System

Abstract

A system is provided.SOLUTION: This system includes a means for collecting traffic information, a means for analyzing a traffic flow on the basis of the collected traffic information, predicting congestion and calculating an optimum route, and a means for providing the calculated optimum route to a user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traffic congestion is a major issue for the logistics industry and general consumers, not only causing time and cost waste but also increasing stress and environmental impact. The present invention aims to provide a navigation system that predicts and efficiently avoids such traffic congestion. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, a means for predicting congestion and calculating an optimal route, and a means for providing the calculated optimal route to a user terminal. The system also includes a means for generating multiple route candidates during the calculation of the optimal route and simulating traffic flow for each route, and a means for the user terminal to transmit the current location and destination to a server, which then returns the optimal route in real time. This makes it possible to predict and avoid traffic congestion and provide efficient route guidance.

[0006] "Traffic information" refers to all information related to traffic, such as road congestion, accidents, construction work, and traffic flow.

[0007] "Traffic flow" refers to the state of movement and flow of vehicles on a particular road or in a particular area.

[0008] "Congestion" refers to a situation where vehicles are so congested on a road that they cannot travel at a normal speed or flow.

[0009] An "optimal route" refers to the most suitable route for traveling from a starting point to a destination point under specific conditions.

[0010] A "user terminal" is a device, such as a smartphone or a car navigation system, that a user operates to obtain information.

[0011] "Simulating" means imitating and predicting real-world traffic conditions in a computer.

[0012] A "server" is a central computing system that provides services to many computers.

[0013] "Navigation system" refers to a system that provides guidance on the vehicle's location and route. [Brief explanation of the drawings]

[0014] [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 illustrating 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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0036] 1. Data collection (server)

[0037] The server periodically collects traffic information from external traffic information providers (e.g., traffic data API). This data includes road congestion status, traffic accident information, road construction information, etc. Upon receiving this information, the server stores it in a database and prepares it for traffic flow analysis.

[0038] For example, the server makes an API request every 10 minutes to obtain traffic data, and then obtains congestion and speed information for each road section and stores it in a database.

[0039] 2. Traffic flow analysis and congestion prediction (server)

[0040] The server analyzes the collected traffic information and models traffic flow. This includes predicting traffic congestion and generating multiple route candidates to avoid it. In this process, the server uses algorithms (e.g., Dijkstra algorithm and A algorithm) that utilize graph theory to optimize navigation routes.

[0041] For example, when a vehicle travels from point A to point B, the server generates multiple route candidates and simulates traffic flow for each route, thereby selecting the route that will reach the destination in the shortest time while avoiding traffic congestion.

[0042] 3. Providing optimal routes (server and terminal)

[0043] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user.

[0044] When a user requests a route "from home to office" on a smartphone app, the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user.

[0045] 4. System Evolution

[0046] This navigation system has been designed with future integration with autonomous driving technology in mind, and is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision, even in a future where autonomous vehicles become more common.

[0047] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0048] The processing flow will be explained below.

[0049] Step 1: Collecting traffic information (server)

[0050] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0051] Step 2: Traffic flow analysis (server)

[0052] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0053] Step 3: Calculate the optimal route (server)

[0054] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0055] Step 4: Receiving a request from the user (device)

[0056] The user inputs a destination using the device and sends a navigation request. The device sends the current location and destination information to the server. The server receives the request and calculates the optimal route in real time.

[0057] Step 5: Providing the optimal route (server)

[0058] The server calculates the optimal route based on the user's request and returns it to the device. The device then analyzes the route information and visually displays it to the user, including route details and estimated arrival times.

[0059] Step 6: View directions (device)

[0060] The device visually displays the optimal route provided by the server to the user, including route display on a map and audio guidance, allowing the user to reach their destination safely and efficiently using visual and audio information.

[0061] Step 7: Real-time updates (server)

[0062] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0063] Example 1

[0064] 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."

