system

Abstract

A system is provided.SOLUTION: The system includes a means for collecting real-time data, a means for preprocessing the collected data, a means for analyzing the preprocessed data and calculating an optimum route, a means for providing a calculation result to a user terminal, and a means for providing a public API for receiving a request from the outside and returning the optimum route.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] Even after ride-sharing services were legalized, rural and depopulated areas continue to face challenges such as limited transportation access, shrinking public transportation options, and a declining number of taxi drivers. Efficient vehicle dispatch systems are essential, particularly for elderly people and those with limited transportation options. However, conventional vehicle dispatch systems and transportation information systems lack the ability to forecast demand and understand traffic conditions in real time, making it difficult to efficiently dispatch vehicles and propose routes. Therefore, this invention aims to utilize real-time data and AI analysis to enable the provision of efficient, demand-based ride-sharing services and provide sustainable transportation options. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including the following means: a means for collecting real-time data, a means for preprocessing the collected data, a means for analyzing the preprocessed data and calculating an optimal route, a means for providing the calculation results to a user terminal, and a means for providing a public API that accepts requests from outside and returns the optimal route. This system calculates the optimal route using an artificial intelligence model based on the collected data, which includes GPS data, map information, aerial photographs, congestion information on nearby facilities, and information on nearby events. The preprocessing means also performs data cleaning, normalization, and feature extraction, and the user terminal is a smartphone app. As a result, transportation convenience in rural and depopulated areas can be improved, providing a sustainable means of transportation.

[0006] "Real-time data" means data that is collected in real time and is available near-instantaneously.

[0007] "Collection means" is a general term for devices, sensors, and software used to collect various types of data.

[0008] "Preprocessing means" refers to the process and its implementation for converting collected data into an analyzable format.

[0009] An "analytical tool" is a method or device that uses pre-processed data to calculate indicators or results for a specific purpose.

[0010] An "optimal route" is the most efficient or effective route under specific conditions.

[0011] A "user terminal" is a device used by a user (for example, a smartphone).

[0012] A "public API" is an application program interface that is made publicly available for use by external systems and applications.

[0013] An "artificial intelligence model" is a general term for algorithms that are trained to solve specific problems using machine learning and data analysis.

[0014] "Data cleaning" is the process of removing noise and outliers from a dataset and improving the quality of the data.

[0015] "Normalization" is the process of aligning data of different scales to a consistent standard.

[0016] "Feature extraction" is the process of extracting features from data that are useful for analysis. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] This invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI. The following describes an embodiment of the system and the processing of its program in natural language.

[0039] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0040] server

[0041] 1. Data Collection

[0042] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time, ensuring that the latest information is always available.

[0043] 2. Data Preprocessing

[0044] The server stores the collected data in a database and performs preprocessing such as data cleaning, normalization, and feature extraction. This preprocessing improves the quality of the data and makes it suitable for analysis.

[0045] 3. AI analysis

[0046] Based on the pre-processed data, the server calculates the optimal route using an AI model that takes into account various traffic conditions, congestion, event information, etc. to predict the most efficient route for the user.

[0047] 4. API provided

[0048] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other Mobility as a Service (MaaS) systems.

[0049] Terminal

[0050] 1. User Request

[0051] The device receives requests from users through a smartphone app and sends their current location and destination to the server.

[0052] 2. Route display

[0053] The device receives the optimal route information from the server and displays it to the user, allowing the user to efficiently reach their destination.

[0054] User

[0055] 1. Submit a request

[0056] The user uses the device to request a ride-sharing service by inputting their current location and destination, and the system searches for the optimal route.

[0057] 2. Route confirmation and movement

[0058] Users can travel by following the optimal route displayed on the device, which can save time and reduce costs.

[0059] Specific examples

[0060] A user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0061] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app.

[0062] 2. The device sends this information to the server.

[0063] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0064] 4. The calculated route information is sent back from the server to the device.

[0065] 5. The device displays the optimal route to the user, who then follows the route to their destination, the shopping mall.

[0066] In this way, the system of the present invention provides users with a fast and efficient means of transportation, complements the lack of transportation in rural and depopulated areas, and enables the provision of sustainable transportation.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time from various sensors and databases. This data collection is performed automatically at regular intervals, and is set to always obtain the latest information.

[0070] Step 2:

[0071] The server stores the collected data in a database, which is used for subsequent preprocessing.

[0072] Step 3:

[0073] The server preprocesses the stored data, cleaning it to remove incomplete data and noise, normalizing it to convert the scale of the data into a consistent format, and extracting the features required for analysis to prepare it for efficient analysis by the AI ​​model.

[0074] Step 4:

[0075] The server uses an artificial intelligence model to analyze the preprocessed data. Specifically, it calculates the optimal route by taking into account traffic conditions, congestion information, event information, etc. The AI ​​model predicts supply and demand based on past and current data, and calculates the optimal travel route for the user.

[0076] Step 5:

[0077] The server provides the optimal route information, which is the result of the analysis, via a public API. In response to requests from external systems and applications, the server responds with the optimal route information in real time. Using this API, it is possible to link with other MaaS systems and third-party applications.

[0078] Step 6:

[0079] The device receives requests from users via a smartphone app and sends information about their current location and destination to the server.

[0080] Step 7:

[0081] The server recalculates the optimal route based on the current location and destination information received from the device and returns the analysis results to the device.

[0082] Step 8:

[0083] The terminal displays the optimal route information received from the server to the user, thereby enabling the user to travel efficiently.

[0084] Step 9:

[0085] The user heads to the destination by following the optimal route displayed on the terminal, thereby quickly reaching the destination.

[0086] Example 1

[0087] 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."

[0088] Conventional ride-sharing services face challenges in finding the optimal route to reach a destination efficiently and quickly. In particular, it is difficult to optimize routes that take into account real-time changes in traffic conditions and event information, which can lead to lower user satisfaction. Furthermore, due to the lack of adequate transportation options in rural and depopulated areas, there is a need for sustainable transportation services.

[0089] 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.

[0090] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for transmitting current location and destination information to the server based on a user request, and means for displaying the optimal route information received from the server on the user terminal. This allows users to obtain optimal route information that is updated in real time and reach their destination efficiently and quickly. Furthermore, it is possible to provide appropriate transportation options even in rural and depopulated areas, thereby realizing sustainable transportation services.

[0091] "Real-time data" refers to data that indicates the current situation or state, and is available at approximately the same time as it is collected.

[0092] "Preprocessing" is the process of preparing collected data in a form that is easier to analyze, and includes processes such as cleaning, normalization, and feature extraction.

[0093] An "optimal route" is a route that allows a user to reach a destination most efficiently under certain conditions, and is calculated taking into account time, cost, traffic conditions, and the like.

[0094] A "public API" is a published interface available to external systems and applications, providing a means to access specific functionality or data.

[0095] A "user terminal" refers to an electronic device used by a user, including a smartphone, tablet, etc.

[0096] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions and analyses for specific tasks.

[0097] A "prompt" is a piece of text or command that is input to a generative AI model and serves as instructions for the AI ​​to generate output.

[0098] "GPS Data" means geographical location information data obtained using the Global Positioning System.

[0099] "Map information" is data that includes geographical information such as topography, roads, buildings, and facilities, and is used for navigation and location-based services.

[0100] "Aerial photography" refers to images of the Earth's surface taken from aircraft or satellites and is used for advanced geographic analysis and mapping.

[0101] "Congestion information for surrounding facilities" is data that indicates the current congestion situation at a specific facility or area, and is information that affects the movement and behavior of users.

[0102] "Local event information" is data relating to events taking place in a specific area, and includes information such as time, location, and content.

[0103] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and generative AI models. An embodiment of the system and the processing of its program are described below.

[0104] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and public API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0105] server

[0106] The server has the following functions:

[0107] 1. Data Collection

[0108] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information about nearby events in real time. A general online map API (e.g., Google Maps API) is used for map information. Event information and congestion status are also acquired by linking with various sensors and online databases.

[0109] 2. Data Preprocessing

[0110] The server stores the collected data in a database and performs cleaning (removing outliers), normalization (standardizing the data format), and feature extraction (extracting important elements), improving the quality of the data and making it suitable for analysis.

[0111] 3. AI analysis

[0112] Based on the preprocessed data, the server uses a generative AI model to calculate the optimal route. This AI model inputs complex data such as traffic information, congestion status, and event information to predict the most efficient route for the user. The generative AI model uses libraries such as PyTorch and TensorFlow.

[0113] 4. API provided

[0114] The server accepts requests from external systems and applications through a public API and responds with the optimal route, making the system compatible with other Mobility as a Service (MaaS) systems.

[0115] Terminal

[0116] The terminal has the following features:

[0117] 1. User Request

[0118] The device receives requests from users and sends their current location and destination to the server. The request is made through a smartphone app. The device can automatically obtain location information using GPS, eliminating the need for users to enter their location information.

[0119] 2. Route display

[0120] The device interprets the optimal route information received from the server and displays it to the user. The device provides the information as an easy-to-understand map display and uses a large screen and voice guidance to guide the user to the optimal route.

[0121] User

[0122] The user performs the following activities:

[0123] 1. Submit a request

[0124] Users use the device to request a ride-sharing service by inputting their current location and destination and searching for the best route. Requests are made through a simple user interface, and can be made either by voice or manual input.

[0125] 2. Route confirmation and movement

[0126] Users can travel by following the optimal route displayed on their device, which saves time and money, and also helps avoid problems such as traffic congestion.

[0127] Specific examples

[0128] The following example illustrates a scenario where a user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0129] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app. The user can use voice input or manual input.

[0130] 2. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0131] 3. The server uses a generative AI model to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0132] 4. The calculated route information is sent back from the server to the device.

[0133] 5. The device displays the optimal route to the user, who then follows that route to their destination, the shopping mall.

[0134] An example of a prompt sentence to be input into the generative AI model is, "I'm currently at home and my destination is a nearby shopping mall. Please tell me the best route."

[0135] In this way, the system of the present invention not only provides users with a fast and efficient means of transportation, but also complements the lack of transportation in rural and depopulated areas, thereby realizing the provision of sustainable transportation services.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1: Data collection

[0138] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time.

[0139] Specific behavior:

[0140] The server periodically acquires the vehicle's GPS data and stores the location information in a database.

[0141] Map information is obtained using online map APIs, such as Google Maps API to obtain road information and terrain data.

[0142] Aerial photographs are downloaded from a satellite image database to maintain up-to-date surface information.

[0143] Congestion information at nearby facilities is obtained by synchronizing with sensor data provided by each facility and online databases.

[0144] Information about surrounding events is collected regularly from social networks and event management systems.

[0145] Input: Various data sources (GPS system, map API, satellite image database, facility sensor, SNS)

[0146] Output: A database of collected data

[0147] Step 2: Data Preprocessing

[0148] The server stores the collected data in a database and performs data cleaning, normalization, and feature extraction.

[0149] Specific behavior:

[0150] The server uses scripts to clean the data stored in the database and fill in any outliers or missing parts.

[0151] Normalization processing unifies data formats and ensures consistency.

[0152] Important features such as traffic signals, road width, and congestion status are extracted to generate a dataset suitable for AI analysis.

[0153] Input: Raw data collected

[0154] Output: Preprocessed dataset

[0155] Step 3: AI analysis

[0156] The server uses the pre-processed data to calculate the optimal route using a generative AI model.

[0157] Specific behavior:

[0158] The server inputs the preprocessed data into a generative AI model (e.g., PyTorch, TensorFlow) and executes the model.

[0159] Based on the input data, the generative AI model calculates the expected travel time and cost for each route and predicts the most efficient route for the user.

[0160] The model results are saved in a database and prepared for API provision.

[0161] Input: Preprocessed dataset

[0162] Output: Optimal route information (estimated travel time and cost)

[0163] Step 4: Providing API

[0164] The server provides optimal route information in response to requests from external systems and applications.

[0165] Specific behavior:

[0166] The server analyzes the API request and searches for the optimal route based on the requested current location and destination information.

[0167] The optimal route information found is returned to external systems and applications.

[0168] Input: Requests from external systems and applications

[0169] Output: Optimal route information (API response)

[0170] Step 5: User Request

[0171] The terminal receives a request from the user and sends the current location and destination to the server.

[0172] Specific behavior:

[0173] The device automatically obtains the user's current location information using the GPS function.

[0174] The user enters their destination into the device and taps the send request button.

[0175] The device sends the current location and destination information to the server.

[0176] Input: User's current location and destination information

[0177] Output: Request sent to server

[0178] Step 6: View Route

[0179] The terminal displays the optimal route information received from the server to the user.

[0180] Specific behavior:

[0181] The terminal interprets the optimum route information received from the server and displays it in an easy-to-understand manner on the user interface.

[0182] The device provides voice guidance and visual map displays to help users navigate to their destinations.

[0183] Input: Optimal route information from the server

[0184] Output: Show route to user

[0185] Step 7: Move and update in real time

[0186] Users follow the optimal route displayed on the device, and information is updated in real time while they are traveling.

[0187] Specific behavior:

[0188] The device periodically communicates with the server to obtain real-time traffic and event information.

[0189] If the route changes, the terminal notifies the user and recalculates and displays the optimal route.

[0190] Input: Real-time traffic and event information

[0191] Output: Constantly updated optimal route information

[0192] (Application example 1)

[0193] 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."

[0194] Currently, parcel delivery management at logistics centers is often inefficient because delivery priority and traffic conditions are not fully considered. This results in delays in delivery time and increased costs, which is a problem. These issues are particularly serious in rural and depopulated areas where transportation options are limited.

[0195] 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.

[0196] In this invention, the server includes means for collecting real-time data, means for preprocessing the data, means for analyzing the preprocessed data using an AI model to calculate an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for managing package deliveries at the logistics center, and means for providing a delivery route that takes into account local traffic information and the priority of delivery destinations. This significantly improves package delivery efficiency at the logistics center, enabling shorter delivery times and reduced costs.

[0197] "Real-time data" refers to up-to-date information that reflects ongoing events and situations.

[0198] "Data preprocessing" refers to the process of preparing collected data in a form suitable for analysis through processes such as cleaning, normalization, and feature extraction.

[0199] An "artificial intelligence model" refers to a program that is trained by machine learning algorithms to automatically perform a specific task (in this case, calculating the optimal route).

[0200] An "optimal route" refers to the route that most efficiently reaches a destination under certain conditions.

[0201] "User terminal" refers to a device (e.g., smartphone, tablet) that a user operates directly to use a service.

[0202] A "public API" refers to a programmatic interface that is accessible to external systems and applications.

[0203] A "logistics center" refers to a facility that carries out logistics operations such as consolidating, storing, and shipping cargo.

[0204] "Delivery management" refers to the process of efficiently planning and executing a series of tasks from package receipt to final delivery.

[0205] "Traffic information" refers to information that affects traffic flow, such as road congestion, traffic accidents, and construction work.

[0206] "Delivery destination priority" refers to an indicator that indicates the urgency or importance of the delivery of the package.

[0207] "Collection means" refers to methods and devices for collecting data through various sensors, the Internet, etc.

[0208] "Providing means" refers to a method or device for providing information to a user terminal or other system.

[0209] This invention is a system for improving the efficiency of package delivery in a logistics center. The system is composed of three main components: a server, a terminal, and a user, and will be described in detail below.

[0210] server

[0211] 1. Real-time data collection methods

[0212] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and delivery priority information in real time, ensuring that the latest information needed to calculate delivery routes is always available.

[0213] 2. Data preprocessing methods

[0214] The server stores the collected data in a database and performs preprocessing such as cleaning, normalization, and feature extraction, which improves the quality of the data and makes it suitable for analysis.

[0215] 3. Analysis methods using artificial intelligence models

[0216] Based on the preprocessed data, the server uses an artificial intelligence model (e.g., a model using TensorFlow or PyTorch) to calculate the optimal route. This AI model comprehensively evaluates traffic information, congestion status, delivery destination priority, etc. to calculate the most efficient delivery route.

[0217] 4. Public API Provision Method

[0218] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other logistics systems.

[0219] Terminal

[0220] 1. User request reception method

[0221] The device receives requests from delivery drivers via a smartphone app and sends the current location and delivery destination information to the server.

[0222] 2. Route display method

[0223] The terminal receives the optimal route information received from the server and displays it to the delivery driver, enabling efficient delivery.

[0224] User

[0225] 1. Request sending method

[0226] Delivery drivers use the device to enter their current location and delivery destination information and request the optimal route.

[0227] 2. Route confirmation and delivery

[0228] Delivery drivers follow the optimal route displayed on the terminal, which shortens delivery times and reduces costs.

[0229] Specific examples

[0230] For example, if a driver delivers packages from their current location (Tokyo Station) to multiple destinations (Shibuya Station, Shinjuku Station), the following steps are taken:

[0231] 1. The driver enters their current location and delivery destination into a smartphone app.

[0232] 2. The device sends this information to the server.

[0233] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, delivery priority, etc.

[0234] 4. The calculated route information is sent back from the server to the terminal and displayed to the driver.

[0235] 5. The driver follows the route to the desired delivery location.

[0236] Prompt Sentence Examples

[0237] For example, use the following prompt:

[0238] "Based on the current location (Tokyo Station) and multiple delivery destinations (Shibuya Station, Shinjuku Station), please use AI to calculate the optimal delivery route taking into account traffic information and congestion."

