proposal data to the terminal

[1536] Display data received by the device as a map and text

[1537] Step 6:

[1538] Commanding autonomous vehicles

[1539] The server sends the optimal route information to the autonomous vehicle's control system, which guides the vehicle to the user.

[1540] The autonomous vehicle will drive users to their destination based on the optimal route.

[1541] input:

[1542] Data suggesting the best route home

[1543] Data processing and calculation:

[1544] Converting route data into a format suitable for the navigation system of an autonomous vehicle

[1545] output:

[1546] Route instructions for autonomous vehicles

[1547] Specific behavior:

[1548] The server sends the route information to the vehicle's control system

[1549] The vehicle's navigation system sets the route and begins operation.

[1550] Step 7:

[1551] Real-time recalculation and notifications

[1552] The server continuously monitors traffic information and recalculates the optimal route when new information becomes available.

[1553] If a route change is necessary, the device and the autonomous vehicle will be notified.

[1554] input:

[1555] Continuously updated traffic information

[1556] Data processing and calculation:

[1557] Route recalculation based on real-time information

[1558] output:

[1559] Notification of updated route information

[1560] Specific behavior:

[1561] The server periodically calls the traffic API to obtain the latest information.

[1562] Recalculate the route and notify the device and vehicle if necessary

[1563] In this way, through a series of steps, the present invention can provide users with the optimal route home in real time and, in cooperation with autonomous vehicles, support a safe and efficient journey home.

[1564] 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.

[1565] The present invention relates to a system that helps users find the best route home after the last train. The system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and suggests the best route home. Furthermore, by combining this system with an emotion engine, the system can recognize the user's emotional state and reflect it in the suggested route home. Specific processing details and examples of the system are described below.

[1566] Registering user information

[1567] 1. User (device): Enter information

[1568] Users launch the app and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[1569] 2. Terminal: Information transmission

[1570] The device sends the entered user information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[1571] 3. Server: Information storage

[1572] The server stores the received user information in a database, which is then used to train the AI ​​model.

[1573] 4. Server: AI Learning

[1574] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[1575] Real-time traffic information collection

[1576] 5. User (Device): Enable GPS

[1577] When the user restarts the app after the last train and enables the GPS function, the app obtains the user's current location and sends that information to the server.

[1578] 6. Device: Send current location

[1579] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[1580] 7. Server: Traffic information collection

[1581] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[1582] Recognizing and recording user emotions

[1583] 8. Terminal: Emotional Data Collection

[1584] The device uses microphones, cameras, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate, and sends this data to the emotion engine.

[1585] 9. Sentiment Engine: Sentiment Analysis

[1586] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as, for example, "stressed" or "relaxed."

[1587] Optimal route suggestions

[1588] 10. Server: Optimal route calculation

[1589] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine.

[1590] 11. Server: Route proposal generation

[1591] The server then generates a specific route home based on the calculation results. For example, it might suggest "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." If the stress level during the stay is high, it would suggest a more relaxing way home.

[1592] 12. Server: Send Proposal

[1593] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1594] 13. Terminal: Route display

[1595] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1596] Real-time information updates

[1597] 14. User (device): Continuously send current location

[1598] Users keep the app open while they move around, and it continually obtains their location and sends updates to the server if there are any changes.

[1599] 15. Server: Traffic Condition Monitoring

[1600] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[1601] 16. Server: Route Recalculation

[1602] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[1603] 17. Server: Send Re-Proposal

[1604] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1605] 18. Terminal: Reroute Display

[1606] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1607] This not only enables users to find the best route home even after the last train, but also allows them to receive suggestions that take their emotional state into account, making for a less stressful journey home.

[1608] The processing flow will be explained below.

[1609] Step 1:

[1610] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[1611] Step 2:

[1612] The device sends the entered information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[1613] Step 3:

[1614] The server stores the received user information in a database, which is then used to train the AI ​​model.

[1615] Step 4:

[1616] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[1617] Step 5:

[1618] The user (device) restarts the app after the last train and enables the GPS function, which then acquires the user's current location.

[1619] Step 6:

[1620] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[1621] Step 7:

[1622] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[1623] Step 8:

[1624] The device uses a microphone, camera, sensors, etc. to obtain emotional data from the user. For example, it can assess stress levels based on the user's tone of voice and measure relaxation levels using a heart rate sensor.

[1625] Step 9:

[1626] The emotion engine analyzes the acquired emotion data and determines the user's emotional state. The result is classified as "stressed" or "relaxed" and sent to the server.

[1627] Step 10:

[1628] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. For example, if a user is feeling stressed, it will prioritize a route with less congestion.

[1629] Step 11:

[1630] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1631] Step 12:

[1632] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1633] Step 13:

[1634] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1635] Step 14:

[1636] The user (device) keeps the app open while moving, and the app continuously acquires the user's location. If there are any changes, the information is updated and sent to the server.

[1637] Step 15:

[1638] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[1639] Step 16:

[1640] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[1641] Step 17:

[1642] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1643] Step 18:

[1644] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1645] Through the above processing steps, the system can find the optimal route home after the last train, allowing the user to have a stress-free journey home that takes into account their emotional state.

[1646] Example 2

[1647] 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."

[1648] It is difficult for users to find the optimal route home after the last train, especially when considering transportation delays, congestion, and the user's emotional state. As a result, users often spend a long time finding a route home that is less stressful. This can reduce user satisfaction and lead to further stress on the way home. Therefore, there is a need for a system that can quickly suggest the optimal route home that reflects traffic information and the user's emotional state in real time.

[1649] 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.

[1650] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation facilities, means for analyzing the user's emotional state, means for calculating an optimal route home based on the above information and the results of the emotional state analysis, and means for providing the calculated route information, thereby enabling the user to quickly find an optimal route home taking into account real-time updated traffic information and the user's emotional state.

[1651] "Means for obtaining the user's current location" refers to technologies or methods that use the GPS function or location information services of the device used by the user to identify and obtain the user's current geographical location.

[1652] "Means for registering a user's home address, nearest station, and preferences" refers to the technology or method by which a user can input their home address, nearest station, and various personal preferences (such as wanting to take a taxi) through an application or terminal and store them in a database.

[1653] "Means of collecting real-time information on transportation" refers to technologies and methods that use external transportation information services and APIs to obtain the latest information on the operation status and congestion levels of trains, buses, taxis, etc.

[1654] "Means for analyzing the user's emotional state" refers to technologies and methods that collect data such as the user's voice, facial expressions, and heart rate, and then use artificial intelligence and analytical algorithms to classify the user's emotions into "stressed state," "relaxed state," etc.

[1655] "Means for calculating the optimal route home" refers to technology or methods that use a generative AI model to generate the optimal route home based on the user's current location information, registration information, real-time traffic information, and emotion analysis results.

[1656] "Means for providing calculated route information" refers to a technology or method for sending calculated information on the optimal route home to the user's device and presenting it as a map display or text description.

[1657] "Registered user information" refers to information such as home address, nearest station, and preferences that the user enters into the application or device.