[0065] Modern transportation systems are prone to frequent road delays due to traffic congestion and accidents, resulting in increased driver stress and fuel consumption. Furthermore, a lack of means to provide accurate traffic information in real time and suggest optimal routes has led to a decline in traffic efficiency. Therefore, there is a need for a system that can collect traffic information in real time and efficiently analyze it to provide drivers with optimal routes.

[0066] 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.

[0067] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, a means for modeling the traffic flow using graph theory, a means for predicting congestion, a means for calculating an optimal route using an algorithm for optimizing a navigation route using the modeled traffic flow, and a means for providing the calculated optimal route to a user terminal, thereby enabling accurate analysis of traffic information in real time and providing an optimal route to a driver.

[0068] "Traffic information" is a general term for data related to vehicle movement, such as road congestion, traffic accident information, road construction information, and speed limits.

[0069] "Graph theory" is a theory for modeling and analyzing various problems using mathematical structures consisting of nodes and edges connecting them.

[0070] "Modeling" is a technique for simulating and analyzing the behavior of actual systems and processes by representing them using mathematical and logical structures.

[0071] A "navigation route" is a route from a starting point to a destination point, which is a route suggested for the driver to travel.

[0072] An "optimization algorithm" is a computational method for finding the most efficient solution under given conditions, and includes the Dijkstra algorithm and the A algorithm in particular.

[0073] A "user terminal" is a device, such as a smartphone or tablet, that a user directly operates and receives information.

[0074] "Real-time" refers to immediate processing and response at the moment an event occurs.

[0075] "Traffic flow" is a concept that indicates the movement of vehicles on a road during a specific time period, and includes factors such as congestion, speed, and vehicle density.

[0076] "Simulation" is a method of virtually recreating real-world systems and processes and testing and analyzing their behavior.

[0077] A "server" is a computer system whose role is to provide data and services to other computers on a network.

[0078] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0079] First, the operation of the entire system requires a server, user terminals (e.g., smartphones), and a communications infrastructure such as the Internet that connects them. The server obtains traffic information from external traffic information services (e.g., traffic data API) and stores it in a database. The traffic information includes road congestion status, traffic accident information, and road construction information.

[0080] Specifically, the server sends a request to the traffic data API every 10 minutes and obtains the necessary information from the returned response data. This information undergoes a series of data processing and is stored in a database. The server then analyzes traffic flow based on the collected data and models it using graph theory. This makes it possible to understand the congestion level and vehicle speed for each road section and predict traffic congestion.

[0081] Machine learning models can be used to predict traffic congestion, allowing for highly accurate analysis. Furthermore, based on the traffic flow model, optimization algorithms such as the Dijkstra algorithm and the A algorithm are used to generate multiple route candidates, and a process is then performed to simulate traffic flow for each candidate. This allows the system to propose the optimal route to the user.

[0082] When a user inputs a destination using a smartphone app, the device sends a request to the server. Upon receiving the request, the server calculates the optimal route in real time and returns the results to the user's device. The device plots the returned optimal route information on a map and visually displays the estimated arrival time to the user.

[0083] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. At this time, the following prompt sentence is generated:

[0084] 1. Data collection prompt:

[0085] The server obtains road congestion and speed information from the traffic data API every 10 minutes and stores it in a database.

[0086] 2. Traffic flow analysis prompt:

[0087] Based on graph theory, the Dijkstra algorithm is used to simulate and determine the optimal route from point A to point B that avoids congestion.

[0088] 3. Prompt for optimal route:

[0089] When a user requests a route from home to the office on their smartphone, the server calculates the optimal route and estimated arrival time and returns it to the device, which then displays this information to the user.

[0090] This system will enable professional drivers and consumers to enjoy an efficient and stress-free driving experience. It will also contribute to reducing environmental impact by minimizing fuel consumption. Furthermore, it is designed with collaboration with autonomous driving technology in mind, and is expected to improve the efficiency and safety of future transportation systems.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] Data collection (server)

[0094] Input: Endpoint URL and access key of external traffic information service (e.g. traffic data API)

[0095] Processing: The server sends a request to the traffic data API every 10 minutes. This request includes the specified latitude and longitude range, date and time, and the required traffic information items (e.g., road congestion level, accident information, construction information).