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] server

[0242] Real-time data collection

[0243] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and package delivery priority information in real time. Data from each sensor and the Internet is used as input. This data is sent to the server and stored in a database. The output is raw data stored in the database.

[0244] Step 2:

[0245] server

[0246] Data Preprocessing

[0247] The server performs preprocessing on the collected data, including cleaning, normalization, and feature extraction. The raw data collected in step 1 is used as input. The data is processed to remove imperfections and make it consistent. The output is preprocessed data that has been organized into a form suitable for analysis.

[0248] Step 3:

[0249] server

[0250] Analysis using artificial intelligence models

[0251] The server uses the preprocessed data to calculate the optimal delivery route using an artificial intelligence model (e.g., a model using TensorFlow or PyTorch). The preprocessed data is used as input. The AI ​​model comprehensively evaluates delivery priority, real-time traffic information, congestion status, etc., and calculates the most efficient route. The output is optimal route information.

[0252] Step 4:

[0253] server

[0254] Route information provided via public API

[0255] The server provides the optimal route information to the terminal through a public API. The optimal route information calculated in step 3 is used as input. The server receives an API request and returns the corresponding optimal route information. The output is a response to the API request with the optimal route information.

[0256] Step 5:

[0257] Terminal

[0258] Submitting a User Request

[0259] The terminal receives a request from the delivery driver and sends the current location and delivery destination information to the server. The current location and delivery destination information entered by the driver on the smartphone app are used as input. The request is then sent to the server. The output is the request data sent to the server.

[0260] Step 6:

[0261] Terminal

[0262] View route information

[0263] The terminal displays the optimal route information received from the server to the driver. The optimal route information from the server is used as input. A process is performed to visually display the route information. The output is the route information that can be visually viewed by the driver.

[0264] Step 7:

[0265] User

[0266] Entering a request

[0267] The delivery driver uses a smartphone app to input their current location and delivery destination information and request the optimal route. As input, they manually enter their current location and delivery destination information into the app. The request is processed through the app. The output is the request data entered into the terminal.

[0268] Step 8:

[0269] User

[0270] Route confirmation and delivery

[0271] The delivery driver makes the delivery by following the optimal route displayed on the smartphone app. The optimal route information displayed on the terminal is used as input. The driver follows the route to the destination. The output is the delivery completed.

[0272] 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.

[0273] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI analysis, and also to a system that combines an emotion engine that recognizes user emotions. An embodiment of the system and the processing of its program are explained below in natural language.

[0274] server

[0275] 1. Data Collection

[0276] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals, ensuring that the latest information is always available.

[0277] 2. Data Preprocessing

[0278] The server stores the collected data in a database and pre-processes it. Data cleaning removes incomplete data and noise, and normalization converts the scale of the data into a consistent format. Emotion data is also pre-processed in the same way to make it suitable for analysis.

[0279] 3. AI analysis

[0280] Based on the pre-processed data, the server calculates the optimal route using an artificial intelligence model that takes into account traffic conditions, congestion information, and event information, and also uses emotion data provided by an emotion engine to predict the most comfortable route for the user.

[0281] 4. API provided

[0282] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0283] Terminal

[0284] 1. User Request

[0285] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. Requests are typically made through smartphone apps.

[0286] 2. Route display

[0287] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[0288] User

[0289] 1. Submit a request

[0290] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[0291] 2. Route confirmation and movement

[0292] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0293] Specific examples

[0294] Consider a scenario where a user uses a smartphone app to book a ride from their home (current location) to their workplace (destination):

[0295] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[0296] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[0297] 3. The server uses AI analysis to calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion information at nearby facilities, event information, emotional data, etc.

[0298] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[0299] 5. The user follows the directions on the device to reach their workplace quickly and via a less stressful route.

[0300] In this way, the system of the present invention proposes optimal routes taking into account the user's emotional data, enabling comfortable and efficient travel. This will alleviate the shortage of transportation options in rural and depopulated areas, and provide a sustainable means of transportation while reducing user stress and anxiety.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This data is acquired in real time through various sensors and online databases. The server also receives user emotion data from the device.

[0304] Step 2:

[0305] The server stores the collected data in a database. At the same time, it preprocesses the data. Specifically, it cleans the data to remove incomplete data and noise. Next, it normalizes the data to convert data of different scales into a consistent format. After that, it extracts features to extract the information necessary for data analysis. Emotion data is also preprocessed in the same way.

[0306] Step 3:

[0307] The server uses the preprocessed data to perform analysis using an AI model. The AI ​​model combines and analyzes traffic conditions, congestion information, event information, emotional data, and other data to calculate the optimal route. By incorporating emotional data, the system selects a route that reduces the user's stress and anxiety.

[0308] Step 4:

[0309] After calculating the optimal route information, the server provides this data to external parties via a public API that external systems and applications can use to return route information in real time upon request.

[0310] Step 5:

[0311] The device receives a request from the user and sends information about the current location, destination, and emotion data to the server. The user makes this request using a smartphone app.

[0312] Step 6:

[0313] The server recalculates the optimal route based on the current location, destination information, and emotion data received from the device, and sends the results back to the device, providing the optimal route based on the latest data.

[0314] Step 7:

[0315] The terminal displays the optimal route information received from the server to the user, the route information being adjusted based on the user's emotional state.

[0316] Step 8:

[0317] The user travels according to the optimal route displayed on the device. By utilizing the emotion engine, the user can reduce stress and reach their destination comfortably.

[0318] Example 2

[0319] 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."

[0320] Conventional ride-sharing services calculate optimal routes using real-time data, but do not take the user's emotional state into account. As a result, users experiencing high levels of stress or fatigue may find their trips uncomfortable. Furthermore, route suggestions that take into account event information and the congestion status of nearby facilities are lacking, creating a need to improve the quality of the travel experience. The present invention aims to solve these problems by proposing more comfortable routes based on the user's emotional state.

[0321] 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.

[0322] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns an optimal route, means for collecting, integrating, and analyzing user emotion data, and means for evaluating the comfort of the route based on the emotion data. This makes it possible to provide a comfortable travel route that reduces stress and fatigue based on the user's emotional state.

[0323] "Real-time data" refers to data that is collected instantaneously by the system and is immediately available for use.

[0324] "Collection" is the act of gathering the required data or information in a certain way.

[0325] "Preprocessing" refers to the process of cleaning, normalizing, and other operations to prepare collected data in a form that is easier to analyze.

[0326] "Analysis" is the process of drawing conclusions and insights from data using algorithms and artificial intelligence.

[0327] An "optimal route" is the most efficient and appropriate route from one point to another in terms of time, distance, comfort, etc.

[0328] A "user terminal" is a device that a user operates to send and receive information, and typically refers to a smartphone or tablet.

[0329] A "public API" is a publicly available application programming interface, a program interface designed to be available to external systems and applications.

[0330] "Emotion data" is information that reflects the user's emotional state, such as data indicating stress level or fatigue level.

[0331] A "machine learning model" is a collection of algorithms that learn from data and perform tasks such as prediction and classification based on that data.

[0332] "Optimization" is the process of making adjustments to achieve the best possible results within conditions and constraints in order to achieve a specific goal.

[0333] MODE FOR CARRYING OUT THE INVENTION

[0334] This invention relates to a system that uses real-time data and AI analysis to improve the convenience of ride-sharing services, as well as a system that combines an emotion engine that recognizes user emotions.

[0335] server

[0336] The server performs the following functions:

[0337] 1. Data Collection

[0338] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals. Specifically, data is obtained from external services such as Google Maps API, OpenStreetMap, and Eventbrite API.

[0339] 2. Data Preprocessing

[0340] The server stores the collected data in a database and performs data cleaning and normalization using an SQL database and the Python pandas library. This removes incomplete data and noise, and converts the data scale into a consistent format. Sentiment data is also preprocessed in the same way.

[0341] 3. AI analysis

[0342] Based on the preprocessed data, the server uses machine learning models such as TensorFlow and PyTorch to calculate the optimal route, taking into account traffic conditions, congestion information, and event information, as well as emotional data provided by the emotion engine, to predict the most comfortable route for the user.

[0343] 4. API provided

[0344] The server accepts requests from external systems and applications through a Restful API and provides real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0345] Terminal

[0346] 1. User Request

[0347] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends it to the server. Requests are typically made through a smartphone app.

[0348] 2. Route display

[0349] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving comfort.

[0350] User

[0351] 1. Submit a request

[0352] The user inputs their current location and destination using a smartphone app to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to a server.

[0353] 2. Route confirmation and movement

[0354] Users travel according to the optimal route displayed on their device, and the emotion engine also provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0355] Specific examples

[0356] Consider a scenario where a user uses a smartphone app to book a ride from home to work:

[0357] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[0358] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[0359] 3. The server uses AI analysis to calculate the optimal route based on location information, traffic conditions, facility usage information, event information, and emotional data collected in real time.

[0360] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[0361] 5. The user follows the instructions on the device to reach their workplace quickly and via a less stressful route.

[0362] In this way, the system of the present invention proposes an optimal route taking into consideration the user's emotional data, enabling comfortable and efficient travel.

[0363] ※Example prompt:

[0364] "User A wants to book a ride from home to work and is looking for a short, low-stress route. Current location: 'Home Address', Destination: 'Work Address', Emotional state: 'High Stress'. Let the AI ​​suggest the optimal route."

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Step 1: Data collection

[0367] The server collects real-time GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This uses Google Maps API, OpenStreetMap, aerial photograph API, congestion information API, event information API, etc. The server collects and integrates the data obtained from these APIs. The input is data from each external API, and the output is integrated real-time data.

[0368] Step 2: Collecting Emotional Data

[0369] The device collects the user's emotional data, such as stress level and fatigue level, obtained from a smartphone or wearable device. The device then transmits this data to a server. The input is the emotional data, and the output is the integrated emotional data transmitted to the server.

[0370] Step 3: Data Preprocessing

[0371] The server stores the collected data in a database and performs data cleaning and normalization processes to remove incomplete data and noise, and convert the data scale into a consistent format. Sentiment data is also preprocessed in the same way. The input is raw data, and the output is preprocessed clean data.

[0372] Step 4: Emotion data preprocessing

[0373] The server receives the user's emotion data sent from the device and stores it in a database. This data is also cleaned and normalized to form a consistent format. The input is raw emotion data, and the output is preprocessed emotion data.

[0374] Step 5: Calculate the optimal route

[0375] The server inputs the preprocessed data into machine learning models such as TensorFlow and PyTorch. The optimal route is calculated based on traffic conditions, congestion information, event information, and emotion data. The input is the preprocessed clean data and emotion data, and the output is the optimal route information.

[0376] Step 6: Accept API requests

[0377] The server accepts requests from external systems and applications via a Restful API. The input is the request from the external system, and the output is the result of the optimal route calculation.

[0378] Step 7: Providing optimal route information

[0379] The server provides optimal route information to external systems and terminals, allowing users to receive optimal route information in real time. The input is optimal route information, and the output is route information provided to external systems and terminals.

[0380] Step 8: Receiving a User Request

[0381] The user inputs their current location and destination using a smartphone app. At the same time, the user's emotional data is also collected and sent from the device to the server. The input is the user's location information and emotional data, and the output is a request sent to the server.

[0382] Step 9: View Route Information

[0383] The terminal displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently. Route suggestions that take into account data from the emotion engine are also displayed. The input is optimal route information from the server, and the output is route information displayed to the user.

[0384] (Application example 2)

[0385] 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."

[0386] Conventional ride-sharing services do not select routes that take into account the user's actual emotional state during the trip, and do not sufficiently consider the user's psychological comfort. This results in increased stress and fatigue during the trip, resulting in a poor user experience. Furthermore, optimal route calculations that reflect real-time changes in traffic conditions and congestion information at nearby facilities are also inadequate. A new system is needed to solve these issues and realize more comfortable and efficient travel.

[0387] The specific processing by the specific 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 collecting real-time data, means for pre-processing the collected data, means for analyzing the pre-processed data and calculating an optimal route, means for providing the calculation results to the user terminal, means for accepting requests from outside and providing a public API that returns an optimal route, means for recognizing the user's emotional state and collecting emotional data, means for calculating an optimal route including the emotional data, and means for presenting a route adjusted based on the emotional state. This makes it possible to select a comfortable route that takes the user's emotional state into consideration, reducing stress during travel and providing a better user experience.

[0388] "Means for collecting real-time data" refers to a device or method for acquiring GPS data, map information, aerial photographs, congestion information on nearby facilities, information on nearby events, and user emotion data in real time.

[0389] "Means for pre-processing collected data" refers to devices or methods that perform data cleaning, noise removal, and normalization processes to convert raw data into a form suitable for analysis.

[0390] "Means for analyzing pre-processed data and calculating optimal routes" means a device or method that uses an artificial intelligence model to calculate the most efficient and safest travel route from the pre-processed data.

[0391] The "means for providing the calculation results to the user terminal" refers to a device or method for transmitting the calculated optimum route information to the user's device such as a smartphone or smart glasses in real time and displaying it.

[0392] "A means of providing a public API that accepts requests from outside and responds with the optimal route" is a public interface that provides optimal route information in real time in response to requests from other systems or applications.

[0393] "Means for recognizing a user's emotional state and collecting emotional data" refers to a device or method that uses emotion recognition technology to obtain a user's emotional state (e.g., stress level, fatigue level) and collects this as data.

[0394] The "means for calculating an optimal route that takes into account emotional data" refers to a device or method that takes into account the emotional state of the user and integrates it with conventional data to calculate the most comfortable and efficient travel route.

[0395] A "means for presenting a route adjusted based on emotional state" is a device or method that displays an optimal route adjusted based on the user's emotional state on the user's device and provides real-time guidance.

[0396] This invention is a system that improves the convenience of ride-sharing services by using real-time data and artificial intelligence analysis, and combines an emotion engine that recognizes user emotions. This system consists of three main components: a server, a terminal, and a user.

[0397] server

[0398] 1. Data Collection

[0399] The server collects GPS data, map information, aerial photographs, information on the congestion status of nearby facilities, and information on nearby events in real time. It also collects user emotion data using an emotion engine. This process is performed automatically at regular intervals, ensuring that the latest information is always available.

[0400] 2. Data Preprocessing

[0401] The server stores the collected data in a database and preprocesses it. It cleans and removes noise from the data, and normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way to make it suitable for analysis.

[0402] 3. AI analysis

[0403] Based on the pre-processed data, the server calculates the optimal route using a generative AI model that takes into account traffic conditions, congestion information, event information, and sentiment data to predict the most comfortable and efficient route for the user.

[0404] 4. API provided

[0405] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0406] Terminal

[0407] 1. User Request

[0408] The device receives a request from the user and sends information about the current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. This request is usually made through a smartphone app.

[0409] 2. Route display

[0410] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[0411] User

[0412] 1. Submit a request

[0413] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[0414] 2. Route confirmation and movement

[0415] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0416] Hardware and Software Used

[0417] Hardware: Smart glasses (general name), head-mounted display (general name)

[0418] Software: Emotion recognition engine (any software for recognizing emotional states), public API (any interface for sending and receiving real-time data)

[0419] Prompt Sentence Examples

[0420] Current location: "35.6895,139.6917" (Tokyo Station)

[0421] Destination: "35.6580,139.7514" (Tokyo Tower)

[0422] User's emotional state: "stressed"

[0423] This system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[0424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0425] Step 1:

[0426] Data collection

[0427] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, information on nearby events, and user sentiment data in real time. The latest data is obtained from external data sources at specified intervals through the data collection API. The input is data from the API, and the output is raw data stored in a database.

[0428] Step 2:

[0429] Data Preprocessing

[0430] The server performs data cleaning to remove incomplete data and noise from the collected raw data, then normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way. The input is raw data stored in a database, and the output is preprocessed analysis data.

[0431] Step 3:

[0432] AI analysis

[0433] The server inputs the preprocessed data into a generative AI model to calculate the optimal route based on traffic conditions, congestion information, event information, and emotional data. The input is the preprocessed analytical data, and the output is the calculation result of the optimal route. In this process, the devised optimal route is customized by taking into account the user's current emotional state.

[0434] Step 4:

[0435] Request received

[0436] The device receives a request from the user (current location, destination, and emotion data) and sends it to the server. The input is the data entered by the user into the device, and the output is the request data to the server. In this step, the emotion recognition engine obtains the user's emotion data.

[0437] Step 5:

[0438] Providing route information

[0439] The server calculates the optimal route information for the request based on the received request data and returns it to the device. The input is the request data received from the device, and the output is the route information to the device. This information is provided in real time via the API.

[0440] Step 6:

[0441] Route display

[0442] The terminal displays the optimal route information received from the server to the user. The input is the route information from the server, and the output is the route guidance displayed to the user. The display is performed via the user's device, such as smart glasses or a head-mounted display.

[0443] Step 7:

[0444] User Movement

[0445] The user travels according to the optimal route displayed on the device. The input is the displayed route guidance, and the output is the user's actual travel route. The emotion engine adjusts the route based on the user's emotional state, allowing the user to enjoy a comfortable travel experience.

[0446] As a result, the system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Second embodiment]

[0451] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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."

[0463] This invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI. The following describes an embodiment of the system and the processing of its program in natural language.

[0464] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0465] server

[0466] 1. Data Collection

[0467] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time, ensuring that the latest information is always available.

[0468] 2. Data Preprocessing

[0469] The server stores the collected data in a database and performs preprocessing such as data cleaning, normalization, and feature extraction. This preprocessing improves the quality of the data and makes it suitable for analysis.