[1658] An "emotion engine" refers to an artificial intelligence or analytical algorithm system that analyzes collected emotional data to identify the user's emotional state.

[1659] "Transportation Information API" refers to an application programming interface for accessing and obtaining information from external services that provide information on transportation operation status and congestion levels.

[1660] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to learn user behavior and patterns from various data and make predictions and suggestions.

[1661] This invention provides a system that allows users to find the best route home after the last train, and in particular, reflects traffic information and the user's emotional state in real time to support a more comfortable journey home.

[1662] This system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and proposes the optimal route home. It can also analyze the user's emotional state and reflect the results in the route home. A specific embodiment of the system is described below.

[1663] Registering user information

[1664] Users launch an application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum." This information is sent from the device to the server and securely stored in a database. The server uses a generative AI model based on the stored user information to perform learning. Examples of machine learning models used here include TensorFlow and PyTorch.

[1665] Real-time traffic information collection

[1666] The user restarts the application after the last train and enables the GPS function. The device obtains the user's current location and sends it to the server in real time. The server then calls a traffic information API (e.g., Google Maps API or OpenWeatherMap API) to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis. This information is temporarily stored in a database.

[1667] Recognizing and recording user emotions

[1668] The device uses a microphone, camera, and sensors to capture data such as the user's voice, facial expressions, and heart rate. This data is sent to an emotion engine (e.g., Amazon Rekognition or IBM Watson), which analyzes the user's emotional state into categories such as "stressed" or "relaxed." The analysis results are sent to a server and used to calculate the route home.

[1669] Optimal route suggestions

[1670] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. Using a generative AI model, it generates the optimal route while taking multiple factors into account. For example, it could suggest routes such as "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." The server sends the calculation results to the device, which then presents them to the user as a map display or text explanation.

[1671] Real-time information updates

[1672] By keeping the app open while the user is traveling, the current location information is continuously sent to the server. The server periodically calls the traffic information API to obtain the latest service status and congestion information. If an unexpected delay or other problem occurs, the server recalculates the route in real time, reanalyzes the user's emotional state, and sends a new route suggestion to the device. The device promptly displays the recalculated route to the user and updates the map and text information if there are any changes.

[1673] Examples of specific examples and prompts

[1674] For example, if a user in Roppongi searches for a route home after the last train, the system will suggest options such as "walk to Shinjuku Station, take the train to Shibuya Station, or take a taxi from Shibuya Station to Roppongi Station." It will suggest the optimal way to get home by taking into account traffic conditions, congestion information, and the user's emotional state.

[1675] Prompt Sentence Examples

[1676] 1. "If my current location is Roppongi, the nearest station is Roppongi Station, and my home is Shibuya, what is the best route home after the last train?"

[1677] 2. "If the user is currently in a stressful situation, please suggest a relaxing way to get home."

[1678] 3. "Calculate the best route home with real-time traffic updates."

[1679] This allows users to quickly find the best route home based on traffic information and their emotional state.

[1680] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1681] Step 1:

[1682] User: Enter information

[1683] Users launch the application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[1684] Input: Home address, nearest station, commuting pattern, preferences

[1685] Output: User information data

[1686] Step 2:

[1687] Terminal: Sending information

[1688] The device encrypts the information entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1689] Input: User information data

[1690] Output: User information sent to the server

[1691] Step 3:

[1692] Server: Information storage

[1693] The server stores the received user information in a database, which is then used to train and predict generative AI models.

[1694] Input: User information data

[1695] Output: User information stored in the database

[1696] Step 4:

[1697] Server: AI learning

[1698] The server uses the stored user information to train a generative AI model, which then makes predictions tailored to each individual user.

[1699] Input: User information stored in the database

[1700] Output: Trained generative AI model

[1701] Step 5:

[1702] User:GPS Enabled

[1703] The user restarts the application after the last train and enables the GPS function, which allows the app to automatically obtain the user's current location.

[1704] Input: None

[1705] Output: User's current location

[1706] Step 6:

[1707] Device: Send current location

[1708] The device transmits the acquired location information to the server in real time, where it is re-encrypted and transmitted securely.

[1709] Input: User's current location

[1710] Output: Location information sent to the server

[1711] Step 7:

[1712] Server: Traffic information collection

[1713] The server calls the traffic information API to collect real-time operational status and congestion information for trains, buses, taxis, etc. This information is temporarily stored in a database.

[1714] Input: Current location

[1715] Output: Traffic information stored in a database

[1716] Step 8:

[1717] Device: Emotion data collection

[1718] The device uses microphones, cameras, and sensors to capture data such as the user's voice, facial expressions, and heart rate, which are then sent to the emotion engine.

[1719] Input: Voice data, facial expression data, heart rate data

[1720] Output: Emotion data

[1721] Step 9:

[1722] Sentiment Engine: Sentiment Analysis

[1723] The emotion engine analyzes the collected data, classifies the user's emotional state as "stressed" or "relaxed," etc., and sends the results to the server.

[1724] Input: Emotion data

[1725] Output: Emotion analysis results

[1726] Step 10:

[1727] Server: Optimal route calculation

[1728] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. It uses a generative AI model to predict the optimal route.

[1729] Input: Current location information, user information, traffic information, sentiment analysis results

[1730] Output: Optimal route data

[1731] Step 11:

[1732] Server: Route proposal generation

[1733] The server generates specific route suggestions based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1734] Input: Optimal route data

[1735] Output: Route suggestion data

[1736] Step 12:

[1737] Server: Send proposal

[1738] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1739] Input: Route suggestion data

[1740] Output: Route suggestions sent to the device

[1741] Step 13:

[1742] Terminal: Route display

[1743] The device then displays the route suggestions to the user, either as a map view or as a text description, which the user can review.

[1744] Input: Route suggestion data

[1745] Output: Route display to user

[1746] Step 14:

[1747] User:Continuously send current location

[1748] Users keep the app open while they move around, continuously sending their location information to the server.

[1749] Input: Current location

[1750] Output: Location information sent to the server

[1751] Step 15:

[1752] Server: Traffic information monitoring

[1753] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the database as necessary.

[1754] Input: Data from traffic information API

[1755] Output: Updated traffic information

[1756] Step 16:

[1757] Server: Route recalculation

[1758] The server responds to unexpected delays and troubles and recalculates the route in real time based on traffic information and emotional state.

[1759] Input: updated traffic information, sentiment analysis results

[1760] Output: Recalculated route data

[1761] Step 17:

[1762] Server: Send a second proposal

[1763] The server sends the new recalculated route proposal to the device, including any changes to the mode of transportation if necessary.

[1764] Input: Recalculated route data

[1765] Output: New route proposal sent to the device

[1766] Step 18:

[1767] Terminal: Reroute display

[1768] The device promptly displays recalculated route suggestions to the user, updates the map and text information, and notifies the user.

[1769] Input: New route proposal data

[1770] Output: Updated route displayed to user

[1771] (Application example 2)

[1772] 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."