[0096] Specific operations: Sending HTTP requests and receiving API responses

[0097] Output: Dataset of acquired traffic information (e.g., response data in JSON format)

[0098] Step 2:

[0099] Traffic data storage (server)

[0100] Input: Traffic information dataset obtained in step 1

[0101] Processing: The server analyzes the acquired traffic information and extracts necessary data items (e.g., road section ID, congestion level, speed, accident information). The extracted data is saved in a database.

[0102] Specific operations: database update operations, data insertion and updating

[0103] Output: Updated and saved database state

[0104] Step 3:

[0105] Traffic flow analysis and modeling (server)

[0106] Input: Traffic information database saved in step 2

[0107] Processing: The server uses the stored traffic information to model traffic flow using graph theory. This involves representing road sections as edges and intersections as nodes, and assigning congestion levels and vehicle speeds to each edge.

[0108] Specific operations: Creating a graph structure, setting attributes of edges and nodes

[0109] Output: Modeled traffic flow graph

[0110] Step 4:

[0111] Traffic congestion prediction (server)

[0112] Input: Traffic flow graph modeled in step 3

[0113] Processing: The server uses a machine learning model to predict congestion based on the traffic flow graph. The model, which has learned from past data, calculates the probability of congestion occurring for each time period.

[0114] Specific operation: Applying machine learning models and calculating prediction results

[0115] Output: Predicted data of traffic congestion probability

[0116] Step 5:

[0117] Optimal route calculation (server)

[0118] Input: Prediction data from Step 4, route request from user device (current location and destination)

[0119] Processing: The server uses the Dijkstra algorithm and the A algorithm to generate multiple route candidates based on the modeled traffic flow graph and congestion prediction data, performs traffic flow simulations for each route, and selects the optimal route.

[0120] Specific operations: generating multiple route candidates, simulating each route, and selecting the optimal route

[0121] Output: Data on optimal route and expected arrival time

[0122] Step 6:

[0123] Providing optimal route information (server and terminal)

[0124] Input: Optimal route data generated in step 5, request information from user terminal

[0125] Processing: The server generates optimal route information and sends it back to the user's device. The device plots the received optimal route information on a map and provides visual navigation to the user.

[0126] Specific operations: Data transmission by server, route plotting by device, display on user interface

[0127] Output: Optimal route and estimated arrival time displayed on the user's terminal

[0128] Step 7:

[0129] Real-time updates (server and device)

[0130] Input: Real-time information from each traffic data API, periodic location data updates from user devices

[0131] Processing: The server continuously updates new traffic information in real time, and the device periodically obtains new optimal route information from the server. This allows the user to always be guided to the optimal route based on the latest traffic conditions.

[0132] Specific operations: Automatically updating the server database, sending periodic requests from the terminal, and processing the server's responses

[0133] Output: Real-time updated navigation information

[0134] (Application example 1)

[0135] 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."

[0136] As autonomous vehicles become more widespread, it is becoming increasingly necessary to provide optimal routes in real time to deal with traffic congestion and accidents. However, conventional navigation systems lack real-time data analysis and integration with vehicle control systems, making it difficult to provide efficient routes. To address this issue, there is a need for an advanced navigation system that can collect and analyze traffic information in real time and further integrate it into the autonomous vehicle control system.

[0137] 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.

[0138] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, predicting congestion, and calculating an optimal route, a means for providing the calculated optimal route to a user terminal, and a means for integrating the optimal route information into a vehicle control system. This enables real-time collection and analysis of traffic information and provision of optimal routes to autonomous vehicles. Furthermore, optimal route calculation using a network graph and operation optimization of autonomous vehicles enable efficient and safe operation.

[0139] "Traffic information" refers to information related to road traffic conditions, such as road congestion, traffic accident information, and road construction information.