[0470] 3. AI analysis

[0471] Based on the pre-processed data, the server calculates the optimal route using an AI model that takes into account various traffic conditions, congestion, event information, etc. to predict the most efficient route for the user.

[0472] 4. API provided

[0473] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other Mobility as a Service (MaaS) systems.

[0474] Terminal

[0475] 1. User Request

[0476] The device receives requests from users through a smartphone app and sends their current location and destination to the server.

[0477] 2. Route display

[0478] The device receives the optimal route information from the server and displays it to the user, allowing the user to efficiently reach their destination.

[0479] User

[0480] 1. Submit a request

[0481] The user uses the device to request a ride-sharing service by inputting their current location and destination, and the system searches for the optimal route.

[0482] 2. Route confirmation and movement

[0483] Users can travel by following the optimal route displayed on the device, which can save time and reduce costs.

[0484] Specific examples

[0485] A user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0486] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app.

[0487] 2. The device sends this information to the server.

[0488] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0489] 4. The calculated route information is sent back from the server to the device.

[0490] 5. The device displays the optimal route to the user, who then follows the route to their destination, the shopping mall.

[0491] In this way, the system of the present invention provides users with a fast and efficient means of transportation, complements the lack of transportation in rural and depopulated areas, and enables the provision of sustainable transportation.

[0492] The processing flow will be explained below.

[0493] Step 1:

[0494] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time from various sensors and databases. This data collection is performed automatically at regular intervals, and is set to always obtain the latest information.

[0495] Step 2:

[0496] The server stores the collected data in a database, which is used for subsequent preprocessing.

[0497] Step 3:

[0498] The server preprocesses the stored data, cleaning it to remove incomplete data and noise, normalizing it to convert the scale of the data into a consistent format, and extracting the features required for analysis to prepare it for efficient analysis by the AI ​​model.

[0499] Step 4:

[0500] The server uses an artificial intelligence model to analyze the preprocessed data. Specifically, it calculates the optimal route by taking into account traffic conditions, congestion information, event information, etc. The AI ​​model predicts supply and demand based on past and current data, and calculates the optimal travel route for the user.

[0501] Step 5:

[0502] The server provides the optimal route information, which is the result of the analysis, via a public API. In response to requests from external systems and applications, the server responds with the optimal route information in real time. Using this API, it is possible to link with other MaaS systems and third-party applications.

[0503] Step 6:

[0504] The device receives requests from users via a smartphone app and sends information about their current location and destination to the server.

[0505] Step 7:

[0506] The server recalculates the optimal route based on the current location and destination information received from the device and returns the analysis results to the device.

[0507] Step 8:

[0508] The terminal displays the optimal route information received from the server to the user, thereby enabling the user to travel efficiently.

[0509] Step 9:

[0510] The user heads to the destination by following the optimal route displayed on the terminal, thereby quickly reaching the destination.

[0511] Example 1

[0512] 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."

[0513] Conventional ride-sharing services face challenges in finding the optimal route to reach a destination efficiently and quickly. In particular, it is difficult to optimize routes that take into account real-time changes in traffic conditions and event information, which can lead to lower user satisfaction. Furthermore, due to the lack of adequate transportation options in rural and depopulated areas, there is a need for sustainable transportation services.

[0514] 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.

[0515] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for transmitting current location and destination information to the server based on a user request, and means for displaying the optimal route information received from the server on the user terminal. This allows users to obtain optimal route information that is updated in real time and reach their destination efficiently and quickly. Furthermore, it is possible to provide appropriate transportation options even in rural and depopulated areas, thereby realizing sustainable transportation services.

[0516] "Real-time data" refers to data that indicates the current situation or state, and is available at approximately the same time as it is collected.

[0517] "Preprocessing" is the process of preparing collected data in a form that is easier to analyze, and includes processes such as cleaning, normalization, and feature extraction.

[0518] An "optimal route" is a route that allows a user to reach a destination most efficiently under certain conditions, and is calculated taking into account time, cost, traffic conditions, and the like.

[0519] A "public API" is a published interface available to external systems and applications, providing a means to access specific functionality or data.

[0520] A "user terminal" refers to an electronic device used by a user, including a smartphone, tablet, etc.

[0521] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions and analyses for specific tasks.

[0522] A "prompt" is a piece of text or command that is input to a generative AI model and serves as instructions for the AI ​​to generate output.

[0523] "GPS Data" means geographical location information data obtained using the Global Positioning System.

[0524] "Map information" is data that includes geographical information such as topography, roads, buildings, and facilities, and is used for navigation and location-based services.

[0525] "Aerial photography" refers to images of the Earth's surface taken from aircraft or satellites and is used for advanced geographic analysis and mapping.

[0526] "Congestion information for surrounding facilities" is data that indicates the current congestion situation at a specific facility or area, and is information that affects the movement and behavior of users.

[0527] "Local event information" is data relating to events taking place in a specific area, and includes information such as time, location, and content.

[0528] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and generative AI models. An embodiment of the system and the processing of its program are described below.

[0529] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and public API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0530] server

[0531] The server has the following functions:

[0532] 1. Data Collection

[0533] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information about nearby events in real time. A general online map API (e.g., Google Maps API) is used for map information. Event information and congestion status are also acquired by linking with various sensors and online databases.

[0534] 2. Data Preprocessing

[0535] The server stores the collected data in a database and performs cleaning (removing outliers), normalization (standardizing the data format), and feature extraction (extracting important elements), improving the quality of the data and making it suitable for analysis.

[0536] 3. AI analysis

[0537] Based on the preprocessed data, the server uses a generative AI model to calculate the optimal route. This AI model inputs complex data such as traffic information, congestion status, and event information to predict the most efficient route for the user. The generative AI model uses libraries such as PyTorch and TensorFlow.

[0538] 4. API provided

[0539] The server accepts requests from external systems and applications through a public API and responds with the optimal route, making the system compatible with other Mobility as a Service (MaaS) systems.

[0540] Terminal

[0541] The terminal has the following features:

[0542] 1. User Request

[0543] The device receives requests from users and sends their current location and destination to the server. The request is made through a smartphone app. The device can automatically obtain location information using GPS, eliminating the need for users to enter their location information.

[0544] 2. Route display

[0545] The device interprets the optimal route information received from the server and displays it to the user. The device provides the information as an easy-to-understand map display and uses a large screen and voice guidance to guide the user to the optimal route.

[0546] User

[0547] The user performs the following activities:

[0548] 1. Submit a request

[0549] Users use the device to request a ride-sharing service by inputting their current location and destination and searching for the best route. Requests are made through a simple user interface, and can be made either by voice or manual input.

[0550] 2. Route confirmation and movement

[0551] Users can travel by following the optimal route displayed on their device, which saves time and money, and also helps avoid problems such as traffic congestion.

[0552] Specific examples

[0553] The following example illustrates a scenario where a user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0554] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app. The user can use voice input or manual input.

[0555] 2. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0556] 3. The server uses a generative AI model to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0557] 4. The calculated route information is sent back from the server to the device.

[0558] 5. The device displays the optimal route to the user, who then follows that route to their destination, the shopping mall.

[0559] An example of a prompt sentence to be input into the generative AI model is, "I'm currently at home and my destination is a nearby shopping mall. Please tell me the best route."

[0560] In this way, the system of the present invention not only provides users with a fast and efficient means of transportation, but also complements the lack of transportation in rural and depopulated areas, thereby realizing the provision of sustainable transportation services.

[0561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0562] Step 1: Data collection

[0563] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time.

[0564] Specific behavior:

[0565] The server periodically acquires the vehicle's GPS data and stores the location information in a database.

[0566] Map information is obtained using online map APIs, such as Google Maps API to obtain road information and terrain data.

[0567] Aerial photographs are downloaded from a satellite image database to maintain up-to-date surface information.

[0568] Congestion information at nearby facilities is obtained by synchronizing with sensor data provided by each facility and online databases.

[0569] Information about surrounding events is collected regularly from social networks and event management systems.

[0570] Input: Various data sources (GPS system, map API, satellite image database, facility sensor, SNS)

[0571] Output: A database of collected data

[0572] Step 2: Data Preprocessing

[0573] The server stores the collected data in a database and performs data cleaning, normalization, and feature extraction.

[0574] Specific behavior:

[0575] The server uses scripts to clean the data stored in the database and fill in any outliers or missing parts.

[0576] Normalization processing unifies data formats and ensures consistency.

[0577] Important features such as traffic signals, road width, and congestion status are extracted to generate a dataset suitable for AI analysis.

[0578] Input: Raw data collected

[0579] Output: Preprocessed dataset

[0580] Step 3: AI analysis

[0581] The server uses the pre-processed data to calculate the optimal route using a generative AI model.

[0582] Specific behavior:

[0583] The server inputs the preprocessed data into a generative AI model (e.g., PyTorch, TensorFlow) and executes the model.

[0584] Based on the input data, the generative AI model calculates the expected travel time and cost for each route and predicts the most efficient route for the user.

[0585] The model results are saved in a database and prepared for API provision.

[0586] Input: Preprocessed dataset

[0587] Output: Optimal route information (estimated travel time and cost)

[0588] Step 4: Providing API

[0589] The server provides optimal route information in response to requests from external systems and applications.

[0590] Specific behavior:

[0591] The server analyzes the API request and searches for the optimal route based on the requested current location and destination information.

[0592] The optimal route information found is returned to external systems and applications.

[0593] Input: Requests from external systems and applications

[0594] Output: Optimal route information (API response)

[0595] Step 5: User Request

[0596] The terminal receives a request from the user and sends the current location and destination to the server.

[0597] Specific behavior:

[0598] The device automatically obtains the user's current location information using the GPS function.

[0599] The user enters their destination into the device and taps the send request button.

[0600] The device sends the current location and destination information to the server.

[0601] Input: User's current location and destination information

[0602] Output: Request sent to server

[0603] Step 6: View Route

[0604] The terminal displays the optimal route information received from the server to the user.

[0605] Specific behavior:

[0606] The terminal interprets the optimum route information received from the server and displays it in an easy-to-understand manner on the user interface.

[0607] The device provides voice guidance and visual map displays to help users navigate to their destinations.

[0608] Input: Optimal route information from the server

[0609] Output: Show route to user

[0610] Step 7: Move and update in real time

[0611] Users follow the optimal route displayed on the device, and information is updated in real time while they are traveling.

[0612] Specific behavior:

[0613] The device periodically communicates with the server to obtain real-time traffic and event information.

[0614] If the route changes, the terminal notifies the user and recalculates and displays the optimal route.

[0615] Input: Real-time traffic and event information

[0616] Output: Constantly updated optimal route information

[0617] (Application example 1)

[0618] 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."

[0619] Currently, parcel delivery management at logistics centers is often inefficient because delivery priority and traffic conditions are not fully considered. This results in delays in delivery time and increased costs, which is a problem. These issues are particularly serious in rural and depopulated areas where transportation options are limited.

[0620] 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.

[0621] In this invention, the server includes means for collecting real-time data, means for preprocessing the data, means for analyzing the preprocessed data using an AI model to calculate an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for managing package deliveries at the logistics center, and means for providing a delivery route that takes into account local traffic information and the priority of delivery destinations. This significantly improves package delivery efficiency at the logistics center, enabling shorter delivery times and reduced costs.

[0622] "Real-time data" refers to up-to-date information that reflects ongoing events and situations.

[0623] "Data preprocessing" refers to the process of preparing collected data in a form suitable for analysis through processes such as cleaning, normalization, and feature extraction.

[0624] An "artificial intelligence model" refers to a program that is trained by machine learning algorithms to automatically perform a specific task (in this case, calculating the optimal route).

[0625] An "optimal route" refers to the route that most efficiently reaches a destination under certain conditions.

[0626] "User terminal" refers to a device (e.g., smartphone, tablet) that a user operates directly to use a service.

[0627] A "public API" refers to a programmatic interface that is accessible to external systems and applications.

[0628] A "logistics center" refers to a facility that carries out logistics operations such as consolidating, storing, and shipping cargo.

[0629] "Delivery management" refers to the process of efficiently planning and executing a series of tasks from package receipt to final delivery.

[0630] "Traffic information" refers to information that affects traffic flow, such as road congestion, traffic accidents, and construction work.

[0631] "Delivery destination priority" refers to an indicator that indicates the urgency or importance of the delivery of the package.

[0632] "Collection means" refers to methods and devices for collecting data through various sensors, the Internet, etc.

[0633] "Providing means" refers to a method or device for providing information to a user terminal or other system.

[0634] This invention is a system for improving the efficiency of package delivery in a logistics center. The system is composed of three main components: a server, a terminal, and a user, and will be described in detail below.

[0635] server

[0636] 1. Real-time data collection methods

[0637] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and delivery priority information in real time, ensuring that the latest information needed to calculate delivery routes is always available.

[0638] 2. Data preprocessing methods

[0639] The server stores the collected data in a database and performs preprocessing such as cleaning, normalization, and feature extraction, which improves the quality of the data and makes it suitable for analysis.

[0640] 3. Analysis methods using artificial intelligence models

[0641] Based on the preprocessed data, the server uses an artificial intelligence model (e.g., a model using TensorFlow or PyTorch) to calculate the optimal route. This AI model comprehensively evaluates traffic information, congestion status, delivery destination priority, etc. to calculate the most efficient delivery route.

[0642] 4. Public API Provision Method

[0643] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other logistics systems.

[0644] Terminal

[0645] 1. User request reception method

[0646] The device receives requests from delivery drivers via a smartphone app and sends the current location and delivery destination information to the server.

[0647] 2. Route display method

[0648] The terminal receives the optimal route information received from the server and displays it to the delivery driver, enabling efficient delivery.

[0649] User

[0650] 1. Request sending method

[0651] Delivery drivers use the device to enter their current location and delivery destination information and request the optimal route.

[0652] 2. Route confirmation and delivery

[0653] Delivery drivers follow the optimal route displayed on the terminal, which shortens delivery times and reduces costs.

[0654] Specific examples

[0655] For example, if a driver delivers packages from their current location (Tokyo Station) to multiple destinations (Shibuya Station, Shinjuku Station), the following steps are taken:

[0656] 1. The driver enters their current location and delivery destination into a smartphone app.

[0657] 2. The device sends this information to the server.

[0658] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, delivery priority, etc.

[0659] 4. The calculated route information is sent back from the server to the terminal and displayed to the driver.

[0660] 5. The driver follows the route to the desired delivery location.

[0661] Prompt Sentence Examples

[0662] For example, use the following prompt:

[0663] "Based on the current location (Tokyo Station) and multiple delivery destinations (Shibuya Station, Shinjuku Station), please use AI to calculate the optimal delivery route taking into account traffic information and congestion."

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] server

[0667] Real-time data collection

[0668] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and package delivery priority information in real time. Data from each sensor and the Internet is used as input. This data is sent to the server and stored in a database. The output is raw data stored in the database.

[0669] Step 2:

[0670] server

[0671] Data Preprocessing

[0672] The server performs preprocessing on the collected data, including cleaning, normalization, and feature extraction. The raw data collected in step 1 is used as input. The data is processed to remove imperfections and make it consistent. The output is preprocessed data that has been organized into a form suitable for analysis.

[0673] Step 3:

[0674] server

[0675] Analysis using artificial intelligence models

[0676] The server uses the preprocessed data to calculate the optimal delivery route using an artificial intelligence model (e.g., a model using TensorFlow or PyTorch). The preprocessed data is used as input. The AI ​​model comprehensively evaluates delivery priority, real-time traffic information, congestion status, etc., and calculates the most efficient route. The output is optimal route information.

[0677] Step 4:

[0678] server

[0679] Route information provided via public API

[0680] The server provides the optimal route information to the terminal through a public API. The optimal route information calculated in step 3 is used as input. The server receives an API request and returns the corresponding optimal route information. The output is a response to the API request with the optimal route information.

[0681] Step 5:

[0682] Terminal

[0683] Submitting a User Request

[0684] The terminal receives a request from the delivery driver and sends the current location and delivery destination information to the server. The current location and delivery destination information entered by the driver on the smartphone app are used as input. The request is then sent to the server. The output is the request data sent to the server.

[0685] Step 6:

[0686] Terminal

[0687] View route information

[0688] The terminal displays the optimal route information received from the server to the driver. The optimal route information from the server is used as input. A process is performed to visually display the route information. The output is the route information that can be visually viewed by the driver.

[0689] Step 7:

[0690] User

[0691] Entering a request

[0692] The delivery driver uses a smartphone app to input their current location and delivery destination information and request the optimal route. As input, they manually enter their current location and delivery destination information into the app. The request is processed through the app. The output is the request data entered into the terminal.

[0693] Step 8:

[0694] User

[0695] Route confirmation and delivery

[0696] The delivery driver makes the delivery by following the optimal route displayed on the smartphone app. The optimal route information displayed on the terminal is used as input. The driver follows the route to the destination. The output is the delivery completed.

[0697] 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.

[0698] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI analysis, and also to a system that combines an emotion engine that recognizes user emotions. An embodiment of the system and the processing of its program are explained below in natural language.

[0699] server

[0700] 1. Data Collection

[0701] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals, ensuring that the latest information is always available.

[0702] 2. Data Preprocessing

[0703] The server stores the collected data in a database and pre-processes it. Data cleaning removes incomplete data and noise, and normalization converts the scale of the data into a consistent format. Emotion data is also pre-processed in the same way to make it suitable for analysis.