[1773] Conventional home route suggestion systems suggest the optimal route based on the user's current location and transportation information, but because they do not take the user's emotional state into consideration, they are unable to reduce the user's stress and frustration.In addition, their ability to collect traffic information and recalculate routes in real time is limited, making them unable to respond to unexpected delays and congestion.

[1774] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation, means for analyzing the acquired emotional state of the user, means for calculating the optimal route home based on the above information and analysis results, and means for providing the calculated route information. This makes it possible to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[1775] "Means for obtaining the user's current location" refers to the means for obtaining location information about the user's current location, and typically involves using a GPS module or location information API.

[1776] "Means for registering the user's home address, nearest station, and preferences" refers to a means for inputting and saving information about the user's home location, nearest station, and preferred means and route for getting home.

[1777] "Means for collecting real-time information on transportation" refers to means for collecting information on the operation status and congestion status of transportation such as trains, buses, and taxis in real time.

[1778] The "means for analyzing the acquired emotional state of the user" refers to a means for acquiring data such as the user's facial expressions, voice, and heart rate, and analyzing that emotional state.

[1779] The "means for calculating the optimal route home" is a means for calculating a route that enables the user to safely return home via the optimal route based on information such as the user's current location, traffic conditions, the user's preferences, and emotional state.

[1780] The "means for providing calculated route information" refers to a means for providing the user with information on the calculated route home, which is usually done through a smartphone application.

[1781] "Means for the server to recalculate the route based on information on delays and congestion of public transport and notify the user" refers to a means for the server to detect changes in information on delays and congestion of public transport in real time, recalculate the route based on that information, and notify the user of the results.

[1782] The "means for recalculating a route according to the acquired emotional state" is a means for recalculating a route based on the emotional state of the user for the purpose of reducing stress, etc.

[1783] "Artificial intelligence that learns from a user's past movement history and understands their preferences and patterns" refers to artificial intelligence that uses machine learning algorithms to analyze past movement history data and learn and understand the user's preferences and patterns.

[1784] A specific embodiment for carrying out the present invention will be described below. The following system is used to propose an optimal route home based on the user's current location and emotional state.

[1785] First, the user registers basic information such as their home address, nearest station, and preferences through a smartphone application. The system then obtains the user's current location using a GPS module and Google Maps API. Real-time transportation information is collected using the Google Transit API and OpenWeatherMap API. Furthermore, the system uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze the user's emotional state based on data such as facial images, voice, and heart rate.

[1786] The server aggregates this data and uses machine learning algorithms to learn about the user's past travel history and emotional state. Based on this information, it calculates the optimal route for the user to return home comfortably. The calculated route information is provided to the user via a smartphone application.

[1787] For example, assume a user is in Roppongi, and their home is in Roppongi, Minato Ward, Tokyo. If the user is returning home at night and experiencing high stress, the system will suggest the fastest and least stressful route. Specifically, it might suggest walking to Shinjuku Station, then taking a train to Shibuya Station, and finally taking a taxi from Shibuya Station to Roppongi Station. The route is dynamically recalculated based on real-time updates to traffic information and the user's emotional state.

[1788] Examples of prompt sentences include the following:

[1789] "Consider the current traffic situation and suggest the best way home based on future stress levels. The current location is Roppongi, and the destination is Roppongi, Minato-ku, Tokyo. Sentiment analysis indicates the user is in a high stress state. Given this, what is the best way home?"

[1790] As a result, this system is able to propose the optimal route home in real time, taking into account the user's emotional state and traffic information.

[1791] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1792] Step 1:

[1793] The user launches the smartphone application and inputs their home address, nearest station, commuting pattern, and preferences (input: user information). The device then sends the input user information to the server (output: upload of user information data).

[1794] Step 2:

[1795] The server stores the received user information in a database. This data is used for training the AI ​​model later (input: user information, output: user information stored in the database).

[1796] Step 3:

[1797] The server uses machine learning to learn the user's commuting patterns and preferences based on the stored user information, enabling it to make predictions that are optimized for each individual user (input: user information, output: learning model).

[1798] Step 4:

[1799] When the user restarts the app after the last train and enables the GPS function, the device acquires the user's current location and sends that information to the server (input: GPS location information, output: upload of current location data).

[1800] Step 5:

[1801] The server uses a traffic information API to collect real-time operating status and congestion information for nearby trains, buses, and taxis. This information is temporarily stored in a database (input: current location data, traffic information API, output: real-time traffic data).

[1802] Step 6:

[1803] The device uses a microphone, camera, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate. This data is sent to the emotion engine (input: voice, facial expressions, heart rate data; output: emotion data).

[1804] Step 7:

[1805] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as "stressed state" or "relaxed state" (input: emotion data, output: emotion analysis results).

[1806] Step 8:

[1807] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine (input: current location information, user information, traffic information, emotion analysis results, output: optimal route).

[1808] Step 9:

[1809] The server generates a specific route home based on the calculation results and sends it to the terminal (input: optimal route, output: route proposal data).

[1810] Step 10:

[1811] The terminal displays the optimal route proposal to the user, visually displaying the proposed route on a map and providing detailed textual explanations (input: route proposal data, output: route display).

[1812] Step 11:

[1813] The user keeps the app open while moving, and the app continuously obtains their location. If there are any changes, the app updates the location and sends it to the server (Input: updated location data, Output: continuous location data upload).

[1814] Step 12:

[1815] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is updated when necessary (input: traffic information API, output: continuous traffic information).

[1816] Step 13:

[1817] The server recalculates the route in real time to accommodate unexpected delays and other issues, taking into account the user's emotional state (input: updated traffic information, emotion analysis results, output: recalculated route).

[1818] Step 14:

[1819] The server sends the recalculated new route proposal to the terminal (input: recalculated route, output: reroute proposal data).

[1820] Step 15:

[1821] The device promptly displays new route suggestions to the user, and if there are any changes, updates the map and text information and notifies the user (input: reroute suggestion data, output: reroute display).

[1822] 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.

[1823] 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.

[1824] 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.

[1825] [Fourth embodiment]

[1826] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1827] 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.

[1828] 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).

[1829] 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.

[1830] 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.

[1831] 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).

[1832] 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.

[1833] 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.

[1834] 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.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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."

[1839] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. The specific processing contents of the system and examples are explained below.

[1840] Registering user information

[1841] 1. User (device): Enter information

[1842] Users launch the app and register their home address, nearest station, commuting pattern, and preferences. For example, a user enters information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[1843] 2. Terminal: Information transmission

[1844] The device sends the entered user information to the server, confirms that a connection with the server has been established, and encrypts and transmits the data.

[1845] 3. Server: Information storage

[1846] The server stores the received user information in a database, which the AI ​​model can later use to learn the user's individual needs.

[1847] 4. Server: AI Learning

[1848] The server trains the AI ​​model based on the stored user information, building a personalized predictive model based on the user's commuting patterns and preferences.