[0140] "Traffic flow" refers to the flow of vehicles on a road during a specific time period, and is a concept that includes the associated speed and congestion.

[0141] A "traffic jam" is a condition in which the flow of vehicles on a road comes to a near halt or becomes very slow.

[0142] An "optimal route" is a route from a specific origin to a destination that is deemed to be the most efficient route based on certain conditions.

[0143] "User terminal" refers to a device that receives information from the navigation system, such as a smartphone or an in-vehicle display.

[0144] A "vehicle control system" is a system that controls the operation, speed, and direction of a vehicle, and includes autonomous driving technology.

[0145] A "network graph" is a mathematical structure that represents a road network as vertices and edges, and is used to analyze traffic flow and calculate optimal routes.

[0146] In graph theory, a "weighted graph" is a graph in which each edge is weighted, and is used to represent road congestion and travel time.

[0147] "Real-time" refers to responding immediately to ongoing events, and is an important concept in traffic information and autonomous driving control.

[0148] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, a user terminal, and a communication network connecting them.

[0149] First, the server periodically collects traffic information from a traffic information service (API). This data includes information on road congestion, traffic accidents, road construction, and more. The server makes API requests for this information every 10 minutes, obtains congestion levels and speed information for each road section, and stores this information in a database. The collected traffic information is used to model traffic flow, predict traffic congestion, and calculate optimal routes.

[0150] The server analyzes traffic flow based on traffic information and uses an algorithm (e.g., Dijkstra algorithm or A algorithm) to optimize the navigation route using graph theory. This generates multiple route candidates and simulates traffic flow for each route. The server then determines the optimal route based on the simulation results.

[0151] When a user inputs their destination into their smartphone or the device of an autonomous vehicle, the device sends their current location and destination to a server. The server calculates the optimal route in real time based on the latest traffic information and returns the results to the user's device. The device then visually displays the optimal route and estimated arrival time to the user. Furthermore, this optimal route information is integrated into the vehicle control system, helping the autonomous vehicle optimize its operation in real time.

[0152] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. For example, enter the following as a prompt:

[0153] current_location = "Tokyo"

[0154] destination = "Yokohama"

[0155] route = nav_sys.provide_navigation(current_location, destination)

[0156] print("Optimal Route from Tokyo to Yokohama:", route)

[0157] This system uses programs written in the Python language. On the server side, it uses the requests module for making API requests and the networkx module for traffic flow modeling and route calculation. For the database, it uses an SQL-based database (e.g., PostgreSQL) to efficiently store traffic information.

[0158] As described above, this invention maximizes traffic efficiency and realizes efficient and safe operation by collecting and analyzing traffic information in real time and providing optimal routes to autonomous vehicles.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] The server collects traffic information from a traffic information service. Specifically, the server sends API requests every 10 minutes and receives information on road congestion, traffic accidents, road construction, etc. The input is API response data, which is structured in JSON format, etc. The server stores this in a database.

[0162] Step 2:

[0163] The server analyzes traffic flow based on the collected traffic information. It retrieves information stored in a database and analyzes congestion levels and speed information for each road section. The input here is past traffic data retrieved from the database, and the output is an analyzed traffic flow model. Past data trends are used for the analysis.

[0164] Step 3:

[0165] The server uses a traffic flow model to predict congestion and generate multiple route candidates. This uses the Dijkstra algorithm and the A algorithm. The input is the traffic flow model, and the output is predicted congestion information and multiple route candidates to avoid it. The route candidate simulation uses the networkx module in Python.

[0166] Step 4:

[0167] The user inputs their destination into their smartphone or in-car device. The user device acquires the user's current location and destination, and sends a request to the server. The input is the destination information from the user, and the output is the request data sent to the server. The device uses GPS and location information services.

[0168] Step 5:

[0169] The server calculates the optimal route in real time based on the current location and destination received from the user's device. This calculation uses the latest traffic information and existing congestion prediction data. The input is the user's current location and destination, as well as a traffic flow model, and the output is optimal route information. The calculation is performed using a network graph.