[0704] 3. AI analysis

[0705] Based on the pre-processed data, the server calculates the optimal route using an artificial intelligence model that takes into account traffic conditions, congestion information, and event information, and also uses emotion data provided by an emotion engine to predict the most comfortable route for the user.

[0706] 4. API provided

[0707] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0708] Terminal

[0709] 1. User Request

[0710] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. Requests are typically made through smartphone apps.

[0711] 2. Route display

[0712] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[0713] User

[0714] 1. Submit a request

[0715] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[0716] 2. Route confirmation and movement

[0717] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0718] Specific examples

[0719] Consider a scenario where a user uses a smartphone app to book a ride from their home (current location) to their workplace (destination):

[0720] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[0721] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[0722] 3. The server uses AI analysis to calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion information at nearby facilities, event information, emotional data, etc.

[0723] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[0724] 5. The user follows the directions on the device to reach their workplace quickly and via a less stressful route.

[0725] In this way, the system of the present invention proposes optimal routes taking into account the user's emotional data, enabling comfortable and efficient travel. This will alleviate the shortage of transportation options in rural and depopulated areas, and provide a sustainable means of transportation while reducing user stress and anxiety.

[0726] The processing flow will be explained below.

[0727] Step 1:

[0728] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This data is acquired in real time through various sensors and online databases. The server also receives user emotion data from the device.

[0729] Step 2:

[0730] The server stores the collected data in a database. At the same time, it preprocesses the data. Specifically, it cleans the data to remove incomplete data and noise. Next, it normalizes the data to convert data of different scales into a consistent format. After that, it extracts features to extract the information necessary for data analysis. Emotion data is also preprocessed in the same way.

[0731] Step 3:

[0732] The server uses the preprocessed data to perform analysis using an AI model. The AI ​​model combines and analyzes traffic conditions, congestion information, event information, emotional data, and other data to calculate the optimal route. By incorporating emotional data, the system selects a route that reduces the user's stress and anxiety.

[0733] Step 4:

[0734] After calculating the optimal route information, the server provides this data to external parties via a public API that external systems and applications can use to return route information in real time upon request.

[0735] Step 5:

[0736] The device receives a request from the user and sends information about the current location, destination, and emotion data to the server. The user makes this request using a smartphone app.

[0737] Step 6:

[0738] The server recalculates the optimal route based on the current location, destination information, and emotion data received from the device, and sends the results back to the device, providing the optimal route based on the latest data.

[0739] Step 7:

[0740] The terminal displays the optimal route information received from the server to the user, the route information being adjusted based on the user's emotional state.

[0741] Step 8:

[0742] The user travels according to the optimal route displayed on the device. By utilizing the emotion engine, the user can reduce stress and reach their destination comfortably.

[0743] Example 2

[0744] 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."

[0745] Conventional ride-sharing services calculate optimal routes using real-time data, but do not take the user's emotional state into account. As a result, users experiencing high levels of stress or fatigue may find their trips uncomfortable. Furthermore, route suggestions that take into account event information and the congestion status of nearby facilities are lacking, creating a need to improve the quality of the travel experience. The present invention aims to solve these problems by proposing more comfortable routes based on the user's emotional state.

[0746] 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.

[0747] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns an optimal route, means for collecting, integrating, and analyzing user emotion data, and means for evaluating the comfort of the route based on the emotion data. This makes it possible to provide a comfortable travel route that reduces stress and fatigue based on the user's emotional state.

[0748] "Real-time data" refers to data that is collected instantaneously by the system and is immediately available for use.

[0749] "Collection" is the act of gathering the required data or information in a certain way.

[0750] "Preprocessing" refers to the process of cleaning, normalizing, and other operations to prepare collected data in a form that is easier to analyze.

[0751] "Analysis" is the process of drawing conclusions and insights from data using algorithms and artificial intelligence.

[0752] An "optimal route" is the most efficient and appropriate route from one point to another in terms of time, distance, comfort, etc.

[0753] A "user terminal" is a device that a user operates to send and receive information, and typically refers to a smartphone or tablet.

[0754] A "public API" is a publicly available application programming interface, a program interface designed to be available to external systems and applications.

[0755] "Emotion data" is information that reflects the user's emotional state, such as data indicating stress level or fatigue level.

[0756] A "machine learning model" is a collection of algorithms that learn from data and perform tasks such as prediction and classification based on that data.

[0757] "Optimization" is the process of making adjustments to achieve the best possible results within conditions and constraints in order to achieve a specific goal.

[0758] MODE FOR CARRYING OUT THE INVENTION

[0759] This invention relates to a system that uses real-time data and AI analysis to improve the convenience of ride-sharing services, as well as a system that combines an emotion engine that recognizes user emotions.

[0760] server

[0761] The server performs the following functions:

[0762] 1. Data Collection

[0763] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals. Specifically, data is obtained from external services such as Google Maps API, OpenStreetMap, and Eventbrite API.

[0764] 2. Data Preprocessing

[0765] The server stores the collected data in a database and performs data cleaning and normalization using an SQL database and the Python pandas library. This removes incomplete data and noise, and converts the data scale into a consistent format. Sentiment data is also preprocessed in the same way.

[0766] 3. AI analysis

[0767] Based on the preprocessed data, the server uses machine learning models such as TensorFlow and PyTorch to calculate the optimal route, taking into account traffic conditions, congestion information, and event information, as well as emotional data provided by the emotion engine, to predict the most comfortable route for the user.

[0768] 4. API provided

[0769] The server accepts requests from external systems and applications through a Restful API and provides real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0770] Terminal

[0771] 1. User Request

[0772] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends it to the server. Requests are typically made through a smartphone app.

[0773] 2. Route display

[0774] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving comfort.

[0775] User

[0776] 1. Submit a request

[0777] The user inputs their current location and destination using a smartphone app to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to a server.

[0778] 2. Route confirmation and movement

[0779] Users travel according to the optimal route displayed on their device, and the emotion engine also provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0780] Specific examples

[0781] Consider a scenario where a user uses a smartphone app to book a ride from home to work:

[0782] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[0783] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[0784] 3. The server uses AI analysis to calculate the optimal route based on location information, traffic conditions, facility usage information, event information, and emotional data collected in real time.

[0785] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[0786] 5. The user follows the instructions on the device to reach their workplace quickly and via a less stressful route.

[0787] In this way, the system of the present invention proposes an optimal route taking into consideration the user's emotional data, enabling comfortable and efficient travel.

[0788] ※Example prompt:

[0789] "User A wants to book a ride from home to work and is looking for a short, low-stress route. Current location: 'Home Address', Destination: 'Work Address', Emotional state: 'High Stress'. Let the AI ​​suggest the optimal route."

[0790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0791] Step 1: Data collection

[0792] The server collects real-time GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This uses Google Maps API, OpenStreetMap, aerial photograph API, congestion information API, event information API, etc. The server collects and integrates the data obtained from these APIs. The input is data from each external API, and the output is integrated real-time data.

[0793] Step 2: Collecting Emotional Data

[0794] The device collects the user's emotional data, such as stress level and fatigue level, obtained from a smartphone or wearable device. The device then transmits this data to a server. The input is the emotional data, and the output is the integrated emotional data transmitted to the server.

[0795] Step 3: Data Preprocessing

[0796] The server stores the collected data in a database and performs data cleaning and normalization processes to remove incomplete data and noise, and convert the data scale into a consistent format. Sentiment data is also preprocessed in the same way. The input is raw data, and the output is preprocessed clean data.

[0797] Step 4: Emotion data preprocessing

[0798] The server receives the user's emotion data sent from the device and stores it in a database. This data is also cleaned and normalized to form a consistent format. The input is raw emotion data, and the output is preprocessed emotion data.

[0799] Step 5: Calculate the optimal route

[0800] The server inputs the preprocessed data into machine learning models such as TensorFlow and PyTorch. The optimal route is calculated based on traffic conditions, congestion information, event information, and emotion data. The input is the preprocessed clean data and emotion data, and the output is the optimal route information.

[0801] Step 6: Accept API requests

[0802] The server accepts requests from external systems and applications via a Restful API. The input is the request from the external system, and the output is the result of the optimal route calculation.

[0803] Step 7: Providing optimal route information

[0804] The server provides optimal route information to external systems and terminals, allowing users to receive optimal route information in real time. The input is optimal route information, and the output is route information provided to external systems and terminals.

[0805] Step 8: Receiving a User Request

[0806] The user inputs their current location and destination using a smartphone app. At the same time, the user's emotional data is also collected and sent from the device to the server. The input is the user's location information and emotional data, and the output is a request sent to the server.

[0807] Step 9: View Route Information

[0808] The terminal displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently. Route suggestions that take into account data from the emotion engine are also displayed. The input is optimal route information from the server, and the output is route information displayed to the user.

[0809] (Application example 2)

[0810] 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."

[0811] Conventional ride-sharing services do not select routes that take into account the user's actual emotional state during the trip, and do not sufficiently consider the user's psychological comfort. This results in increased stress and fatigue during the trip, resulting in a poor user experience. Furthermore, optimal route calculations that reflect real-time changes in traffic conditions and congestion information at nearby facilities are also inadequate. A new system is needed to solve these issues and realize more comfortable and efficient travel.

[0812] The specific processing by the specific 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 collecting real-time data, means for pre-processing the collected data, means for analyzing the pre-processed data and calculating an optimal route, means for providing the calculation results to the user terminal, means for accepting requests from outside and providing a public API that returns an optimal route, means for recognizing the user's emotional state and collecting emotional data, means for calculating an optimal route including the emotional data, and means for presenting a route adjusted based on the emotional state. This makes it possible to select a comfortable route that takes the user's emotional state into consideration, reducing stress during travel and providing a better user experience.

[0813] "Means for collecting real-time data" refers to a device or method for acquiring GPS data, map information, aerial photographs, congestion information on nearby facilities, information on nearby events, and user emotion data in real time.

[0814] "Means for pre-processing collected data" refers to devices or methods that perform data cleaning, noise removal, and normalization processes to convert raw data into a form suitable for analysis.

[0815] "Means for analyzing pre-processed data and calculating optimal routes" means a device or method that uses an artificial intelligence model to calculate the most efficient and safest travel route from the pre-processed data.

[0816] The "means for providing the calculation results to the user terminal" refers to a device or method for transmitting the calculated optimum route information to the user's device such as a smartphone or smart glasses in real time and displaying it.

[0817] "A means of providing a public API that accepts requests from outside and responds with the optimal route" is a public interface that provides optimal route information in real time in response to requests from other systems or applications.

[0818] "Means for recognizing a user's emotional state and collecting emotional data" refers to a device or method that uses emotion recognition technology to obtain a user's emotional state (e.g., stress level, fatigue level) and collects this as data.

[0819] The "means for calculating an optimal route that takes into account emotional data" refers to a device or method that takes into account the emotional state of the user and integrates it with conventional data to calculate the most comfortable and efficient travel route.

[0820] A "means for presenting a route adjusted based on emotional state" is a device or method that displays an optimal route adjusted based on the user's emotional state on the user's device and provides real-time guidance.

[0821] This invention is a system that improves the convenience of ride-sharing services by using real-time data and artificial intelligence analysis, and combines an emotion engine that recognizes user emotions. This system consists of three main components: a server, a terminal, and a user.

[0822] server

[0823] 1. Data Collection

[0824] The server collects GPS data, map information, aerial photographs, information on the congestion status of nearby facilities, and information on nearby events in real time. It also collects user emotion data using an emotion engine. This process is performed automatically at regular intervals, ensuring that the latest information is always available.

[0825] 2. Data Preprocessing

[0826] The server stores the collected data in a database and preprocesses it. It cleans and removes noise from the data, and normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way to make it suitable for analysis.

[0827] 3. AI analysis

[0828] Based on the pre-processed data, the server calculates the optimal route using a generative AI model that takes into account traffic conditions, congestion information, event information, and sentiment data to predict the most comfortable and efficient route for the user.

[0829] 4. API provided

[0830] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[0831] Terminal

[0832] 1. User Request

[0833] The device receives a request from the user and sends information about the current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. This request is usually made through a smartphone app.

[0834] 2. Route display

[0835] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[0836] User

[0837] 1. Submit a request

[0838] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[0839] 2. Route confirmation and movement

[0840] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[0841] Hardware and Software Used

[0842] Hardware: Smart glasses (general name), head-mounted display (general name)

[0843] Software: Emotion recognition engine (any software for recognizing emotional states), public API (any interface for sending and receiving real-time data)

[0844] Prompt Sentence Examples

[0845] Current location: "35.6895,139.6917" (Tokyo Station)

[0846] Destination: "35.6580,139.7514" (Tokyo Tower)

[0847] User's emotional state: "stressed"

[0848] This system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[0849] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0850] Step 1:

[0851] Data collection

[0852] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, information on nearby events, and user sentiment data in real time. The latest data is obtained from external data sources at specified intervals through the data collection API. The input is data from the API, and the output is raw data stored in a database.

[0853] Step 2:

[0854] Data Preprocessing

[0855] The server performs data cleaning to remove incomplete data and noise from the collected raw data, then normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way. The input is raw data stored in a database, and the output is preprocessed analysis data.

[0856] Step 3:

[0857] AI analysis

[0858] The server inputs the preprocessed data into a generative AI model to calculate the optimal route based on traffic conditions, congestion information, event information, and emotional data. The input is the preprocessed analytical data, and the output is the calculation result of the optimal route. In this process, the devised optimal route is customized by taking into account the user's current emotional state.

[0859] Step 4:

[0860] Request received

[0861] The device receives a request from the user (current location, destination, and emotion data) and sends it to the server. The input is the data entered by the user into the device, and the output is the request data to the server. In this step, the emotion recognition engine obtains the user's emotion data.

[0862] Step 5:

[0863] Providing route information

[0864] The server calculates the optimal route information for the request based on the received request data and returns it to the device. The input is the request data received from the device, and the output is the route information to the device. This information is provided in real time via the API.

[0865] Step 6:

[0866] Route display

[0867] The terminal displays the optimal route information received from the server to the user. The input is the route information from the server, and the output is the route guidance displayed to the user. The display is performed via the user's device, such as smart glasses or a head-mounted display.

[0868] Step 7:

[0869] User Movement

[0870] The user travels according to the optimal route displayed on the device. The input is the displayed route guidance, and the output is the user's actual travel route. The emotion engine adjusts the route based on the user's emotional state, allowing the user to enjoy a comfortable travel experience.

[0871] As a result, the system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] [Third embodiment]

[0876] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0877] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0878] 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).

[0879] 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.

[0880] 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.

[0881] 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).

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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."

[0888] This invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI. The following describes an embodiment of the system and the processing of its program in natural language.

[0889] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0890] server

[0891] 1. Data Collection

[0892] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time, ensuring that the latest information is always available.

[0893] 2. Data Preprocessing

[0894] The server stores the collected data in a database and performs preprocessing such as data cleaning, normalization, and feature extraction. This preprocessing improves the quality of the data and makes it suitable for analysis.

[0895] 3. AI analysis

[0896] Based on the pre-processed data, the server calculates the optimal route using an AI model that takes into account various traffic conditions, congestion, event information, etc. to predict the most efficient route for the user.

[0897] 4. API provided

[0898] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other Mobility as a Service (MaaS) systems.

[0899] Terminal

[0900] 1. User Request

[0901] The device receives requests from users through a smartphone app and sends their current location and destination to the server.

[0902] 2. Route display

[0903] The device receives the optimal route information from the server and displays it to the user, allowing the user to efficiently reach their destination.

[0904] User

[0905] 1. Submit a request

[0906] The user uses the device to request a ride-sharing service by inputting their current location and destination, and the system searches for the optimal route.

[0907] 2. Route confirmation and movement

[0908] Users can travel by following the optimal route displayed on the device, which can save time and reduce costs.

[0909] Specific examples

[0910] A user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0911] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app.

[0912] 2. The device sends this information to the server.

[0913] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0914] 4. The calculated route information is sent back from the server to the device.

[0915] 5. The device displays the optimal route to the user, who then follows the route to their destination, the shopping mall.

[0916] In this way, the system of the present invention provides users with a fast and efficient means of transportation, complements the lack of transportation in rural and depopulated areas, and enables the provision of sustainable transportation.

[0917] The processing flow will be explained below.

[0918] Step 1:

[0919] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time from various sensors and databases. This data collection is performed automatically at regular intervals, and is set to always obtain the latest information.

[0920] Step 2:

[0921] The server stores the collected data in a database, which is used for subsequent preprocessing.

[0922] Step 3:

[0923] The server preprocesses the stored data, cleaning it to remove incomplete data and noise, normalizing it to convert the scale of the data into a consistent format, and extracting the features required for analysis to prepare it for efficient analysis by the AI ​​model.

[0924] Step 4:

[0925] The server uses an artificial intelligence model to analyze the preprocessed data. Specifically, it calculates the optimal route by taking into account traffic conditions, congestion information, event information, etc. The AI ​​model predicts supply and demand based on past and current data, and calculates the optimal travel route for the user.

[0926] Step 5:

[0927] The server provides the optimal route information, which is the result of the analysis, via a public API. In response to requests from external systems and applications, the server responds with the optimal route information in real time. Using this API, it is possible to link with other MaaS systems and third-party applications.

[0928] Step 6:

[0929] The device receives requests from users via a smartphone app and sends information about their current location and destination to the server.