[1849] Real-time traffic information collection

[1850] 5. User (Device): Enable GPS

[1851] The user launches the app after the last train and enables GPS, which then obtains the user's current location and sends that information to the server.

[1852] 6. Device: Send current location

[1853] The device sends the user's current location information to the server in real time and continuously transmits GPS data.

[1854] 7. Server: Traffic information collection

[1855] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, temporarily stores this information, and updates it as needed.

[1856] Optimal route suggestions

[1857] 8. Server: Optimal route calculation

[1858] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[1859] 9. Server: Route proposal generation

[1860] The server then generates a specific route home based on the calculation results, suggesting, for example, "walk to Shinjuku Station, take the train to Shibuya Station, and then take a taxi from Shibuya Station to Roppongi Station."

[1861] 10. Server: Send Proposal

[1862] The server then sends the generated route proposal to the device, including route details and a total cost estimate.

[1863] 11. Terminal: Route display

[1864] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1865] Real-time information updates

[1866] 12. User (device): Continuously send current location

[1867] Users keep the app open while they move around, and the app continuously obtains their location and sends updates to the server if there are any changes.

[1868] 13. Server: Traffic Condition Monitoring

[1869] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[1870] 14. Server: Route Recalculation

[1871] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[1872] 15. Server: Send Re-Proposal

[1873] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1874] 16. Terminal: Reroute Display

[1875] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1876] This allows users to find the best route home even after the last train, allowing them to get home quickly while keeping travel costs down.

[1877] The processing flow will be explained below.

[1878] Step 1:

[1879] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences using a form, and then presses the "Submit" button once the information is complete.

[1880] Step 2:

[1881] The terminal sends the entered information to the server. At this time, the data is encrypted and the information is sent using a secure communication protocol. The terminal then checks the connection with the server.

[1882] Step 3:

[1883] The server stores the received user information in a database, which is then used to train the AI ​​model.

[1884] Step 4:

[1885] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[1886] Step 5:

[1887] The user (device) restarts the app after the last train and enables the GPS function, and the app obtains the user's current location.

[1888] Step 6:

[1889] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[1890] Step 7:

[1891] The server uses a traffic information API to collect real-time information on train, bus, and taxi service status and congestion information around the user's current location. The acquired information is temporarily stored in a database.

[1892] Step 8:

[1893] The server calculates the optimal route home based on the user's current location, registered user information, and real-time traffic information, taking into account the user's preferences (e.g., wanting to save on taxi fares).

[1894] Step 9:

[1895] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[1896] Step 10:

[1897] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[1898] Step 11:

[1899] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[1900] Step 12:

[1901] The user (device) keeps the app open while moving, and the current location is continuously acquired. If there is a change, the current location information is updated and sent to the server.

[1902] Step 13:

[1903] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information. The obtained information is temporarily stored and any changes are treated as new data.

[1904] Step 14:

[1905] The server recalculates routes in real time to accommodate unexpected delays and other issues, generating new, optimal routes that take into account user preferences and current conditions.

[1906] Step 15:

[1907] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[1908] Step 16:

[1909] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[1910] Example 1

[1911] 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."

[1912] Currently, many users face difficulties in finding a way to get home after the last train. This is especially true at night, when transportation options are limited or when transportation is affected by delays or congestion. Furthermore, because proposed routes do not reflect the user's individual preferences or commuting patterns, this can be time-consuming and costly. This invention is proposed to solve these problems.

[1913] 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.

[1914] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, commuting pattern, and preferences, means for transmitting encrypted user information to the server, means for collecting real-time information on transportation facilities, means for calculating an optimal route home based on the user information and real-time traffic information, means for providing the calculated route information, and means for updating the traffic information in real time and recalculating the route as necessary. This allows the user to receive a real-time suggestion of an optimal route home according to traffic conditions, enabling them to return home quickly and efficiently.

[1915] "Means for obtaining the user's current location" refers to a means for accurately identifying the user's current location using location information technology such as GPS or Wi-Fi.

[1916] "Means for registering a user's home address, nearest station, commuting patterns, and preferences" means an interface or input method that allows a user to register their home address, nearest station, daily commuting patterns, and transportation preferences in the system.

[1917] "Means for sending encrypted user information to a server" refers to a technology that encrypts data to securely protect information entered by users and sends it to a server via the Internet.

[1918] "Means of collecting real-time information on transportation" refers to technology that uses transportation information services and APIs to obtain real-time information on the operation status and congestion of trains, buses, taxis, etc.

[1919] "Means for calculating the optimal route home based on user information and real-time traffic information" refers to an algorithm or calculation method that calculates the optimal route home, taking into account the information registered by the user and the collected current traffic conditions.

[1920] The "means for providing calculated route information" refers to an interface or output method for providing the calculated route home in a form that is easy for the user to understand.

[1921] "Means for updating traffic information in real time and recalculating routes as needed" refers to methods or algorithms that continuously update information in response to changing traffic conditions and recalculate a new route home as needed.

[1922] The present invention provides a system for assisting a user in finding the best route home after the last train. Specific embodiments of the system will be described below.

[1923] Overview and Structure

[1924] This system uses the user's current location, home address, nearest station, commuting pattern, and preference information to collect real-time traffic information and calculate and suggest the optimal route home. The main components of the system are the user's device, server, database, and traffic information provision API.

[1925] Hardware and software used

[1926] Device: The smartphone or tablet used by the user

[1927] Server: Cloud-based server (e.g., AWS, Microsoft Azure, Google Cloud)

[1928] Database: SQL or NoSQL database (e.g. MySQL, PostgreSQL, MongoDB)

[1929] Traffic information API: Real-time traffic information providers such as Google Maps API and OpenWeatherMap

[1930] Encryption technology: AES encryption

[1931] Data processing and calculation

[1932] 1. Enter and submit user information

[1933] Users launch a dedicated app on their smartphone and enter their home address, nearest train station, commuting pattern, and individual preferences.

[1934] The data entered is encrypted within the app and securely sent to the server.

[1935] 2. Server-side information processing

[1936] The server decrypts the received encrypted data and stores it in a database.

[1937] Using the stored data, a generative AI model learns users' commuting patterns and preferences, built using TensorFlow and PyTorch.

[1938] 3. Collection of real-time traffic information

[1939] When the user launches the app again and sends their current location information, the server calls the traffic information API to obtain real-time traffic conditions.

[1940] The retrieved information is temporarily stored in the Redis cache.

[1941] 4. Calculating the optimal route

[1942] The server calculates the optimal route home using Dijkstra's algorithm and A algorithm based on the user's current location, registered information, and collected real-time traffic information.

[1943] 5. Route presentation

[1944] The calculated route information is sent from the server to the terminal where it is presented to the user in visual and textual form.

[1945] This route information includes the mode of transportation (walking, train, taxi, etc.), travel time, cost estimates, etc.

[1946] 6. Real-time updates

[1947] The device continuously transmits its current location information, and the server monitors changes in traffic information.

[1948] If traffic conditions change, the server recalculates the optimal route and notifies the user of the new route.