[0170] Step 6:

[0171] The server returns the calculated optimal route information to the user's device. The user's device receives this information and displays it visually. The output is the optimal route and expected arrival time, displayed in a format that is easy for the user to understand. This is often displayed in a browser or a dedicated app.

[0172] Step 7:

[0173] The user device sends the optimal route information to the vehicle control system, which then optimizes the operation of the autonomous vehicle based on the received information. The input is the optimal route information from the device, and the output is vehicle operation control commands. The system adjusts operation in real time.

[0174] 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.

[0175] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0176] 1. Data collection (server)

[0177] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0178] 2. Traffic flow analysis and congestion prediction (server)

[0179] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0180] 3. Calculating the optimal route (server)

[0181] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0182] 4. Emotion engine (server and terminal)

[0183] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows it to evaluate the user's stress level and fatigue. For example, if the user is feeling stressed while driving, the emotion engine will detect this and send feedback to the navigation system.

[0184] 5. Navigation interface adjustment (terminal)

[0185] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0186] 6. Providing and updating optimal routes (server)

[0187] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0188] 7. System Evolution

[0189] This navigation system is also designed with future integration with autonomous driving technology in mind. Even in a future where autonomous vehicles are on the rise, it is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision. Furthermore, by utilizing an emotion engine, more user-friendly navigation can be achieved, reducing driver stress.

[0190] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0191] The processing flow will be explained below.

[0192] Step 1: Collecting traffic information (server)

[0193] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0194] Step 2: Traffic flow analysis (server)

[0195] The server analyzes the traffic information it receives and models the traffic flow for each road section, based on factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. This allows it to evaluate the passability of each road section.

[0196] Step 3: Calculate the optimal route (server)

[0197] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0198] Step 4: Collect and analyze emotion data (device)

[0199] The device uses a camera and microphone to collect emotion data from the user's facial expressions and tone of voice, and the device then sends this data to an emotion engine to evaluate the user's stress level and fatigue.

[0200] Step 5: Feedback from the emotion engine (device)

[0201] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or alternative routes to avoid crowds.

[0202] Step 6: Providing the best route (server)

[0203] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device then visually displays the received optimal route information to the user, including route display on a map and voice guidance.

[0204] Step 7: View directions (device)

[0205] The device visually displays the optimal route provided by the server to the user, allowing the user to reach their destination safely and efficiently using visual and audio information. The display includes route details and estimated arrival time.

[0206] Step 8: Real-time updates (server)

[0207] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0208] Step 9: Periodic analysis by the emotion engine (device)

[0209] While the user is driving, the emotion engine periodically analyzes the user's emotional data, allowing it to respond immediately to changes in the user's emotional state. For example, if the user's fatigue level increases during a long drive, the engine can suggest taking a break.

[0210] In this way, by combining traffic information and emotional data, we are able to create a system that provides a safer and less stressful driving experience.

[0211] Example 2

[0212] 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."

[0213] Conventional navigation systems can collect traffic information in real time and provide optimal routes, but they cannot take into account the user's emotional state. As a result, users are required to use the same navigation interface even when they are under high stress or fatigue, which results in insufficient stress relief and safety improvement while driving.

[0214] 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.

[0215] In this invention, the server includes means for collecting traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for adjusting the calculated optimal route in accordance with the emotional state of the user, means for providing the adjusted optimal route to the user terminal, and means for providing an adjusted navigation interface. This makes it possible to provide appropriate navigation information in accordance with the emotional state of the user, reduce stress while driving, and improve safety.

[0216] "Traffic information" is data relating to road usage, such as road congestion, accident information, and construction information.

[0217] "Traffic flow" refers to data that indicates dynamic conditions such as the flow, speed, and density of vehicles on roads.

[0218] A "user terminal" is a device held by a user who uses a navigation system, such as a smartphone or a car navigation system.

[0219] An "optimal route" refers to a route from a starting point to a destination that takes the least amount of time or meets other specified criteria.