[0930] Step 7:

[0931] The server recalculates the optimal route based on the current location and destination information received from the device and returns the analysis results to the device.

[0932] Step 8:

[0933] The terminal displays the optimal route information received from the server to the user, thereby enabling the user to travel efficiently.

[0934] Step 9:

[0935] The user heads to the destination by following the optimal route displayed on the terminal, thereby quickly reaching the destination.

[0936] Example 1

[0937] 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."

[0938] Conventional ride-sharing services face challenges in finding the optimal route to reach a destination efficiently and quickly. In particular, it is difficult to optimize routes that take into account real-time changes in traffic conditions and event information, which can lead to lower user satisfaction. Furthermore, due to the lack of adequate transportation options in rural and depopulated areas, there is a need for sustainable transportation services.

[0939] 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.

[0940] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for transmitting current location and destination information to the server based on a user request, and means for displaying the optimal route information received from the server on the user terminal. This allows users to obtain optimal route information that is updated in real time and reach their destination efficiently and quickly. Furthermore, it is possible to provide appropriate transportation options even in rural and depopulated areas, thereby realizing sustainable transportation services.

[0941] "Real-time data" refers to data that indicates the current situation or state, and is available at approximately the same time as it is collected.

[0942] "Preprocessing" is the process of preparing collected data in a form that is easier to analyze, and includes processes such as cleaning, normalization, and feature extraction.

[0943] An "optimal route" is a route that allows a user to reach a destination most efficiently under certain conditions, and is calculated taking into account time, cost, traffic conditions, and the like.

[0944] A "public API" is a published interface available to external systems and applications, providing a means to access specific functionality or data.

[0945] A "user terminal" refers to an electronic device used by a user, including a smartphone, tablet, etc.

[0946] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions and analyses for specific tasks.

[0947] A "prompt" is a piece of text or command that is input to a generative AI model and serves as instructions for the AI ​​to generate output.

[0948] "GPS Data" means geographical location information data obtained using the Global Positioning System.

[0949] "Map information" is data that includes geographical information such as topography, roads, buildings, and facilities, and is used for navigation and location-based services.

[0950] "Aerial photography" refers to images of the Earth's surface taken from aircraft or satellites and is used for advanced geographic analysis and mapping.

[0951] "Congestion information for surrounding facilities" is data that indicates the current congestion situation at a specific facility or area, and is information that affects the movement and behavior of users.

[0952] "Local event information" is data relating to events taking place in a specific area, and includes information such as time, location, and content.

[0953] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and generative AI models. An embodiment of the system and the processing of its program are described below.

[0954] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and public API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[0955] server

[0956] The server has the following functions:

[0957] 1. Data Collection

[0958] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information about nearby events in real time. A general online map API (e.g., Google Maps API) is used for map information. Event information and congestion status are also acquired by linking with various sensors and online databases.

[0959] 2. Data Preprocessing

[0960] The server stores the collected data in a database and performs cleaning (removing outliers), normalization (standardizing the data format), and feature extraction (extracting important elements), improving the quality of the data and making it suitable for analysis.

[0961] 3. AI analysis

[0962] Based on the preprocessed data, the server uses a generative AI model to calculate the optimal route. This AI model inputs complex data such as traffic information, congestion status, and event information to predict the most efficient route for the user. The generative AI model uses libraries such as PyTorch and TensorFlow.

[0963] 4. API provided

[0964] The server accepts requests from external systems and applications through a public API and responds with the optimal route, making the system compatible with other Mobility as a Service (MaaS) systems.

[0965] Terminal

[0966] The terminal has the following features:

[0967] 1. User Request

[0968] The device receives requests from users and sends their current location and destination to the server. The request is made through a smartphone app. The device can automatically obtain location information using GPS, eliminating the need for users to enter their location information.

[0969] 2. Route display

[0970] The device interprets the optimal route information received from the server and displays it to the user. The device provides the information as an easy-to-understand map display and uses a large screen and voice guidance to guide the user to the optimal route.

[0971] User

[0972] The user performs the following activities:

[0973] 1. Submit a request

[0974] Users use the device to request a ride-sharing service by inputting their current location and destination and searching for the best route. Requests are made through a simple user interface, and can be made either by voice or manual input.

[0975] 2. Route confirmation and movement

[0976] Users can travel by following the optimal route displayed on their device, which saves time and money, and also helps avoid problems such as traffic congestion.

[0977] Specific examples

[0978] The following example illustrates a scenario where a user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[0979] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app. The user can use voice input or manual input.

[0980] 2. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0981] 3. The server uses a generative AI model to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[0982] 4. The calculated route information is sent back from the server to the device.

[0983] 5. The device displays the optimal route to the user, who then follows that route to their destination, the shopping mall.

[0984] An example of a prompt sentence to be input into the generative AI model is, "I'm currently at home and my destination is a nearby shopping mall. Please tell me the best route."

[0985] In this way, the system of the present invention not only provides users with a fast and efficient means of transportation, but also complements the lack of transportation in rural and depopulated areas, thereby realizing the provision of sustainable transportation services.

[0986] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0987] Step 1: Data collection

[0988] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time.

[0989] Specific behavior:

[0990] The server periodically acquires the vehicle's GPS data and stores the location information in a database.

[0991] Map information is obtained using online map APIs, such as Google Maps API to obtain road information and terrain data.

[0992] Aerial photographs are downloaded from a satellite image database to maintain up-to-date surface information.

[0993] Congestion information at nearby facilities is obtained by synchronizing with sensor data provided by each facility and online databases.

[0994] Information about surrounding events is collected regularly from social networks and event management systems.

[0995] Input: Various data sources (GPS system, map API, satellite image database, facility sensor, SNS)

[0996] Output: A database of collected data

[0997] Step 2: Data Preprocessing

[0998] The server stores the collected data in a database and performs data cleaning, normalization, and feature extraction.

[0999] Specific behavior:

[1000] The server uses scripts to clean the data stored in the database and fill in any outliers or missing parts.

[1001] Normalization processing unifies data formats and ensures consistency.

[1002] Important features such as traffic signals, road width, and congestion status are extracted to generate a dataset suitable for AI analysis.

[1003] Input: Raw data collected

[1004] Output: Preprocessed dataset

[1005] Step 3: AI analysis

[1006] The server uses the pre-processed data to calculate the optimal route using a generative AI model.

[1007] Specific behavior:

[1008] The server inputs the preprocessed data into a generative AI model (e.g., PyTorch, TensorFlow) and executes the model.

[1009] Based on the input data, the generative AI model calculates the expected travel time and cost for each route and predicts the most efficient route for the user.

[1010] The model results are saved in a database and prepared for API provision.

[1011] Input: Preprocessed dataset

[1012] Output: Optimal route information (estimated travel time and cost)

[1013] Step 4: Providing API

[1014] The server provides optimal route information in response to requests from external systems and applications.

[1015] Specific behavior:

[1016] The server analyzes the API request and searches for the optimal route based on the requested current location and destination information.

[1017] The optimal route information found is returned to external systems and applications.

[1018] Input: Requests from external systems and applications

[1019] Output: Optimal route information (API response)

[1020] Step 5: User Request

[1021] The terminal receives a request from the user and sends the current location and destination to the server.

[1022] Specific behavior:

[1023] The device automatically obtains the user's current location information using the GPS function.

[1024] The user enters their destination into the device and taps the send request button.

[1025] The device sends the current location and destination information to the server.

[1026] Input: User's current location and destination information

[1027] Output: Request sent to server

[1028] Step 6: View Route

[1029] The terminal displays the optimal route information received from the server to the user.

[1030] Specific behavior:

[1031] The terminal interprets the optimum route information received from the server and displays it in an easy-to-understand manner on the user interface.

[1032] The device provides voice guidance and visual map displays to help users navigate to their destinations.

[1033] Input: Optimal route information from the server

[1034] Output: Show route to user

[1035] Step 7: Move and update in real time

[1036] Users follow the optimal route displayed on the device, and information is updated in real time while they are traveling.

[1037] Specific behavior:

[1038] The device periodically communicates with the server to obtain real-time traffic and event information.

[1039] If the route changes, the terminal notifies the user and recalculates and displays the optimal route.

[1040] Input: Real-time traffic and event information

[1041] Output: Constantly updated optimal route information

[1042] (Application example 1)

[1043] 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."

[1044] Currently, parcel delivery management at logistics centers is often inefficient because delivery priority and traffic conditions are not fully considered. This results in delays in delivery time and increased costs, which is a problem. These issues are particularly serious in rural and depopulated areas where transportation options are limited.

[1045] 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.

[1046] In this invention, the server includes means for collecting real-time data, means for preprocessing the data, means for analyzing the preprocessed data using an AI model to calculate an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for managing package deliveries at the logistics center, and means for providing a delivery route that takes into account local traffic information and the priority of delivery destinations. This significantly improves package delivery efficiency at the logistics center, enabling shorter delivery times and reduced costs.

[1047] "Real-time data" refers to up-to-date information that reflects ongoing events and situations.

[1048] "Data preprocessing" refers to the process of preparing collected data in a form suitable for analysis through processes such as cleaning, normalization, and feature extraction.

[1049] An "artificial intelligence model" refers to a program that is trained by machine learning algorithms to automatically perform a specific task (in this case, calculating the optimal route).

[1050] An "optimal route" refers to the route that most efficiently reaches a destination under certain conditions.

[1051] "User terminal" refers to a device (e.g., smartphone, tablet) that a user operates directly to use a service.

[1052] A "public API" refers to a programmatic interface that is accessible to external systems and applications.

[1053] A "logistics center" refers to a facility that carries out logistics operations such as consolidating, storing, and shipping cargo.

[1054] "Delivery management" refers to the process of efficiently planning and executing a series of tasks from package receipt to final delivery.

[1055] "Traffic information" refers to information that affects traffic flow, such as road congestion, traffic accidents, and construction work.

[1056] "Delivery destination priority" refers to an indicator that indicates the urgency or importance of the delivery of the package.

[1057] "Collection means" refers to methods and devices for collecting data through various sensors, the Internet, etc.

[1058] "Providing means" refers to a method or device for providing information to a user terminal or other system.

[1059] This invention is a system for improving the efficiency of package delivery in a logistics center. The system is composed of three main components: a server, a terminal, and a user, and will be described in detail below.

[1060] server

[1061] 1. Real-time data collection methods

[1062] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and delivery priority information in real time, ensuring that the latest information needed to calculate delivery routes is always available.

[1063] 2. Data preprocessing methods

[1064] The server stores the collected data in a database and performs preprocessing such as cleaning, normalization, and feature extraction, which improves the quality of the data and makes it suitable for analysis.

[1065] 3. Analysis methods using artificial intelligence models

[1066] Based on the preprocessed data, the server uses an artificial intelligence model (e.g., a model using TensorFlow or PyTorch) to calculate the optimal route. This AI model comprehensively evaluates traffic information, congestion status, delivery destination priority, etc. to calculate the most efficient delivery route.

[1067] 4. Public API Provision Method

[1068] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other logistics systems.

[1069] Terminal

[1070] 1. User request reception method

[1071] The device receives requests from delivery drivers via a smartphone app and sends the current location and delivery destination information to the server.

[1072] 2. Route display method

[1073] The terminal receives the optimal route information received from the server and displays it to the delivery driver, enabling efficient delivery.

[1074] User

[1075] 1. Request sending method

[1076] Delivery drivers use the device to enter their current location and delivery destination information and request the optimal route.

[1077] 2. Route confirmation and delivery

[1078] Delivery drivers follow the optimal route displayed on the terminal, which shortens delivery times and reduces costs.

[1079] Specific examples

[1080] For example, if a driver delivers packages from their current location (Tokyo Station) to multiple destinations (Shibuya Station, Shinjuku Station), the following steps are taken:

[1081] 1. The driver enters their current location and delivery destination into a smartphone app.

[1082] 2. The device sends this information to the server.

[1083] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, delivery priority, etc.

[1084] 4. The calculated route information is sent back from the server to the terminal and displayed to the driver.

[1085] 5. The driver follows the route to the desired delivery location.

[1086] Prompt Sentence Examples

[1087] For example, use the following prompt:

[1088] "Based on the current location (Tokyo Station) and multiple delivery destinations (Shibuya Station, Shinjuku Station), please use AI to calculate the optimal delivery route taking into account traffic information and congestion."

[1089] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1090] Step 1:

[1091] server

[1092] Real-time data collection

[1093] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and package delivery priority information in real time. Data from each sensor and the Internet is used as input. This data is sent to the server and stored in a database. The output is raw data stored in the database.

[1094] Step 2:

[1095] server

[1096] Data Preprocessing

[1097] The server performs preprocessing on the collected data, including cleaning, normalization, and feature extraction. The raw data collected in step 1 is used as input. The data is processed to remove imperfections and make it consistent. The output is preprocessed data that has been organized into a form suitable for analysis.

[1098] Step 3:

[1099] server

[1100] Analysis using artificial intelligence models

[1101] The server uses the preprocessed data to calculate the optimal delivery route using an artificial intelligence model (e.g., a model using TensorFlow or PyTorch). The preprocessed data is used as input. The AI ​​model comprehensively evaluates delivery priority, real-time traffic information, congestion status, etc., and calculates the most efficient route. The output is optimal route information.

[1102] Step 4:

[1103] server

[1104] Route information provided via public API

[1105] The server provides the optimal route information to the terminal through a public API. The optimal route information calculated in step 3 is used as input. The server receives an API request and returns the corresponding optimal route information. The output is a response to the API request with the optimal route information.

[1106] Step 5:

[1107] Terminal

[1108] Submitting a User Request

[1109] The terminal receives a request from the delivery driver and sends the current location and delivery destination information to the server. The current location and delivery destination information entered by the driver on the smartphone app are used as input. The request is then sent to the server. The output is the request data sent to the server.

[1110] Step 6:

[1111] Terminal

[1112] View route information

[1113] The terminal displays the optimal route information received from the server to the driver. The optimal route information from the server is used as input. A process is performed to visually display the route information. The output is the route information that can be visually viewed by the driver.

[1114] Step 7:

[1115] User

[1116] Entering a request

[1117] The delivery driver uses a smartphone app to input their current location and delivery destination information and request the optimal route. As input, they manually enter their current location and delivery destination information into the app. The request is processed through the app. The output is the request data entered into the terminal.

[1118] Step 8:

[1119] User

[1120] Route confirmation and delivery

[1121] The delivery driver makes the delivery by following the optimal route displayed on the smartphone app. The optimal route information displayed on the terminal is used as input. The driver follows the route to the destination. The output is the delivery completed.

[1122] 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.

[1123] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI analysis, and also to a system that combines an emotion engine that recognizes user emotions. An embodiment of the system and the processing of its program are explained below in natural language.

[1124] server

[1125] 1. Data Collection

[1126] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals, ensuring that the latest information is always available.

[1127] 2. Data Preprocessing

[1128] The server stores the collected data in a database and pre-processes it. Data cleaning removes incomplete data and noise, and normalization converts the scale of the data into a consistent format. Emotion data is also pre-processed in the same way to make it suitable for analysis.

[1129] 3. AI analysis

[1130] Based on the pre-processed data, the server calculates the optimal route using an artificial intelligence model that takes into account traffic conditions, congestion information, and event information, and also uses emotion data provided by an emotion engine to predict the most comfortable route for the user.

[1131] 4. API provided

[1132] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1133] Terminal

[1134] 1. User Request

[1135] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. Requests are typically made through smartphone apps.

[1136] 2. Route display

[1137] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[1138] User

[1139] 1. Submit a request

[1140] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[1141] 2. Route confirmation and movement

[1142] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1143] Specific examples

[1144] Consider a scenario where a user uses a smartphone app to book a ride from their home (current location) to their workplace (destination):

[1145] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[1146] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[1147] 3. The server uses AI analysis to calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion information at nearby facilities, event information, emotional data, etc.

[1148] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[1149] 5. The user follows the directions on the device to reach their workplace quickly and via a less stressful route.

[1150] In this way, the system of the present invention proposes optimal routes taking into account the user's emotional data, enabling comfortable and efficient travel. This will alleviate the shortage of transportation options in rural and depopulated areas, and provide a sustainable means of transportation while reducing user stress and anxiety.

[1151] The processing flow will be explained below.

[1152] Step 1:

[1153] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This data is acquired in real time through various sensors and online databases. The server also receives user emotion data from the device.

[1154] Step 2:

[1155] The server stores the collected data in a database. At the same time, it preprocesses the data. Specifically, it cleans the data to remove incomplete data and noise. Next, it normalizes the data to convert data of different scales into a consistent format. After that, it extracts features to extract the information necessary for data analysis. Emotion data is also preprocessed in the same way.

[1156] Step 3:

[1157] The server uses the preprocessed data to perform analysis using an AI model. The AI ​​model combines and analyzes traffic conditions, congestion information, event information, emotional data, and other data to calculate the optimal route. By incorporating emotional data, the system selects a route that reduces the user's stress and anxiety.

[1158] Step 4:

[1159] After calculating the optimal route information, the server provides this data to external parties via a public API that external systems and applications can use to return route information in real time upon request.

[1160] Step 5:

[1161] The device receives a request from the user and sends information about the current location, destination, and emotion data to the server. The user makes this request using a smartphone app.

[1162] Step 6:

[1163] The server recalculates the optimal route based on the current location, destination information, and emotion data received from the device, and sends the results back to the device, providing the optimal route based on the latest data.