[1949] Examples of specific examples and prompts

[1950] Examples:

[1951] User A lives in Roppongi, Minato-ku, Tokyo, and commutes from 9:00 to 18:00. He is heading home from Shinjuku Station after the last train has left. User A launches the app, enables GPS, and sends his current location to the server. The server uses the Google Maps API to obtain real-time traffic information and calculates the optimal route home, taking into account User A's commuting pattern and preferences. As a result, the server suggests the route: "Take the train from Shinjuku Station to Shibuya Station, then take a taxi from Shibuya Station to Roppongi." This route information is displayed on the app as a map and text.

[1952] Example prompt sentence:

[1953] "If my current location is Shinjuku Station and my home is Roppongi, Minato Ward, Tokyo, please suggest the best route to get home while keeping taxi fare to a minimum."

[1954] This allows users to efficiently obtain the optimal route home in real time.

[1955] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1956] Step 1:

[1957] Input: The user enters their home address, nearest train station, commuting patterns, and preferences into the app.

[1958] How it works: The user launches the app on their smartphone and fills in the designated form with their home address (e.g., Roppongi, Minato-ku, Tokyo), nearest station (e.g., Roppongi Station), commuting pattern (e.g., 9:00 AM to 6:00 PM), and preferences (e.g., want to keep taxi fares to a minimum).

[1959] Output: User information entered on the terminal is generated.

[1960] Step 2:

[1961] Input: Home address, nearest station, commuting patterns, and preference data entered by the user.

[1962] Operation: The device encrypts the collected user information and sends it to the server using AES encryption, for example, AES-256.

[1963] Output: User information data sent to the server in encrypted form.

[1964] Step 3:

[1965] Input: Encrypted user information data.

[1966] How it works: The server decrypts the encrypted data it receives and stores it in a database. The data is decrypted using the corresponding encryption key.

[1967] Output: Decrypted user information stored in the database.

[1968] Step 4:

[1969] Input: Decrypted user information.

[1970] How it works: The server uses this user information to train a generative AI model, which is built using TensorFlow or PyTorch to learn individual patterns for each user.

[1971] Output: A trained generative AI model.

[1972] Step 5:

[1973] Input: trained generative AI model, user's current location.

[1974] How it works: The user restarts the app after the last train and enables GPS, causing the device to collect the user's location.

[1975] Output: Current location information is obtained on the device.

[1976] Step 6:

[1977] Input: User's current location.

[1978] What it does: The device starts streaming continuous GPS data to send real-time location information to the server. The data is sent using WebSockets.

[1979] Output: Real-time location data sent to the server.

[1980] Step 7:

[1981] Input: User location, real-time transport data.

[1982] How it works: The server uses a traffic information API (e.g., Google Maps API) to collect real-time information about train, bus, and taxi service availability and congestion information for the user's area. This information is stored in a Redis cache.

[1983] Output: Temporarily stored real-time traffic information.

[1984] Step 8:

[1985] Input: User's current location information, registered information, real-time traffic information, trained generative AI model.

[1986] How it works: The server uses Dijkstra's algorithm and the A algorithm to calculate the optimal route home based on this information, taking into account the user's preferences.

[1987] Output: The calculated optimal route home.

[1988] Step 9:

[1989] Input: The calculated optimal route home.

[1990] How it works: The server packages this route information in JSON format and sends it to the device.

[1991] Output: Optimal route home information sent from the server to the terminal.

[1992] Step 10:

[1993] Input: The best route home information submitted.

[1994] How it works: The device analyzes the route information it receives and displays it visually and textually on the user interface, and plots the optimal route on a map.

[1995] Output: Route information displayed to the user.

[1996] Step 11:

[1997] Input: The user's continuous location.

[1998] How it works: The user keeps the app open on their device while they move around, and the device continuously sends their location to the server.

[1999] Output: The latest location information sent to the server.

[2000] Step 12:

[2001] Input: Latest traffic information, latest user location information.

[2002] Operation: The server periodically checks the traffic information API to obtain and update the latest traffic status and congestion information.

[2003] Output: Updated latest traffic information.

[2004] Step 13:

[2005] Input: Updated traffic information, user's current location, trained generative AI model.

[2006] What it does: The server recalculates the optimal route based on this information and suggests a new route home if necessary.

[2007] Output: The new recalculated optimal route.

[2008] Step 14:

[2009] Input: The new recalculated optimal route.

[2010] Operation: The server sends the recalculated route in JSON format to the device and notifies the user.

[2011] Output: The new best route information sent to the device.

[2012] Step 15:

[2013] Input: New optimal route information.

[2014] What it does: The device parses the new route information and updates the user interface, notifying the user of any changes.

[2015] Output: The new route information displayed to the user.

[2016] (Application example 1)

[2017] 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."

[2018] Current home-come-home assistance systems that use traffic information are limited to proposing optimal routes home after the last train, and are unable to reflect real-time traffic conditions or road congestion, making it difficult to return home efficiently. Furthermore, they are unable to link with autonomous vehicles, requiring users to choose their own means of transportation, resulting in low convenience. Another issue is that they are unable to provide flexible suggestions that reflect the user's individual travel history and preferences.

[2019] 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.

[2020] In this invention, the server includes a means for acquiring the user's current location, a means for registering the user's home address, nearest station, and preferences, a means for collecting real-time information on transportation, and a means for instructing the autonomous vehicle on the optimal route. This allows the user to always obtain the optimal route home that reflects real-time traffic information, and by coordinating with the autonomous vehicle, the user can return home safely and efficiently. In addition, a generative AI model is used to learn past travel history, enabling flexible route suggestions that take into account the user's preferences and patterns.

[2021] "Means for obtaining the user's current location" means a device or system that measures the user's current location using GPS or other location information technology and transmits that data to the system.

[2022] "Means for registering a user's home address, nearest station, and preferences" refers to a device or system that allows a user to enter their home address, nearest public transport station, and travel preferences (e.g., preferring to avoid taxis) into the system and store this information in a database.

[2023] "Means for collecting real-time information on transportation" refers to devices or systems that use APIs or other data collection technologies to obtain the latest operational status, delay information, and congestion information of transportation services and integrate it into the system.

[2024] The "means for calculating the optimal route home" is a device or system for calculating the most efficient and economical route home for the user based on the user's current location, registered information, and real-time traffic information.

[2025] The "means for providing calculated route information" refers to a device or system that displays the optimal route home calculated by the system on the user's terminal and presents it in a visually easy-to-understand manner.

[2026] "Means for instructing an automated vehicle on the optimal route" refers to a device or system that transmits the calculated optimal route information to the automated vehicle's control system, allowing the automated vehicle to efficiently guide the user to the destination.

[2027] "Means for transmitting user's current location information to a server" refers to a device or system for obtaining user's location information in real time and transmitting it to a server via the Internet or other network.