[0220] "Emotional state" refers to the psychological and emotional state that a user feels, such as stress level or fatigue level.

[0221] The "navigation interface" refers to a user interface such as a map or voice guidance displayed to the user through a navigation system.

[0222] "Analysis means" refers to technical means for processing data based on collected traffic information and modeling traffic flow.

[0223] "Simulation means" refers to a technical means for virtually reproducing actual traffic flow for multiple route candidates and evaluating the effectiveness of each route.

[0224] "Real-time" means that the process from data acquisition to analysis and provision is carried out without delay.

[0225] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0226] The server periodically collects traffic condition data from an external traffic information service. This data includes road congestion, accident information, construction information, and so on. The server sends an API request to obtain the data and stores it in a database. Specifically, the server obtains the latest traffic data using the endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") for calling the traffic information API.

[0227] The server then analyzes the traffic information using the Python pandas library to model traffic flow, which allows the server to evaluate the passability of each road section and predict the likelihood of congestion.

[0228] The server calculates the optimal route using the results of traffic flow analysis. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to calculate the route. Python's NetworkX library is used for this. This allows the optimal route from the user's starting point to their destination to be calculated and travel time to be evaluated.

[0229] The emotion engine is used to recognize the user's emotions. The user's device is equipped with a camera and microphone, which are used to collect the user's facial expressions and tone of voice. The collected data is sent to a server, which then applies an emotion analysis algorithm (for example, a model using TensorFlow) to evaluate the user's emotional state. This allows the user's stress level and fatigue level to be determined.

[0230] The user device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the displayed information and change the voice guidance to a slower tone. The device will also visually display the received optimal route information and highlight it on a map.

[0231] The system responds to changes in traffic conditions in real time, and the server periodically recollects traffic information and updates the optimal route based on the analysis results, allowing users to continue driving based on the latest information.

[0232] Examples of prompts that can be used to generate systems using generative AI models include:

[0233] "Generate a program for a real-time traffic information analysis system that calculates and presents optimal routes to users. The system will have an emotion engine that will adjust the navigation interface based on the user's emotional state."

[0234] Such a system is expected to provide an efficient and stress-free driving experience and also contribute to reducing environmental impact by minimizing fuel consumption.

[0235] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0236] Step 1: Data collection

[0237] The server sends an API request to an external traffic information service. The input is the API endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") and an authentication token. The output is traffic information (JSON format data). Specifically, the server sends an HTTP GET request and stores the traffic information received as a response in a local database.

[0238] Step 2: Traffic flow analysis

[0239] The server analyzes the stored traffic information. The input is the traffic information obtained in the previous step (JSON data stored in the database). The output is the analysis results (passability and congestion prediction data for each road section). Specifically, the server processes the traffic data using Python's pandas library and calculates the number of vehicles and speeds on each road section.

[0240] Step 3: Calculate the optimal route

[0241] The server receives the user's starting point and destination. The input is the starting point and destination information sent from the user's device. The output is the optimal route (a list of GPS coordinates). Specifically, the server uses Python's NetworkX library to run the Dijkstra algorithm to calculate the optimal route.

[0242] Step 4: Collecting emotion data

[0243] The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The inputs include the user's facial images and voice data. The output is the collected emotion data. Specifically, the device captures the user's facial expressions with the front camera and records the tone of voice with the microphone.

[0244] Step 5: Sentiment Analysis

[0245] The device sends the collected facial and voice data to a server. The input is the user's facial expression data and voice data. The output is an evaluation of the user's emotional state. Specifically, the server uses TensorFlow to run an emotion analysis model and evaluate whether the user is stressed or tired.

[0246] Step 6: Adjusting the navigation interface

[0247] The device receives feedback from the emotion engine. The input is the evaluation of the emotional state sent by the server. The output is an adjusted navigation interface. Specifically, the device changes the tone of the voice guidance and simplifies the information display accordingly.

[0248] Step 7: Provide optimal route

[0249] The server sends the calculated optimal route to the user's device. The input is the optimal route (a list of GPS coordinates). The output is the route information displayed on the user's device. Specifically, the server sends the optimal route data to the user's device in real time and displays it on a map.