[1164] Step 7:

[1165] The terminal displays the optimal route information received from the server to the user, the route information being adjusted based on the user's emotional state.

[1166] Step 8:

[1167] The user travels according to the optimal route displayed on the device. By utilizing the emotion engine, the user can reduce stress and reach their destination comfortably.

[1168] Example 2

[1169] 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."

[1170] Conventional ride-sharing services calculate optimal routes using real-time data, but do not take the user's emotional state into account. As a result, users experiencing high levels of stress or fatigue may find their trips uncomfortable. Furthermore, route suggestions that take into account event information and the congestion status of nearby facilities are lacking, creating a need to improve the quality of the travel experience. The present invention aims to solve these problems by proposing more comfortable routes based on the user's emotional state.

[1171] 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.

[1172] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns an optimal route, means for collecting, integrating, and analyzing user emotion data, and means for evaluating the comfort of the route based on the emotion data. This makes it possible to provide a comfortable travel route that reduces stress and fatigue based on the user's emotional state.

[1173] "Real-time data" refers to data that is collected instantaneously by the system and is immediately available for use.

[1174] "Collection" is the act of gathering the required data or information in a certain way.

[1175] "Preprocessing" refers to the process of cleaning, normalizing, and other operations to prepare collected data in a form that is easier to analyze.

[1176] "Analysis" is the process of drawing conclusions and insights from data using algorithms and artificial intelligence.

[1177] An "optimal route" is the most efficient and appropriate route from one point to another in terms of time, distance, comfort, etc.

[1178] A "user terminal" is a device that a user operates to send and receive information, and typically refers to a smartphone or tablet.

[1179] A "public API" is a publicly available application programming interface, a program interface designed to be available to external systems and applications.

[1180] "Emotion data" is information that reflects the user's emotional state, such as data indicating stress level or fatigue level.

[1181] A "machine learning model" is a collection of algorithms that learn from data and perform tasks such as prediction and classification based on that data.

[1182] "Optimization" is the process of making adjustments to achieve the best possible results within conditions and constraints in order to achieve a specific goal.

[1183] MODE FOR CARRYING OUT THE INVENTION

[1184] This invention relates to a system that uses real-time data and AI analysis to improve the convenience of ride-sharing services, as well as a system that combines an emotion engine that recognizes user emotions.

[1185] server

[1186] The server performs the following functions:

[1187] 1. Data Collection

[1188] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals. Specifically, data is obtained from external services such as Google Maps API, OpenStreetMap, and Eventbrite API.

[1189] 2. Data Preprocessing

[1190] The server stores the collected data in a database and performs data cleaning and normalization using an SQL database and the Python pandas library. This removes incomplete data and noise, and converts the data scale into a consistent format. Sentiment data is also preprocessed in the same way.

[1191] 3. AI analysis

[1192] Based on the preprocessed data, the server uses machine learning models such as TensorFlow and PyTorch to calculate the optimal route, taking into account traffic conditions, congestion information, and event information, as well as emotional data provided by the emotion engine, to predict the most comfortable route for the user.

[1193] 4. API provided

[1194] The server accepts requests from external systems and applications through a Restful API and provides real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1195] Terminal

[1196] 1. User Request

[1197] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends it to the server. Requests are typically made through a smartphone app.

[1198] 2. Route display

[1199] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving comfort.

[1200] User

[1201] 1. Submit a request

[1202] The user inputs their current location and destination using a smartphone app to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to a server.

[1203] 2. Route confirmation and movement

[1204] Users travel according to the optimal route displayed on their device, and the emotion engine also provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1205] Specific examples

[1206] Consider a scenario where a user uses a smartphone app to book a ride from home to work:

[1207] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[1208] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[1209] 3. The server uses AI analysis to calculate the optimal route based on location information, traffic conditions, facility usage information, event information, and emotional data collected in real time.

[1210] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[1211] 5. The user follows the instructions on the device to reach their workplace quickly and via a less stressful route.

[1212] In this way, the system of the present invention proposes an optimal route taking into consideration the user's emotional data, enabling comfortable and efficient travel.

[1213] ※Example prompt:

[1214] "User A wants to book a ride from home to work and is looking for a short, low-stress route. Current location: 'Home Address', Destination: 'Work Address', Emotional state: 'High Stress'. Let the AI ​​suggest the optimal route."

[1215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1216] Step 1: Data collection

[1217] The server collects real-time GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This uses Google Maps API, OpenStreetMap, aerial photograph API, congestion information API, event information API, etc. The server collects and integrates the data obtained from these APIs. The input is data from each external API, and the output is integrated real-time data.

[1218] Step 2: Collecting Emotional Data

[1219] The device collects the user's emotional data, such as stress level and fatigue level, obtained from a smartphone or wearable device. The device then transmits this data to a server. The input is the emotional data, and the output is the integrated emotional data transmitted to the server.

[1220] Step 3: Data Preprocessing

[1221] The server stores the collected data in a database and performs data cleaning and normalization processes to remove incomplete data and noise, and convert the data scale into a consistent format. Sentiment data is also preprocessed in the same way. The input is raw data, and the output is preprocessed clean data.

[1222] Step 4: Emotion data preprocessing

[1223] The server receives the user's emotion data sent from the device and stores it in a database. This data is also cleaned and normalized to form a consistent format. The input is raw emotion data, and the output is preprocessed emotion data.

[1224] Step 5: Calculate the optimal route

[1225] The server inputs the preprocessed data into machine learning models such as TensorFlow and PyTorch. The optimal route is calculated based on traffic conditions, congestion information, event information, and emotion data. The input is the preprocessed clean data and emotion data, and the output is the optimal route information.

[1226] Step 6: Accept API requests

[1227] The server accepts requests from external systems and applications via a Restful API. The input is the request from the external system, and the output is the result of the optimal route calculation.

[1228] Step 7: Providing optimal route information

[1229] The server provides optimal route information to external systems and terminals, allowing users to receive optimal route information in real time. The input is optimal route information, and the output is route information provided to external systems and terminals.

[1230] Step 8: Receiving a User Request

[1231] The user inputs their current location and destination using a smartphone app. At the same time, the user's emotional data is also collected and sent from the device to the server. The input is the user's location information and emotional data, and the output is a request sent to the server.

[1232] Step 9: View Route Information

[1233] The terminal displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently. Route suggestions that take into account data from the emotion engine are also displayed. The input is optimal route information from the server, and the output is route information displayed to the user.

[1234] (Application example 2)

[1235] 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."

[1236] Conventional ride-sharing services do not select routes that take into account the user's actual emotional state during the trip, and do not sufficiently consider the user's psychological comfort. This results in increased stress and fatigue during the trip, resulting in a poor user experience. Furthermore, optimal route calculations that reflect real-time changes in traffic conditions and congestion information at nearby facilities are also inadequate. A new system is needed to solve these issues and realize more comfortable and efficient travel.

[1237] The specific processing by the specific 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 collecting real-time data, means for pre-processing the collected data, means for analyzing the pre-processed data and calculating an optimal route, means for providing the calculation results to the user terminal, means for accepting requests from outside and providing a public API that returns an optimal route, means for recognizing the user's emotional state and collecting emotional data, means for calculating an optimal route including the emotional data, and means for presenting a route adjusted based on the emotional state. This makes it possible to select a comfortable route that takes the user's emotional state into consideration, reducing stress during travel and providing a better user experience.

[1238] "Means for collecting real-time data" refers to a device or method for acquiring GPS data, map information, aerial photographs, congestion information on nearby facilities, information on nearby events, and user emotion data in real time.

[1239] "Means for pre-processing collected data" refers to devices or methods that perform data cleaning, noise removal, and normalization processes to convert raw data into a form suitable for analysis.

[1240] "Means for analyzing pre-processed data and calculating optimal routes" means a device or method that uses an artificial intelligence model to calculate the most efficient and safest travel route from the pre-processed data.

[1241] The "means for providing the calculation results to the user terminal" refers to a device or method for transmitting the calculated optimum route information to the user's device such as a smartphone or smart glasses in real time and displaying it.

[1242] "A means of providing a public API that accepts requests from outside and responds with the optimal route" is a public interface that provides optimal route information in real time in response to requests from other systems or applications.

[1243] "Means for recognizing a user's emotional state and collecting emotional data" refers to a device or method that uses emotion recognition technology to obtain a user's emotional state (e.g., stress level, fatigue level) and collects this as data.

[1244] The "means for calculating an optimal route that takes into account emotional data" refers to a device or method that takes into account the emotional state of the user and integrates it with conventional data to calculate the most comfortable and efficient travel route.

[1245] A "means for presenting a route adjusted based on emotional state" is a device or method that displays an optimal route adjusted based on the user's emotional state on the user's device and provides real-time guidance.

[1246] This invention is a system that improves the convenience of ride-sharing services by using real-time data and artificial intelligence analysis, and combines an emotion engine that recognizes user emotions. This system consists of three main components: a server, a terminal, and a user.

[1247] server

[1248] 1. Data Collection

[1249] The server collects GPS data, map information, aerial photographs, information on the congestion status of nearby facilities, and information on nearby events in real time. It also collects user emotion data using an emotion engine. This process is performed automatically at regular intervals, ensuring that the latest information is always available.

[1250] 2. Data Preprocessing

[1251] The server stores the collected data in a database and preprocesses it. It cleans and removes noise from the data, and normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way to make it suitable for analysis.

[1252] 3. AI analysis

[1253] Based on the pre-processed data, the server calculates the optimal route using a generative AI model that takes into account traffic conditions, congestion information, event information, and sentiment data to predict the most comfortable and efficient route for the user.

[1254] 4. API provided

[1255] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1256] Terminal

[1257] 1. User Request

[1258] The device receives a request from the user and sends information about the current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. This request is usually made through a smartphone app.

[1259] 2. Route display

[1260] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[1261] User

[1262] 1. Submit a request

[1263] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[1264] 2. Route confirmation and movement

[1265] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1266] Hardware and Software Used

[1267] Hardware: Smart glasses (general name), head-mounted display (general name)

[1268] Software: Emotion recognition engine (any software for recognizing emotional states), public API (any interface for sending and receiving real-time data)

[1269] Prompt Sentence Examples

[1270] Current location: "35.6895,139.6917" (Tokyo Station)

[1271] Destination: "35.6580,139.7514" (Tokyo Tower)

[1272] User's emotional state: "stressed"

[1273] This system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[1274] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1275] Step 1:

[1276] Data collection

[1277] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, information on nearby events, and user sentiment data in real time. The latest data is obtained from external data sources at specified intervals through the data collection API. The input is data from the API, and the output is raw data stored in a database.

[1278] Step 2:

[1279] Data Preprocessing

[1280] The server performs data cleaning to remove incomplete data and noise from the collected raw data, then normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way. The input is raw data stored in a database, and the output is preprocessed analysis data.

[1281] Step 3:

[1282] AI analysis

[1283] The server inputs the preprocessed data into a generative AI model to calculate the optimal route based on traffic conditions, congestion information, event information, and emotional data. The input is the preprocessed analytical data, and the output is the calculation result of the optimal route. In this process, the devised optimal route is customized by taking into account the user's current emotional state.

[1284] Step 4:

[1285] Request received

[1286] The device receives a request from the user (current location, destination, and emotion data) and sends it to the server. The input is the data entered by the user into the device, and the output is the request data to the server. In this step, the emotion recognition engine obtains the user's emotion data.

[1287] Step 5:

[1288] Providing route information

[1289] The server calculates the optimal route information for the request based on the received request data and returns it to the device. The input is the request data received from the device, and the output is the route information to the device. This information is provided in real time via the API.

[1290] Step 6:

[1291] Route display

[1292] The terminal displays the optimal route information received from the server to the user. The input is the route information from the server, and the output is the route guidance displayed to the user. The display is performed via the user's device, such as smart glasses or a head-mounted display.

[1293] Step 7:

[1294] User Movement

[1295] The user travels according to the optimal route displayed on the device. The input is the displayed route guidance, and the output is the user's actual travel route. The emotion engine adjusts the route based on the user's emotional state, allowing the user to enjoy a comfortable travel experience.

[1296] As a result, the system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] [Fourth embodiment]

[1301] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1302] 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.

[1303] 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).

[1304] 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.

[1305] 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.

[1306] 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).

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] 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."

[1314] This invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI. The following describes an embodiment of the system and the processing of its program in natural language.

[1315] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[1316] server

[1317] 1. Data Collection

[1318] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time, ensuring that the latest information is always available.

[1319] 2. Data Preprocessing

[1320] The server stores the collected data in a database and performs preprocessing such as data cleaning, normalization, and feature extraction. This preprocessing improves the quality of the data and makes it suitable for analysis.

[1321] 3. AI analysis

[1322] Based on the pre-processed data, the server calculates the optimal route using an AI model that takes into account various traffic conditions, congestion, event information, etc. to predict the most efficient route for the user.

[1323] 4. API provided

[1324] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other Mobility as a Service (MaaS) systems.

[1325] Terminal

[1326] 1. User Request

[1327] The device receives requests from users through a smartphone app and sends their current location and destination to the server.

[1328] 2. Route display

[1329] The device receives the optimal route information from the server and displays it to the user, allowing the user to efficiently reach their destination.

[1330] User

[1331] 1. Submit a request

[1332] The user uses the device to request a ride-sharing service by inputting their current location and destination, and the system searches for the optimal route.

[1333] 2. Route confirmation and movement

[1334] Users can travel by following the optimal route displayed on the device, which can save time and reduce costs.

[1335] Specific examples

[1336] A user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[1337] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app.

[1338] 2. The device sends this information to the server.

[1339] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[1340] 4. The calculated route information is sent back from the server to the device.

[1341] 5. The device displays the optimal route to the user, who then follows the route to their destination, the shopping mall.

[1342] In this way, the system of the present invention provides users with a fast and efficient means of transportation, complements the lack of transportation in rural and depopulated areas, and enables the provision of sustainable transportation.

[1343] The processing flow will be explained below.

[1344] Step 1:

[1345] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events in real time from various sensors and databases. This data collection is performed automatically at regular intervals, and is set to always obtain the latest information.

[1346] Step 2:

[1347] The server stores the collected data in a database, which is used for subsequent preprocessing.

[1348] Step 3:

[1349] The server preprocesses the stored data, cleaning it to remove incomplete data and noise, normalizing it to convert the scale of the data into a consistent format, and extracting the features required for analysis to prepare it for efficient analysis by the AI ​​model.

[1350] Step 4:

[1351] The server uses an artificial intelligence model to analyze the preprocessed data. Specifically, it calculates the optimal route by taking into account traffic conditions, congestion information, event information, etc. The AI ​​model predicts supply and demand based on past and current data, and calculates the optimal travel route for the user.

[1352] Step 5:

[1353] The server provides the optimal route information, which is the result of the analysis, via a public API. In response to requests from external systems and applications, the server responds with the optimal route information in real time. Using this API, it is possible to link with other MaaS systems and third-party applications.

[1354] Step 6:

[1355] The device receives requests from users via a smartphone app and sends information about their current location and destination to the server.

[1356] Step 7:

[1357] The server recalculates the optimal route based on the current location and destination information received from the device and returns the analysis results to the device.

[1358] Step 8:

[1359] The terminal displays the optimal route information received from the server to the user, thereby enabling the user to travel efficiently.

[1360] Step 9:

[1361] The user heads to the destination by following the optimal route displayed on the terminal, thereby quickly reaching the destination.

[1362] Example 1

[1363] 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."

[1364] Conventional ride-sharing services face challenges in finding the optimal route to reach a destination efficiently and quickly. In particular, it is difficult to optimize routes that take into account real-time changes in traffic conditions and event information, which can lead to lower user satisfaction. Furthermore, due to the lack of adequate transportation options in rural and depopulated areas, there is a need for sustainable transportation services.

[1365] 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.

[1366] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for transmitting current location and destination information to the server based on a user request, and means for displaying the optimal route information received from the server on the user terminal. This allows users to obtain optimal route information that is updated in real time and reach their destination efficiently and quickly. Furthermore, it is possible to provide appropriate transportation options even in rural and depopulated areas, thereby realizing sustainable transportation services.

[1367] "Real-time data" refers to data that indicates the current situation or state, and is available at approximately the same time as it is collected.

[1368] "Preprocessing" is the process of preparing collected data in a form that is easier to analyze, and includes processes such as cleaning, normalization, and feature extraction.

[1369] An "optimal route" is a route that allows a user to reach a destination most efficiently under certain conditions, and is calculated taking into account time, cost, traffic conditions, and the like.

[1370] A "public API" is a published interface available to external systems and applications, providing a means to access specific functionality or data.

[1371] A "user terminal" refers to an electronic device used by a user, including a smartphone, tablet, etc.

[1372] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and makes predictions and analyses for specific tasks.

[1373] A "prompt" is a piece of text or command that is input to a generative AI model and serves as instructions for the AI ​​to generate output.

[1374] "GPS Data" means geographical location information data obtained using the Global Positioning System.

[1375] "Map information" is data that includes geographical information such as topography, roads, buildings, and facilities, and is used for navigation and location-based services.

[1376] "Aerial photography" refers to images of the Earth's surface taken from aircraft or satellites and is used for advanced geographic analysis and mapping.

[1377] "Congestion information for surrounding facilities" is data that indicates the current congestion situation at a specific facility or area, and is information that affects the movement and behavior of users.

[1378] "Local event information" is data relating to events taking place in a specific area, and includes information such as time, location, and content.