[2028] "Means for a server to recalculate routes based on information on delays and congestion at public transport facilities and set new routes for self-driving vehicles" refers to a device or system in which a server continuously monitors the latest information on public transport facilities, recalculates the optimal route as needed, and sends that information to self-driving vehicles to set new routes.

[2029] "Artificial intelligence for learning past travel history and understanding user preferences and patterns" refers to a device or system equipped with machine learning technology that analyzes a user's past travel data, learns the user's specific behavioral patterns and preferences, and uses that information to predict future travel.

[2030] A "means for suggesting optimal routes using generative AI models" is a device or system that uses a generative artificial intelligence model to suggest optimal routes and transportation methods that take into account a user's current situation and preferences.

[2031] This invention is a system that helps users find the best route home after the last train. This system registers the user's current location, home address, nearest station, and preferences, and collects and utilizes real-time information on transportation to suggest the best route home to the user. Furthermore, this system enables collaboration with autonomous vehicles and uses a generative AI model to suggest routes.

[2032] System Configuration

[2033] Hardware and Software Configuration

[2034] The system includes the following hardware and software:

[2035] The user's smartphone or smart glasses (with GPS and data communication functions)

[2036] Server (with high-performance database and AI processing capabilities)

[2037] Traffic information API (provides real-time traffic information)

[2038] Navigation systems for autonomous vehicles

[2039] How to get the user's location

[2040] The GPS module built into the device (smartphone or smart glasses) is used to obtain the user's current location. When the user launches the app and enables GPS, the device continues to obtain location information in real time.

[2041] User information registration method

[2042] Users use the app to input their home address, nearest station, commuting pattern, and preferences. For example, a user might enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum," and the information is then saved in a database.

[2043] A means of collecting real-time transportation information

[2044] The server uses a traffic information API to obtain real-time information on the operation status and congestion of nearby trains, buses, and taxis, allowing it to constantly maintain the latest information on transportation and provide it to users.

[2045] A way to calculate the best route home

[2046] The server calculates the optimal route home based on the user's current location, registered information, and real-time traffic information, and also takes into account the user's preferences (e.g., wanting to reduce taxi fares) and uses a generative AI model to suggest the optimal route.

[2047] A means of providing calculated route information

[2048] The server generates a specific route home based on the calculation results and sends it to the user's device, which then displays the route proposal to the user on a map and in text format.

[2049] A means of providing optimal route guidance to autonomous vehicles

[2050] The server then sends the calculated optimal route information to the autonomous vehicle's control system, and the vehicle then guides the user safely home based on that route. For example, if a route from Shinjuku Station to Roppongi Station is specified, the autonomous vehicle will use this information to select the optimal route and efficiently transport the user.

[2051] Prompt Sentence Examples

[2052] For example, if a resident of Roppongi is currently in Shinjuku, the prompt to have the generative AI model suggest a route would be:

[2053] Nearest station: Shinjuku Station

[2054] User preference: Minimize taxi fares

[2055] Current location: Shinjuku Station South Exit

[2056] Home address: Roppongi, Minato-ku, Tokyo

[2057] Please suggest the best route for the user to take home after the last train. If necessary, please consider a combination of multiple modes of transport and walking. Please also provide approximate travel costs and travel times.

[2058] Specific examples

[2059] For example, if a user misses the last train near Shinjuku Station, the system will suggest the best route home using the following steps:

[2060] 1. The device obtains its current location using GPS and sends it to the server.

[2061] 2. The server obtains real-time traffic information using the traffic information API.

[2062] 3. The server learns the user's preferences and past travel history and calculates the optimal route using a generative AI model.

[2063] 4. The server sends the calculated route to the device and displays it to the user on a map and in text format.

[2064] 5. The server instructs the autonomous vehicle on the optimal route, and the vehicle guides the user safely home.

[2065] This system allows users to return home safely and efficiently, even after the last train. For example, it can suggest a route that allows users to walk from Shinjuku Station to a nearby self-driving vehicle and go directly to their home.

[2066] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2067] Step 1:

[2068] Registering user information

[2069] Users input their home address, nearest train station, commuting patterns, and preferences into their device (smartphone or smart glasses).

[2070] The terminal encrypts the input information and transmits it to the server.

[2071] The server stores the received user information in a database.

[2072] input:

[2073] Your home address, nearest train station, commuting patterns, and preferences

[2074] Data processing and calculation:

[2075] Encrypts user-entered information

[2076] output:

[2077] Encrypted user information

[2078] Specific behavior:

[2079] Enter information through the device's user interface (UI)

[2080] Secure data transmission using HTTPS protocol

[2081] Server-side data decoding and storage

[2082] Step 2:

[2083] Get and send your current location

[2084] The user launches the app and enables GPS.

[2085] The terminal obtains its current location using a GPS module and transmits that information to the server.

[2086] input:

[2087] Location data from the GPS module

[2088] Data processing and calculation:

[2089] Obtaining current location data and format conversion (longitude and latitude)

[2090] output:

[2091] Location information sent to the server

[2092] Specific behavior:

[2093] Enable the GPS function on your device and periodically update your location.

[2094] Send location information to the server in real time

[2095] Step 3:

[2096] Real-time traffic information collection

[2097] The server obtains real-time traffic information through a traffic information provision API.

[2098] The acquired traffic information is temporarily stored in a database.

[2099] input:

[2100] Traffic information API request

[2101] Data processing and calculation:

[2102] Parsing and saving data retrieved from the API

[2103] output:

[2104] Real-time traffic information

[2105] Specific behavior:

[2106] The server periodically calls the API to retrieve data.

[2107] Analyze JSON format data and save the necessary information to a database

[2108] Step 4:

[2109] Optimal Route Calculation

[2110] The server calculates the optimal route home based on the user's current location, registration information, and real-time traffic information.

[2111] Generate route suggestions using a generative AI model.

[2112] input:

[2113] User information, current location, real-time traffic information

[2114] Data processing and calculation:

[2115] Integrates multiple input data and executes route calculation algorithms

[2116] Uses generative AI models to calculate optimal routes

[2117] output:

[2118] Proposing the best route home

[2119] Specific behavior:

[2120] Algorithms on the server analyze the data and calculate the shortest time and lowest cost route.

[2121] Generate route suggestions taking into account user preferences

[2122] Step 5:

[2123] Providing route information

[2124] The server generates a specific route home based on the calculation results and sends it to the user's device.

[2125] The device displays the received route information to the user in map and text format.

[2126] input:

[2127] Data suggesting the best route home

[2128] Data processing and calculation:

[2129] Route data format conversion (map data, text data)

[2130] output:

[2131] Route information displayed on the user's device

[2132] Specific behavior:

[2133] The server sends route proposal data to the terminal

[2134] Display data received by the device as a map and text

[2135] Step 6:

[2136] Commanding autonomous vehicles

[2137] The server sends the optimal route information to the autonomous vehicle's control system, which guides the vehicle to the user.

[2138] The autonomous vehicle will drive users to their destination based on the optimal route.