[0250] Step 8: Real-time updates

[0251] The server updates route information in real time according to changes in traffic conditions. The input is newly collected traffic information. The output is updated optimal route information sent to the user terminal. Specifically, the server periodically reanalyzes traffic data and updates route information as necessary.

[0252] (Application example 2)

[0253] 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."

[0254] While conventional navigation systems analyze traffic information in real time and provide optimal routes, they are unable to optimize the route taking into account the user's emotions and stress levels. As a result, traffic jams and unexpected traffic conditions can cause significant stress for users, making it difficult to provide a relaxing travel experience. To address these issues, the present invention aims to analyze the user's emotions and provide navigation information that corresponds to their emotional state.

[0255] 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 traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for providing the calculated optimal route to a user terminal, means for analyzing the user's emotions, means for adjusting navigation information based on the analyzed emotions, and means for displaying and providing navigation information on a smart display device by voice. This provides an optimal route that takes the user's emotions into consideration, enabling efficient travel while reducing stress.

[0256] "Traffic information" is data related to traffic conditions, such as road congestion, accident information, and construction information.

[0257] "Traffic flow" refers to information such as the flow, density, and speed of vehicles passing through a specific section.

[0258] The "means for analyzing emotions" is a means for analyzing the user's facial expressions, tone of voice, etc., and evaluating the user's emotional state.

[0259] The "optimal route" refers to the route that can take you from the starting point to the destination in the shortest time, taking into account traffic conditions.

[0260] A "smart display device" is a device that provides visual and audio information to a user, including, for example, smart glasses and head-mounted displays.

[0261] "Navigation information" includes information such as route guidance, traffic information, and current location, and is information that shows the way to the user's destination.

[0262] A "user terminal" is a part of the navigation system, and is a device that provides optimal routes and traffic information provided by the server to the user.

[0263] "Route candidates" indicate multiple possible routes to a destination, and are evaluated taking into consideration factors such as travel time and traffic congestion forecasts for each route.

[0264] "Traffic information service" is an external service that provides data related to traffic conditions. Data is obtained from this service and used for analysis on the server.

[0265] The "server" is the central part of the entire system, and is an information processing device that collects and analyzes traffic information, calculates optimal routes, analyzes user emotions, and so on.

[0266] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0267] 1. Data collection (server)

[0268] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0269] 2. Traffic flow analysis and congestion prediction (server)

[0270] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0271] 3. Calculating the optimal route (server)

[0272] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0273] 4. Emotion engine (server and terminal)

[0274] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows the system to assess the user's stress level and fatigue. For example, if a user says, "Today's traffic jam is tiring" while driving, the system will analyze the user's stress level from the tone of voice. The system may reevaluate the route and recommend a scenic route that is relaxing.

[0275] 5. Navigation interface adjustment (terminal)

[0276] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0277] 6. Providing and updating optimal routes (server)

[0278] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0279] 7. Use of smart display devices

[0280] Smart display devices, such as smart glasses and head-mounted displays (HMDs), can be used to visually display real-time traffic information and recommended routes, allowing users to obtain navigation information more intuitively.

[0281] Prompt Sentence Examples

[0282] If a user says, "Today's traffic jam is tiring," the system will calculate the optimal route in real time and suggest scenic alternatives to reduce stress, providing visual instructions and audio guidance through smart glasses or an HMD.

[0283] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0284] Step 1: Data Collection (Server)

[0285] The server periodically sends API requests from external traffic information providers to obtain traffic condition data (road congestion, accident information, construction information). The obtained data is stored in a database. The input is the API request, and the output is the traffic information stored in the database.

[0286] Step 2: Traffic flow analysis and congestion prediction (server)

[0287] The server analyzes traffic information stored in the database and models the traffic flow (number of vehicles, vehicle speed, and likelihood of congestion) for each road section. This evaluates the passability of each section. The input is the stored traffic information, and the output is the passability evaluation result for each section.