[1379] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and generative AI models. An embodiment of the system and the processing of its program are described below.

[1380] The system of the present invention mainly consists of three entities: a server, a terminal, and a user. The server is responsible for data collection, data preprocessing, AI analysis, optimal route calculation, and public API provision. The terminal receives requests from users and displays the optimal route. Users use the terminal to use the ride-sharing service.

[1381] server

[1382] The server has the following functions:

[1383] 1. Data Collection

[1384] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, and information about nearby events in real time. A general online map API (e.g., Google Maps API) is used for map information. Event information and congestion status are also acquired by linking with various sensors and online databases.

[1385] 2. Data Preprocessing

[1386] The server stores the collected data in a database and performs cleaning (removing outliers), normalization (standardizing the data format), and feature extraction (extracting important elements), improving the quality of the data and making it suitable for analysis.

[1387] 3. AI analysis

[1388] Based on the preprocessed data, the server uses a generative AI model to calculate the optimal route. This AI model inputs complex data such as traffic information, congestion status, and event information to predict the most efficient route for the user. The generative AI model uses libraries such as PyTorch and TensorFlow.

[1389] 4. API provided

[1390] The server accepts requests from external systems and applications through a public API and responds with the optimal route, making the system compatible with other Mobility as a Service (MaaS) systems.

[1391] Terminal

[1392] The terminal has the following features:

[1393] 1. User Request

[1394] The device receives requests from users and sends their current location and destination to the server. The request is made through a smartphone app. The device can automatically obtain location information using GPS, eliminating the need for users to enter their location information.

[1395] 2. Route display

[1396] The device interprets the optimal route information received from the server and displays it to the user. The device provides the information as an easy-to-understand map display and uses a large screen and voice guidance to guide the user to the optimal route.

[1397] User

[1398] The user performs the following activities:

[1399] 1. Submit a request

[1400] Users use the device to request a ride-sharing service by inputting their current location and destination and searching for the best route. Requests are made through a simple user interface, and can be made either by voice or manual input.

[1401] 2. Route confirmation and movement

[1402] Users can travel by following the optimal route displayed on their device, which saves time and money, and also helps avoid problems such as traffic congestion.

[1403] Specific examples

[1404] The following example illustrates a scenario where a user uses a smartphone app to request a ride from their home (current location) to a nearby shopping mall (destination):

[1405] 1. The user enters their current location (home) and destination (shopping mall) into the smartphone app. The user can use voice input or manual input.

[1406] 2. The device sends this information to the server using a secure communication protocol (e.g., HTTPS).

[1407] 3. The server uses a generative AI model to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, etc.

[1408] 4. The calculated route information is sent back from the server to the device.

[1409] 5. The device displays the optimal route to the user, who then follows that route to their destination, the shopping mall.

[1410] An example of a prompt sentence to be input into the generative AI model is, "I'm currently at home and my destination is a nearby shopping mall. Please tell me the best route."

[1411] In this way, the system of the present invention not only provides users with a fast and efficient means of transportation, but also complements the lack of transportation in rural and depopulated areas, thereby realizing the provision of sustainable transportation services.

[1412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1413] Step 1: Data collection

[1414] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time.

[1415] Specific behavior:

[1416] The server periodically acquires the vehicle's GPS data and stores the location information in a database.

[1417] Map information is obtained using online map APIs, such as Google Maps API to obtain road information and terrain data.

[1418] Aerial photographs are downloaded from a satellite image database to maintain up-to-date surface information.

[1419] Congestion information at nearby facilities is obtained by synchronizing with sensor data provided by each facility and online databases.

[1420] Information about surrounding events is collected regularly from social networks and event management systems.

[1421] Input: Various data sources (GPS system, map API, satellite image database, facility sensor, SNS)

[1422] Output: A database of collected data

[1423] Step 2: Data Preprocessing

[1424] The server stores the collected data in a database and performs data cleaning, normalization, and feature extraction.

[1425] Specific behavior:

[1426] The server uses scripts to clean the data stored in the database and fill in any outliers or missing parts.

[1427] Normalization processing unifies data formats and ensures consistency.

[1428] Important features such as traffic signals, road width, and congestion status are extracted to generate a dataset suitable for AI analysis.

[1429] Input: Raw data collected

[1430] Output: Preprocessed dataset

[1431] Step 3: AI analysis

[1432] The server uses the pre-processed data to calculate the optimal route using a generative AI model.

[1433] Specific behavior:

[1434] The server inputs the preprocessed data into a generative AI model (e.g., PyTorch, TensorFlow) and executes the model.

[1435] Based on the input data, the generative AI model calculates the expected travel time and cost for each route and predicts the most efficient route for the user.

[1436] The model results are saved in a database and prepared for API provision.

[1437] Input: Preprocessed dataset

[1438] Output: Optimal route information (estimated travel time and cost)

[1439] Step 4: Providing API

[1440] The server provides optimal route information in response to requests from external systems and applications.

[1441] Specific behavior:

[1442] The server analyzes the API request and searches for the optimal route based on the requested current location and destination information.

[1443] The optimal route information found is returned to external systems and applications.

[1444] Input: Requests from external systems and applications

[1445] Output: Optimal route information (API response)

[1446] Step 5: User Request

[1447] The terminal receives a request from the user and sends the current location and destination to the server.

[1448] Specific behavior:

[1449] The device automatically obtains the user's current location information using the GPS function.

[1450] The user enters their destination into the device and taps the send request button.

[1451] The device sends the current location and destination information to the server.

[1452] Input: User's current location and destination information

[1453] Output: Request sent to server

[1454] Step 6: View Route

[1455] The terminal displays the optimal route information received from the server to the user.

[1456] Specific behavior:

[1457] The terminal interprets the optimum route information received from the server and displays it in an easy-to-understand manner on the user interface.

[1458] The device provides voice guidance and visual map displays to help users navigate to their destinations.

[1459] Input: Optimal route information from the server

[1460] Output: Show route to user

[1461] Step 7: Move and update in real time

[1462] Users follow the optimal route displayed on the device, and information is updated in real time while they are traveling.

[1463] Specific behavior:

[1464] The device periodically communicates with the server to obtain real-time traffic and event information.

[1465] If the route changes, the terminal notifies the user and recalculates and displays the optimal route.

[1466] Input: Real-time traffic and event information

[1467] Output: Constantly updated optimal route information

[1468] (Application example 1)

[1469] 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."

[1470] Currently, parcel delivery management at logistics centers is often inefficient because delivery priority and traffic conditions are not fully considered. This results in delays in delivery time and increased costs, which is a problem. These issues are particularly serious in rural and depopulated areas where transportation options are limited.

[1471] 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.

[1472] In this invention, the server includes means for collecting real-time data, means for preprocessing the data, means for analyzing the preprocessed data using an AI model to calculate an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns the optimal route, means for managing package deliveries at the logistics center, and means for providing a delivery route that takes into account local traffic information and the priority of delivery destinations. This significantly improves package delivery efficiency at the logistics center, enabling shorter delivery times and reduced costs.

[1473] "Real-time data" refers to up-to-date information that reflects ongoing events and situations.

[1474] "Data preprocessing" refers to the process of preparing collected data in a form suitable for analysis through processes such as cleaning, normalization, and feature extraction.

[1475] An "artificial intelligence model" refers to a program that is trained by machine learning algorithms to automatically perform a specific task (in this case, calculating the optimal route).

[1476] An "optimal route" refers to the route that most efficiently reaches a destination under certain conditions.

[1477] "User terminal" refers to a device (e.g., smartphone, tablet) that a user operates directly to use a service.

[1478] A "public API" refers to a programmatic interface that is accessible to external systems and applications.

[1479] A "logistics center" refers to a facility that carries out logistics operations such as consolidating, storing, and shipping cargo.

[1480] "Delivery management" refers to the process of efficiently planning and executing a series of tasks from package receipt to final delivery.

[1481] "Traffic information" refers to information that affects traffic flow, such as road congestion, traffic accidents, and construction work.

[1482] "Delivery destination priority" refers to an indicator that indicates the urgency or importance of the delivery of the package.

[1483] "Collection means" refers to methods and devices for collecting data through various sensors, the Internet, etc.

[1484] "Providing means" refers to a method or device for providing information to a user terminal or other system.

[1485] This invention is a system for improving the efficiency of package delivery in a logistics center. The system is composed of three main components: a server, a terminal, and a user, and will be described in detail below.

[1486] server

[1487] 1. Real-time data collection methods

[1488] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and delivery priority information in real time, ensuring that the latest information needed to calculate delivery routes is always available.

[1489] 2. Data preprocessing methods

[1490] The server stores the collected data in a database and performs preprocessing such as cleaning, normalization, and feature extraction, which improves the quality of the data and makes it suitable for analysis.

[1491] 3. Analysis methods using artificial intelligence models

[1492] Based on the preprocessed data, the server uses an artificial intelligence model (e.g., a model using TensorFlow or PyTorch) to calculate the optimal route. This AI model comprehensively evaluates traffic information, congestion status, delivery destination priority, etc. to calculate the most efficient delivery route.

[1493] 4. Public API Provision Method

[1494] The server accepts requests from external systems and applications through a public API and provides the functionality to respond with the optimal route, making it compatible with other logistics systems.

[1495] Terminal

[1496] 1. User request reception method

[1497] The device receives requests from delivery drivers via a smartphone app and sends the current location and delivery destination information to the server.

[1498] 2. Route display method

[1499] The terminal receives the optimal route information received from the server and displays it to the delivery driver, enabling efficient delivery.

[1500] User

[1501] 1. Request sending method

[1502] Delivery drivers use the device to enter their current location and delivery destination information and request the optimal route.

[1503] 2. Route confirmation and delivery

[1504] Delivery drivers follow the optimal route displayed on the terminal, which shortens delivery times and reduces costs.

[1505] Specific examples

[1506] For example, if a driver delivers packages from their current location (Tokyo Station) to multiple destinations (Shibuya Station, Shinjuku Station), the following steps are taken:

[1507] 1. The driver enters their current location and delivery destination into a smartphone app.

[1508] 2. The device sends this information to the server.

[1509] 3. The server uses AI to analyze and calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion at nearby facilities, event information, delivery priority, etc.

[1510] 4. The calculated route information is sent back from the server to the terminal and displayed to the driver.

[1511] 5. The driver follows the route to the desired delivery location.

[1512] Prompt Sentence Examples

[1513] For example, use the following prompt:

[1514] "Based on the current location (Tokyo Station) and multiple delivery destinations (Shibuya Station, Shinjuku Station), please use AI to calculate the optimal delivery route taking into account traffic information and congestion."

[1515] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1516] Step 1:

[1517] server

[1518] Real-time data collection

[1519] The server collects GPS data, map information, aerial photographs, congestion information at nearby facilities, information on nearby events, and package delivery priority information in real time. Data from each sensor and the Internet is used as input. This data is sent to the server and stored in a database. The output is raw data stored in the database.

[1520] Step 2:

[1521] server

[1522] Data Preprocessing

[1523] The server performs preprocessing on the collected data, including cleaning, normalization, and feature extraction. The raw data collected in step 1 is used as input. The data is processed to remove imperfections and make it consistent. The output is preprocessed data that has been organized into a form suitable for analysis.

[1524] Step 3:

[1525] server

[1526] Analysis using artificial intelligence models

[1527] The server uses the preprocessed data to calculate the optimal delivery route using an artificial intelligence model (e.g., a model using TensorFlow or PyTorch). The preprocessed data is used as input. The AI ​​model comprehensively evaluates delivery priority, real-time traffic information, congestion status, etc., and calculates the most efficient route. The output is optimal route information.

[1528] Step 4:

[1529] server

[1530] Route information provided via public API

[1531] The server provides the optimal route information to the terminal through a public API. The optimal route information calculated in step 3 is used as input. The server receives an API request and returns the corresponding optimal route information. The output is a response to the API request with the optimal route information.

[1532] Step 5:

[1533] Terminal

[1534] Submitting a User Request

[1535] The terminal receives a request from the delivery driver and sends the current location and delivery destination information to the server. The current location and delivery destination information entered by the driver on the smartphone app are used as input. The request is then sent to the server. The output is the request data sent to the server.

[1536] Step 6:

[1537] Terminal

[1538] View route information

[1539] The terminal displays the optimal route information received from the server to the driver. The optimal route information from the server is used as input. A process is performed to visually display the route information. The output is the route information that can be visually viewed by the driver.

[1540] Step 7:

[1541] User

[1542] Entering a request

[1543] The delivery driver uses a smartphone app to input their current location and delivery destination information and request the optimal route. As input, they manually enter their current location and delivery destination information into the app. The request is processed through the app. The output is the request data entered into the terminal.

[1544] Step 8:

[1545] User

[1546] Route confirmation and delivery

[1547] The delivery driver makes the delivery by following the optimal route displayed on the smartphone app. The optimal route information displayed on the terminal is used as input. The driver follows the route to the destination. The output is the delivery completed.

[1548] 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.

[1549] The present invention relates to a system that improves the convenience of ride-sharing services by using real-time data and AI analysis, and also to a system that combines an emotion engine that recognizes user emotions. An embodiment of the system and the processing of its program are explained below in natural language.

[1550] server

[1551] 1. Data Collection

[1552] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals, ensuring that the latest information is always available.

[1553] 2. Data Preprocessing

[1554] The server stores the collected data in a database and pre-processes it. Data cleaning removes incomplete data and noise, and normalization converts the scale of the data into a consistent format. Emotion data is also pre-processed in the same way to make it suitable for analysis.

[1555] 3. AI analysis

[1556] Based on the pre-processed data, the server calculates the optimal route using an artificial intelligence model that takes into account traffic conditions, congestion information, and event information, and also uses emotion data provided by an emotion engine to predict the most comfortable route for the user.

[1557] 4. API provided

[1558] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1559] Terminal

[1560] 1. User Request

[1561] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. Requests are typically made through smartphone apps.

[1562] 2. Route display

[1563] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[1564] User

[1565] 1. Submit a request

[1566] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[1567] 2. Route confirmation and movement

[1568] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1569] Specific examples

[1570] Consider a scenario where a user uses a smartphone app to book a ride from their home (current location) to their workplace (destination):

[1571] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[1572] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[1573] 3. The server uses AI analysis to calculate the optimal route based on real-time collected GPS data, traffic conditions, congestion information at nearby facilities, event information, emotional data, etc.

[1574] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[1575] 5. The user follows the directions on the device to reach their workplace quickly and via a less stressful route.

[1576] In this way, the system of the present invention proposes optimal routes taking into account the user's emotional data, enabling comfortable and efficient travel. This will alleviate the shortage of transportation options in rural and depopulated areas, and provide a sustainable means of transportation while reducing user stress and anxiety.

[1577] The processing flow will be explained below.

[1578] Step 1:

[1579] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This data is acquired in real time through various sensors and online databases. The server also receives user emotion data from the device.

[1580] Step 2:

[1581] The server stores the collected data in a database. At the same time, it preprocesses the data. Specifically, it cleans the data to remove incomplete data and noise. Next, it normalizes the data to convert data of different scales into a consistent format. After that, it extracts features to extract the information necessary for data analysis. Emotion data is also preprocessed in the same way.

[1582] Step 3:

[1583] The server uses the preprocessed data to perform analysis using an AI model. The AI ​​model combines and analyzes traffic conditions, congestion information, event information, emotional data, and other data to calculate the optimal route. By incorporating emotional data, the system selects a route that reduces the user's stress and anxiety.

[1584] Step 4:

[1585] After calculating the optimal route information, the server provides this data to external parties via a public API that external systems and applications can use to return route information in real time upon request.

[1586] Step 5:

[1587] The device receives a request from the user and sends information about the current location, destination, and emotion data to the server. The user makes this request using a smartphone app.

[1588] Step 6:

[1589] The server recalculates the optimal route based on the current location, destination information, and emotion data received from the device, and sends the results back to the device, providing the optimal route based on the latest data.

[1590] Step 7:

[1591] The terminal displays the optimal route information received from the server to the user, the route information being adjusted based on the user's emotional state.

[1592] Step 8:

[1593] The user travels according to the optimal route displayed on the device. By utilizing the emotion engine, the user can reduce stress and reach their destination comfortably.

[1594] Example 2

[1595] 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."

[1596] Conventional ride-sharing services calculate optimal routes using real-time data, but do not take the user's emotional state into account. As a result, users experiencing high levels of stress or fatigue may find their trips uncomfortable. Furthermore, route suggestions that take into account event information and the congestion status of nearby facilities are lacking, creating a need to improve the quality of the travel experience. The present invention aims to solve these problems by proposing more comfortable routes based on the user's emotional state.

[1597] 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.

[1598] In this invention, the server includes means for collecting real-time data, means for preprocessing the collected data, means for analyzing the preprocessed data and calculating an optimal route, means for providing the calculation results to a user terminal, means for providing a public API that accepts requests from outside and returns an optimal route, means for collecting, integrating, and analyzing user emotion data, and means for evaluating the comfort of the route based on the emotion data. This makes it possible to provide a comfortable travel route that reduces stress and fatigue based on the user's emotional state.

[1599] "Real-time data" refers to data that is collected instantaneously by the system and is immediately available for use.

[1600] "Collection" is the act of gathering the required data or information in a certain way.

[1601] "Preprocessing" refers to the process of cleaning, normalizing, and other operations to prepare collected data in a form that is easier to analyze.

[1602] "Analysis" is the process of drawing conclusions and insights from data using algorithms and artificial intelligence.