[2139] input:

[2140] Data suggesting the best route home

[2141] Data processing and calculation:

[2142] Converting route data into a format suitable for the navigation system of an autonomous vehicle

[2143] output:

[2144] Route instructions for autonomous vehicles

[2145] Specific behavior:

[2146] The server sends the route information to the vehicle's control system

[2147] The vehicle's navigation system sets the route and begins operation.

[2148] Step 7:

[2149] Real-time recalculation and notifications

[2150] The server continuously monitors traffic information and recalculates the optimal route when new information becomes available.

[2151] If a route change is necessary, the device and the autonomous vehicle will be notified.

[2152] input:

[2153] Continuously updated traffic information

[2154] Data processing and calculation:

[2155] Route recalculation based on real-time information

[2156] output:

[2157] Notification of updated route information

[2158] Specific behavior:

[2159] The server periodically calls the traffic API to obtain the latest information.

[2160] Recalculate the route and notify the device and vehicle if necessary

[2161] In this way, through a series of steps, the present invention can provide users with the optimal route home in real time and, in cooperation with autonomous vehicles, support a safe and efficient journey home.

[2162] 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.

[2163] The present invention relates to a system that helps users find the best route home after the last train. The system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and suggests the best route home. Furthermore, by combining this system with an emotion engine, the system can recognize the user's emotional state and reflect it in the suggested route home. Specific processing details and examples of the system are described below.

[2164] Registering user information

[2165] 1. User (device): Enter information

[2166] Users launch the app and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "Commuting pattern: 9:00 AM to 6:00 PM," and "I want to keep taxi fares to a minimum."

[2167] 2. Terminal: Information transmission

[2168] The device sends the entered user information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[2169] 3. Server: Information storage

[2170] The server stores the received user information in a database, which is then used to train the AI ​​model.

[2171] 4. Server: AI Learning

[2172] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[2173] Real-time traffic information collection

[2174] 5. User (Device): Enable GPS

[2175] When the user restarts the app after the last train and enables the GPS function, the app obtains the user's current location and sends that information to the server.

[2176] 6. Device: Send current location

[2177] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[2178] 7. Server: Traffic information collection

[2179] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[2180] Recognizing and recording user emotions

[2181] 8. Terminal: Emotional Data Collection

[2182] The device uses microphones, cameras, sensors, etc. to acquire data such as the user's voice, facial expressions, and heart rate, and sends this data to the emotion engine.

[2183] 9. Sentiment Engine: Sentiment Analysis

[2184] The emotion engine analyzes the user's emotional state based on the acquired data, and the analysis results are sent to the server as, for example, "stressed" or "relaxed."

[2185] Optimal route suggestions

[2186] 10. Server: Optimal route calculation

[2187] The server calculates the optimal route home based on the user's current location information, registered user information, real-time traffic information, and the analysis results of the emotion engine.

[2188] 11. Server: Route proposal generation

[2189] The server then generates a specific route home based on the calculation results. For example, it might suggest "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." If the stress level during the stay is high, it would suggest a more relaxing way home.

[2190] 12. Server: Send Proposal

[2191] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[2192] 13. Terminal: Route display

[2193] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[2194] Real-time information updates

[2195] 14. User (device): Continuously send current location

[2196] Users keep the app open while they move around, and it continually obtains their location and sends updates to the server if there are any changes.

[2197] 15. Server: Traffic Condition Monitoring

[2198] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[2199] 16. Server: Route Recalculation

[2200] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[2201] 17. Server: Send Re-Proposal

[2202] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[2203] 18. Terminal: Reroute Display

[2204] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[2205] This not only enables users to find the best route home even after the last train, but also allows them to receive suggestions that take their emotional state into account, making for a less stressful journey home.

[2206] The processing flow will be explained below.

[2207] Step 1:

[2208] The user (device) launches the app and enters their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[2209] Step 2:

[2210] The device sends the entered information to the server, where the data is encrypted and transmitted using a secure communication protocol.

[2211] Step 3:

[2212] The server stores the received user information in a database, which is then used to train the AI ​​model.

[2213] Step 4:

[2214] The server trains an AI model based on the stored user information, allowing it to make predictions based on each individual user's commuting patterns and preferences.

[2215] Step 5:

[2216] The user (device) restarts the app after the last train and enables the GPS function, which then acquires the user's current location.

[2217] Step 6:

[2218] The device sends its current location information to the server in real time, ensuring that the user's location information is always up to date.

[2219] Step 7:

[2220] The server uses a traffic information API to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis, and temporarily stores this information in a database.

[2221] Step 8:

[2222] The device uses a microphone, camera, sensors, etc. to obtain emotional data from the user. For example, it can assess stress levels based on the user's tone of voice and measure relaxation levels using a heart rate sensor.

[2223] Step 9:

[2224] The emotion engine analyzes the acquired emotion data and determines the user's emotional state. The result is classified as "stressed" or "relaxed" and sent to the server.

[2225] Step 10:

[2226] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. For example, if a user is feeling stressed, it will prioritize a route with less congestion.

[2227] Step 11:

[2228] The server generates a specific route home based on the calculation results, such as "walk to Shinjuku Station, take the train to Shibuya Station, and take a taxi from Shibuya Station to Roppongi Station."

[2229] Step 12:

[2230] The server then sends the generated route proposal to the device, which includes detailed route information and a total cost estimate.

[2231] Step 13:

[2232] The device then displays the received optimal route suggestions to the user, visually displaying the proposed route on a map and providing detailed text descriptions.

[2233] Step 14:

[2234] The user (device) keeps the app open while moving, and the app continuously acquires the user's location. If there are any changes, the information is updated and sent to the server.

[2235] Step 15:

[2236] The server periodically calls the traffic information API to obtain the latest traffic status and congestion information, and updates the information as needed.

[2237] Step 16:

[2238] The server recalculates the route in real time to accommodate unexpected delays and other issues, and even takes the user's emotional state into account.

[2239] Step 17:

[2240] The server then sends the new route proposal to the device, and if necessary, suggests changing the mode of transportation, including taxis.

[2241] Step 18:

[2242] The device will promptly display new route suggestions to the user, and will update the map and text information to notify the user of any changes.

[2243] Through the above processing steps, the system can find the optimal route home after the last train, allowing the user to have a stress-free journey home that takes into account their emotional state.

[2244] Example 2

[2245] 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."

[2246] It is difficult for users to find the optimal route home after the last train, especially when considering transportation delays, congestion, and the user's emotional state. As a result, users often spend a long time finding a route home that is less stressful. This can reduce user satisfaction and lead to further stress on the way home. Therefore, there is a need for a system that can quickly suggest the optimal route home that reflects traffic information and the user's emotional state in real time.

[2247] 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.

[2248] In this invention, the server includes means for acquiring the user's current location, means for registering the user's home address, nearest station, and preferences, means for collecting real-time information on transportation facilities, means for analyzing the user's emotional state, means for calculating an optimal route home based on the above information and the results of the emotional state analysis, and means for providing the calculated route information, thereby enabling the user to quickly find an optimal route home taking into account real-time updated traffic information and the user's emotional state.