[1603] An "optimal route" is the most efficient and appropriate route from one point to another in terms of time, distance, comfort, etc.

[1604] A "user terminal" is a device that a user operates to send and receive information, and typically refers to a smartphone or tablet.

[1605] A "public API" is a publicly available application programming interface, a program interface designed to be available to external systems and applications.

[1606] "Emotion data" is information that reflects the user's emotional state, such as data indicating stress level or fatigue level.

[1607] A "machine learning model" is a collection of algorithms that learn from data and perform tasks such as prediction and classification based on that data.

[1608] "Optimization" is the process of making adjustments to achieve the best possible results within conditions and constraints in order to achieve a specific goal.

[1609] MODE FOR CARRYING OUT THE INVENTION

[1610] This invention relates to a system that uses real-time data and AI analysis to improve the convenience of ride-sharing services, as well as a system that combines an emotion engine that recognizes user emotions.

[1611] server

[1612] The server performs the following functions:

[1613] 1. Data Collection

[1614] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, and information about nearby events in real time. The emotion engine collects and integrates user emotion data. This data collection process is performed automatically at regular intervals. Specifically, data is obtained from external services such as Google Maps API, OpenStreetMap, and Eventbrite API.

[1615] 2. Data Preprocessing

[1616] The server stores the collected data in a database and performs data cleaning and normalization using an SQL database and the Python pandas library. This removes incomplete data and noise, and converts the data scale into a consistent format. Sentiment data is also preprocessed in the same way.

[1617] 3. AI analysis

[1618] Based on the preprocessed data, the server uses machine learning models such as TensorFlow and PyTorch to calculate the optimal route, taking into account traffic conditions, congestion information, and event information, as well as emotional data provided by the emotion engine, to predict the most comfortable route for the user.

[1619] 4. API provided

[1620] The server accepts requests from external systems and applications through a Restful API and provides real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1621] Terminal

[1622] 1. User Request

[1623] The device receives requests from users and sends information about their current location and destination to the server. In addition, the device acquires the user's emotional data and sends it to the server. Requests are typically made through a smartphone app.

[1624] 2. Route display

[1625] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving comfort.

[1626] User

[1627] 1. Submit a request

[1628] The user inputs their current location and destination using a smartphone app to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to a server.

[1629] 2. Route confirmation and movement

[1630] Users travel according to the optimal route displayed on their device, and the emotion engine also provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1631] Specific examples

[1632] Consider a scenario where a user uses a smartphone app to book a ride from home to work:

[1633] 1. When the user is at home, they set their current location (home) in a smartphone app and enter their workplace as their destination.

[1634] 2. The terminal transmits this information and the user's emotional state (e.g., if stress is high) to the server.

[1635] 3. The server uses AI analysis to calculate the optimal route based on location information, traffic conditions, facility usage information, event information, and emotional data collected in real time.

[1636] 4. The calculated route information is sent back from the server to the device, which then displays the optimal route for the user, taking into account the user's emotional state.

[1637] 5. The user follows the instructions on the device to reach their workplace quickly and via a less stressful route.

[1638] In this way, the system of the present invention proposes an optimal route taking into consideration the user's emotional data, enabling comfortable and efficient travel.

[1639] ※Example prompt:

[1640] "User A wants to book a ride from home to work and is looking for a short, low-stress route. Current location: 'Home Address', Destination: 'Work Address', Emotional state: 'High Stress'. Let the AI ​​suggest the optimal route."

[1641] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1642] Step 1: Data collection

[1643] The server collects real-time GPS data, map information, aerial photographs, congestion information for nearby facilities, and information on nearby events. This uses Google Maps API, OpenStreetMap, aerial photograph API, congestion information API, event information API, etc. The server collects and integrates the data obtained from these APIs. The input is data from each external API, and the output is integrated real-time data.

[1644] Step 2: Collecting Emotional Data

[1645] The device collects the user's emotional data, such as stress level and fatigue level, obtained from a smartphone or wearable device. The device then transmits this data to a server. The input is the emotional data, and the output is the integrated emotional data transmitted to the server.

[1646] Step 3: Data Preprocessing

[1647] The server stores the collected data in a database and performs data cleaning and normalization processes to remove incomplete data and noise, and convert the data scale into a consistent format. Sentiment data is also preprocessed in the same way. The input is raw data, and the output is preprocessed clean data.

[1648] Step 4: Emotion data preprocessing

[1649] The server receives the user's emotion data sent from the device and stores it in a database. This data is also cleaned and normalized to form a consistent format. The input is raw emotion data, and the output is preprocessed emotion data.

[1650] Step 5: Calculate the optimal route

[1651] The server inputs the preprocessed data into machine learning models such as TensorFlow and PyTorch. The optimal route is calculated based on traffic conditions, congestion information, event information, and emotion data. The input is the preprocessed clean data and emotion data, and the output is the optimal route information.

[1652] Step 6: Accept API requests

[1653] The server accepts requests from external systems and applications via a Restful API. The input is the request from the external system, and the output is the result of the optimal route calculation.

[1654] Step 7: Providing optimal route information

[1655] The server provides optimal route information to external systems and terminals, allowing users to receive optimal route information in real time. The input is optimal route information, and the output is route information provided to external systems and terminals.

[1656] Step 8: Receiving a User Request

[1657] The user inputs their current location and destination using a smartphone app. At the same time, the user's emotional data is also collected and sent from the device to the server. The input is the user's location information and emotional data, and the output is a request sent to the server.

[1658] Step 9: View Route Information

[1659] The terminal displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently. Route suggestions that take into account data from the emotion engine are also displayed. The input is optimal route information from the server, and the output is route information displayed to the user.

[1660] (Application example 2)

[1661] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] Conventional ride-sharing services do not select routes that take into account the user's actual emotional state during the trip, and do not sufficiently consider the user's psychological comfort. This results in increased stress and fatigue during the trip, resulting in a poor user experience. Furthermore, optimal route calculations that reflect real-time changes in traffic conditions and congestion information at nearby facilities are also inadequate. A new system is needed to solve these issues and realize more comfortable and efficient travel.

[1663] The specific processing by the specific 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 collecting real-time data, means for pre-processing the collected data, means for analyzing the pre-processed data and calculating an optimal route, means for providing the calculation results to the user terminal, means for accepting requests from outside and providing a public API that returns an optimal route, means for recognizing the user's emotional state and collecting emotional data, means for calculating an optimal route including the emotional data, and means for presenting a route adjusted based on the emotional state. This makes it possible to select a comfortable route that takes the user's emotional state into consideration, reducing stress during travel and providing a better user experience.

[1664] "Means for collecting real-time data" refers to a device or method for acquiring GPS data, map information, aerial photographs, congestion information on nearby facilities, information on nearby events, and user emotion data in real time.

[1665] "Means for pre-processing collected data" refers to devices or methods that perform data cleaning, noise removal, and normalization processes to convert raw data into a form suitable for analysis.

[1666] "Means for analyzing pre-processed data and calculating optimal routes" means a device or method that uses an artificial intelligence model to calculate the most efficient and safest travel route from the pre-processed data.

[1667] The "means for providing the calculation results to the user terminal" refers to a device or method for transmitting the calculated optimum route information to the user's device such as a smartphone or smart glasses in real time and displaying it.

[1668] "A means of providing a public API that accepts requests from outside and responds with the optimal route" is a public interface that provides optimal route information in real time in response to requests from other systems or applications.

[1669] "Means for recognizing a user's emotional state and collecting emotional data" refers to a device or method that uses emotion recognition technology to obtain a user's emotional state (e.g., stress level, fatigue level) and collects this as data.

[1670] The "means for calculating an optimal route that takes into account emotional data" refers to a device or method that takes into account the emotional state of the user and integrates it with conventional data to calculate the most comfortable and efficient travel route.

[1671] A "means for presenting a route adjusted based on emotional state" is a device or method that displays an optimal route adjusted based on the user's emotional state on the user's device and provides real-time guidance.

[1672] This invention is a system that improves the convenience of ride-sharing services by using real-time data and artificial intelligence analysis, and combines an emotion engine that recognizes user emotions. This system consists of three main components: a server, a terminal, and a user.

[1673] server

[1674] 1. Data Collection

[1675] The server collects GPS data, map information, aerial photographs, information on the congestion status of nearby facilities, and information on nearby events in real time. It also collects user emotion data using an emotion engine. This process is performed automatically at regular intervals, ensuring that the latest information is always available.

[1676] 2. Data Preprocessing

[1677] The server stores the collected data in a database and preprocesses it. It cleans and removes noise from the data, and normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way to make it suitable for analysis.

[1678] 3. AI analysis

[1679] Based on the pre-processed data, the server calculates the optimal route using a generative AI model that takes into account traffic conditions, congestion information, event information, and sentiment data to predict the most comfortable and efficient route for the user.

[1680] 4. API provided

[1681] The server accepts requests from external systems and applications via a public API, providing real-time optimal route information, which also enables integration with other Mobility as a Service (MaaS) modules.

[1682] Terminal

[1683] 1. User Request

[1684] The device receives a request from the user and sends information about the current location and destination to the server. In addition, the device acquires the user's emotional data and sends this to the server. This request is usually made through a smartphone app.

[1685] 2. Route display

[1686] The device displays the optimal route information received from the server to the user, allowing the user to reach their destination efficiently by following that information. Route suggestions that take into account the emotion engine data are also displayed, improving the user's comfort.

[1687] User

[1688] 1. Submit a request

[1689] Users use a smartphone app to input their current location and destination to find the optimal route, and the device also sends the user's emotional data (e.g., stress level and fatigue level) to the server.

[1690] 2. Route confirmation and movement

[1691] Users travel according to the optimal route displayed on their device, and the emotion engine provides routes tailored based on the user's emotional state, allowing for a more comfortable travel experience.

[1692] Hardware and Software Used

[1693] Hardware: Smart glasses (general name), head-mounted display (general name)

[1694] Software: Emotion recognition engine (any software for recognizing emotional states), public API (any interface for sending and receiving real-time data)

[1695] Prompt Sentence Examples

[1696] Current location: "35.6895,139.6917" (Tokyo Station)

[1697] Destination: "35.6580,139.7514" (Tokyo Tower)

[1698] User's emotional state: "stressed"

[1699] This system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[1700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1701] Step 1:

[1702] Data collection

[1703] The server collects GPS data, map information, aerial photographs, congestion information for nearby facilities, information on nearby events, and user sentiment data in real time. The latest data is obtained from external data sources at specified intervals through the data collection API. The input is data from the API, and the output is raw data stored in a database.

[1704] Step 2:

[1705] Data Preprocessing

[1706] The server performs data cleaning to remove incomplete data and noise from the collected raw data, then normalizes it to convert the scale of the data into a consistent format. Emotion data is also preprocessed in the same way. The input is raw data stored in a database, and the output is preprocessed analysis data.

[1707] Step 3:

[1708] AI analysis

[1709] The server inputs the preprocessed data into a generative AI model to calculate the optimal route based on traffic conditions, congestion information, event information, and emotional data. The input is the preprocessed analytical data, and the output is the calculation result of the optimal route. In this process, the devised optimal route is customized by taking into account the user's current emotional state.

[1710] Step 4:

[1711] Request received

[1712] The device receives a request from the user (current location, destination, and emotion data) and sends it to the server. The input is the data entered by the user into the device, and the output is the request data to the server. In this step, the emotion recognition engine obtains the user's emotion data.

[1713] Step 5:

[1714] Providing route information

[1715] The server calculates the optimal route information for the request based on the received request data and returns it to the device. The input is the request data received from the device, and the output is the route information to the device. This information is provided in real time via the API.

[1716] Step 6:

[1717] Route display

[1718] The terminal displays the optimal route information received from the server to the user. The input is the route information from the server, and the output is the route guidance displayed to the user. The display is performed via the user's device, such as smart glasses or a head-mounted display.

[1719] Step 7:

[1720] User Movement

[1721] The user travels according to the optimal route displayed on the device. The input is the displayed route guidance, and the output is the user's actual travel route. The emotion engine adjusts the route based on the user's emotional state, allowing the user to enjoy a comfortable travel experience.

[1722] As a result, the system provides real-time optimized routes that take into account the user's emotional state, enabling unprecedented comfort and efficiency in travel.

[1723] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1724] 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.

[1725] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1726] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1727] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1728] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1729] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1730] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1731] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1732] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1733] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1734] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1735] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1736] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1737] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1738] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1739] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1740] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1741] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1742] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1743] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1744] The following is further disclosed regarding the above embodiment.

[1745] (Claim 1)

[1746] a means for collecting real-time data;

[1747] a means for pre-processing the collected data;

[1748] A means of analyzing the pre-processed data and calculating the optimal route;

[1749] means for providing the calculation result to a user terminal;

[1750] A means to provide a public API that accepts requests from outside and responds with the optimal route,

[1751] A system including:

[1752] (Claim 2)

[1753] 2. The system according to claim 1, wherein the collected data includes GPS data, map information, aerial photographs, congestion information on nearby facilities, and information on nearby events.

[1754] (Claim 3)

[1755] 2. The system of claim 1, wherein the analysis means uses an artificial intelligence model to calculate the optimal route.

[1756] (Claim 4)

[1757] The system according to claim 1, wherein the preprocessing means performs data cleaning, normalization, and feature extraction.

[1758] (Claim 5)

[1759] 2. The system of claim 1, wherein the user terminal is a smartphone app.

[1760] "Example 1"

[1761] (Claim 1)

[1762] a means for collecting real-time data;

[1763] a means for pre-processing the collected data;

[1764] A means of analyzing the pre-processed data and calculating the optimal route;

[1765] means for providing the calculation result to a user terminal;

[1766] A means to provide a public API that accepts requests from outside and responds with the optimal route,

[1767] means for transmitting current location and destination information to a server based on a request from a user;

[1768] means for displaying the optimum route information received from the server on a user terminal;

[1769] A system including:

[1770] (Claim 2)

[1771] 2. The system according to claim 1, wherein the collected data includes GPS data, map information, aerial photographs, congestion information at nearby facilities, and information on nearby events.

[1772] (Claim 3)

[1773] 2. The system of claim 1, wherein the analysis means uses a generative AI model to calculate the optimal route.

[1774] "Application Example 1"

[1775] (Claim 1)

[1776] a means for collecting real-time data;

[1777] a means for pre-processing the collected data;

[1778] A means of analyzing the pre-processed data and calculating the optimal route;

[1779] means for providing the calculation result to a user terminal;

[1780] A means to provide a public API that accepts requests from outside and responds with the optimal route,

[1781] A means for managing package delivery at a logistics center;

[1782] A means of providing delivery routes that take into account local traffic information and destination priorities;

[1783] A system including:

[1784] (Claim 2)

[1785] 2. The system of claim 1, wherein the collected data includes GPS data, map information, aerial photographs, congestion information for surrounding facilities, information for surrounding events, and package delivery priority information.

[1786] (Claim 3)

[1787] 2. The system of claim 1, wherein the analysis means uses an artificial intelligence model to calculate the optimal route.

[1788] "Example 2: Combining Emotion Engines"

[1789] (Claim 1)

[1790] a means for collecting real-time data;

[1791] a means for pre-processing the collected data;

[1792] A means of analyzing the pre-processed data and calculating the optimal route;

[1793] means for providing the calculation result to a user terminal;

[1794] A means to provide a public API that accepts requests from outside and responds with the optimal route,

[1795] a means for collecting, integrating, and analyzing user emotion data;

[1796] a means for assessing the comfort of the route based on the emotion data;

[1797] A system including:

[1798] (Claim 2)

[1799] 10. The system of claim 1, wherein the collected data includes location information, map information, aerial photographs, facility usage information, event information, and emotion data.

[1800] (Claim 3)

[1801] 2. The system of claim 1, wherein the analysis means calculates the optimal route using a machine learning model.

[1802] "Application example 2 when combining emotion engines"

[1803] (Claim 1)

[1804] a means for collecting real-time data;

[1805] a means for pre-processing the collected data;

[1806] A means of analyzing the pre-processed data and calculating the optimal route;

[1807] means for providing the calculation result to a user terminal;

[1808] A means to provide a public API that accepts requests from outside and responds with the optimal route,

[1809] means for recognizing a user's emotional state and collecting emotional data;

[1810] A means of calculating the optimal route including emotional data;

[1811] means for presenting a route adjusted based on the emotional state;

[1812] A system including:

[1813] (Claim 2)

[1814] 2. The system according to claim 1, wherein the collected data includes GPS data, map information, aerial photographs, congestion information of surrounding facilities, information on surrounding events, and user emotion data.

[1815] (Claim 3)

[1816] 2. The system of claim 1, wherein the analysis means uses an artificial intelligence model to calculate the optimal route. [Explanation of symbols]

[1817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting real-time data; a means for pre-processing the collected data; A means of analyzing the pre-processed data and calculating the optimal route; means for providing the calculation result to a user terminal; A means to provide a public API that accepts requests from outside and responds with the optimal route, A system including:

2. 2. The system according to claim 1, wherein the collected data includes GPS data, map information, aerial photographs, information on the congestion status of surrounding facilities, and information on surrounding events.

3. 2. The system of claim 1, wherein the analysis means uses an artificial intelligence model to calculate the optimal route.

4. The system according to claim 1 , wherein the preprocessing means performs data cleaning, normalization, and feature extraction.

5. The system according to claim 1 , wherein the user terminal is a smartphone application.

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