[2249] "Means for obtaining the user's current location" refers to technologies or methods that use the GPS function or location information services of the device used by the user to identify and obtain the user's current geographical location.

[2250] "Means for registering a user's home address, nearest station, and preferences" refers to the technology or method by which a user can input their home address, nearest station, and various personal preferences (such as wanting to take a taxi) through an application or terminal and store them in a database.

[2251] "Means of collecting real-time information on transportation" refers to technologies and methods that use external transportation information services and APIs to obtain the latest information on the operation status and congestion levels of trains, buses, taxis, etc.

[2252] "Means for analyzing the user's emotional state" refers to technologies and methods that collect data such as the user's voice, facial expressions, and heart rate, and then use artificial intelligence and analytical algorithms to classify the user's emotions into "stressed state," "relaxed state," etc.

[2253] "Means for calculating the optimal route home" refers to technology or methods that use a generative AI model to generate the optimal route home based on the user's current location information, registration information, real-time traffic information, and emotion analysis results.

[2254] "Means for providing calculated route information" refers to a technology or method for sending calculated information on the optimal route home to the user's device and presenting it as a map display or text description.

[2255] "Registered user information" refers to information such as home address, nearest station, and preferences that the user enters into the application or device.

[2256] An "emotion engine" refers to an artificial intelligence or analytical algorithm system that analyzes collected emotional data to identify the user's emotional state.

[2257] "Transportation Information API" refers to an application programming interface for accessing and obtaining information from external services that provide information on transportation operation status and congestion levels.

[2258] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to learn user behavior and patterns from various data and make predictions and suggestions.

[2259] This invention provides a system that allows users to find the best route home after the last train, and in particular, reflects traffic information and the user's emotional state in real time to support a more comfortable journey home.

[2260] This system collects real-time traffic information based on the user's current location, home address, nearest station, and preferences, and proposes the optimal route home. It can also analyze the user's emotional state and reflect the results in the route home. A specific embodiment of the system is described below.

[2261] Registering user information

[2262] Users launch an application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences. For example, they enter information such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum." This information is sent from the device to the server and securely stored in a database. The server uses a generative AI model based on the stored user information to perform learning. Examples of machine learning models used here include TensorFlow and PyTorch.

[2263] Real-time traffic information collection

[2264] The user restarts the application after the last train and enables the GPS function. The device obtains the user's current location and sends it to the server in real time. The server then calls a traffic information API (e.g., Google Maps API or OpenWeatherMap API) to collect real-time information on the operation status and congestion of nearby trains, buses, and taxis. This information is temporarily stored in a database.

[2265] Recognizing and recording user emotions

[2266] The device uses a microphone, camera, and sensors to capture data such as the user's voice, facial expressions, and heart rate. This data is sent to an emotion engine (e.g., Amazon Rekognition or IBM Watson), which analyzes the user's emotional state into categories such as "stressed" or "relaxed." The analysis results are sent to a server and used to calculate the route home.

[2267] Optimal route suggestions

[2268] The server calculates the optimal route home based on the user's current location, registered user information, real-time traffic information, and the analysis results of the emotion engine. Using a generative AI model, it generates the optimal route while taking multiple factors into account. For example, it could suggest routes such as "walk to Shinjuku Station, take the train to Shibuya Station, then take a taxi from Shibuya Station to Roppongi Station." The server sends the calculation results to the device, which then presents them to the user as a map display or text explanation.

[2269] Real-time information updates

[2270] By keeping the app open while the user is traveling, the current location information is continuously sent to the server. The server periodically calls the traffic information API to obtain the latest service status and congestion information. If an unexpected delay or other problem occurs, the server recalculates the route in real time, reanalyzes the user's emotional state, and sends a new route suggestion to the device. The device promptly displays the recalculated route to the user and updates the map and text information if there are any changes.

[2271] Examples of specific examples and prompts

[2272] For example, if a user in Roppongi searches for a route home after the last train, the system will suggest options such as "walk to Shinjuku Station, take the train to Shibuya Station, or take a taxi from Shibuya Station to Roppongi Station." It will suggest the optimal way to get home by taking into account traffic conditions, congestion information, and the user's emotional state.

[2273] Prompt Sentence Examples

[2274] 1. "If my current location is Roppongi, the nearest station is Roppongi Station, and my home is Shibuya, what is the best route home after the last train?"

[2275] 2. "If the user is currently in a stressful situation, please suggest a relaxing way to get home."

[2276] 3. "Calculate the best route home with real-time traffic updates."

[2277] This allows users to quickly find the best route home based on traffic information and their emotional state.

[2278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2279] Step 1:

[2280] User: Enter information

[2281] Users launch the application on their smartphone or PC and enter their home address, nearest station, commuting pattern, and preferences, such as "Roppongi, Minato-ku, Tokyo," "Roppongi Station," "commuting pattern: 9:00 AM to 6:00 PM," and "want to keep taxi fares to a minimum."

[2282] Input: Home address, nearest station, commuting pattern, preferences

[2283] Output: User information data

[2284] Step 2:

[2285] Terminal: Sending information

[2286] The device encrypts the information entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS).

[2287] Input: User information data

[2288] Output: User information sent to the server

[2289] Step 3:

[2290] Server: Information storage

[2291] The server stores the received user information in a database, which is then used to train and predict generative AI models.

[2292] Input: User information data

[2293] Output: User information stored in the database

[2294] Step 4:

[2295] Server: AI learning

[2296] The server uses the stored user information to train a generative AI model, which then makes predictions tailored to each individual user.

[2297] Input: User information stored in the database

[2298] Output: Trained generative AI model

[2299] Step 5:

[2300] User:GPS Enabled

[2301] The user restarts the application after the last train and enables the GPS function, which allows the app to automatically obtain the user's current location.

[2302] Input: None

[2303] Output: User's current location

[2304] Step 6:

[2305] Device: Send current location

[2306] The device transmits the acquired location information to the server in real time, where it is re-encrypted and transmitted securely.

[2307] Input: User's current location

[2308] Output: Location information sent to the server

[2309] Step 7:

[2310] Server: Traffic information collection

[2311] The server calls the traffic information API to collect real-time operational status and congestion information for trains, buses, taxis, etc. This information is temporarily stored in a database.

[2312] Inpu...

Claims

1. A means of obtaining the user's current location; A means for registering the user's home address, nearest station, and preferences; a means of collecting real-time transport information; means for calculating an optimal route home based on the information; means for providing calculated route information; A system including:

2. means for transmitting user's current location information to a server; The server recalculates routes based on information about delays and congestion on public transport and notifies the user. The system of claim 1 further comprising:

3. 10. The system of claim 1, including artificial intelligence for learning a user's past travel history and understanding user preferences and patterns.

4. The system according to claim 1, further comprising means for estimating travel costs in the calculated route information and presenting the estimated travel costs to the user.

5. 2. The system according to claim 1, further comprising means for aggregating and temporarily storing operation information of a plurality of transportation facilities.

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