[0288] Step 3: Calculate the optimal route (server)

[0289] The server calculates the optimal route from the departure point to the destination based on the results of traffic flow analysis. Using algorithms such as the Dijkstra algorithm and A algorithm, it generates multiple route candidates, simulates each one, and selects the optimal one. The input is the traffic flow analysis results, and the output is optimal route information.

[0290] Step 4: User sentiment analysis (device)

[0291] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. The step-analyzed emotional state (stress level and fatigue level) is evaluated. The input is the user's facial expressions and tone of voice, and the output is the evaluation result of the emotional state.

[0292] Step 5: Adjusting the navigation interface (device)

[0293] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device simplifies the displayed information and changes the voice guidance to a slower tone. The input is the emotion analysis result, and the output is the adjusted navigation interface.

[0294] Step 6: Providing and updating optimal routes (server, terminal)

[0295] When a user inputs a destination into the device, the device sends that information to the server. The server recalculates the optimal route in real time and returns the results to the device. The device then provides the user with route information through visual and audio guidance. The route information is continually updated as traffic conditions change. The input is destination information and current location information, and the output is real-time optimal route information.

[0296] Step 7: Using a Smart Display Device (Device)

[0297] Using smart display devices (smart glasses or head-mounted displays), navigation information is displayed in real time, including optimal routes, traffic information, and interface adjustments according to emotional states. The input is optimal route information and emotion analysis results from the server, and the output is navigation information displayed on the display.

[0298] 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.

[0299] 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.

[0300] 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.

[0301] [Second embodiment]

[0302] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0303] 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.

[0304] 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).

[0305] 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.

[0306] 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.

[0307] 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).

[0308] 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.

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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."

[0314] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0315] 1. Data collection (server)

[0316] The server periodically collects traffic information from external traffic information providers (e.g., traffic data API). This data includes road congestion status, traffic accident information, road construction information, etc. Upon receiving this information, the server stores it in a database and prepares it for traffic flow analysis.

[0317] For example, the server makes an API request every 10 minutes to obtain traffic data, and then obtains congestion and speed information for each road section and stores it in a database.

[0318] 2. Traffic flow analysis and congestion prediction (server)

[0319] The server analyzes the collected traffic information and models traffic flow. This includes predicting traffic congestion and generating multiple route candidates to avoid it. In this process, the server uses algorithms (e.g., Dijkstra algorithm and A algorithm) that utilize graph theory to optimize navigation routes.

[0320] For example, when a vehicle travels from point A to point B, the server generates multiple route candidates and simulates traffic flow for each route, thereby selecting the route that will reach the destination in the shortest time while avoiding traffic congestion.

[0321] 3. Providing optimal routes (server and terminal)

[0322] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user.

[0323] When a user requests a route "from home to office" on a smartphone app, the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user.

[0324] 4. System Evolution

[0325] This navigation system has been designed with future integration with autonomous driving technology in mind, and is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision, even in a future where autonomous vehicles become more common.

[0326] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0327] The processing flow will be explained below.

[0328] Step 1: Collecting traffic information (server)

[0329] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0330] Step 2: Traffic flow analysis (server)

[0331] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0332] Step 3: Calculate the optimal route (server)

[0333] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0334] Step 4: Receiving a request from the user (device)

[0335] The user inputs a destination using the device and sends a navigation request. The device sends the current location and destination information to the server. The server receives the request and calculates the optimal route in real time.

[0336] Step 5: Providing the optimal route (server)

[0337] The server calculates the optimal route based on the user's request and returns it to the device. The device then analyzes the route information and visually displays it to the user, including route details and estimated arrival times.

[0338] Step 6: View directions (device)

[0339] The device visually displays the optimal route provided by the server to the user, including route display on a map and audio guidance, allowing the user to reach their destination safely and efficiently using visual and audio information.

[0340] Step 7: Real-time updates (server)

[0341] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0342] Example 1

[0343] 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."

[0344] Modern transportation systems are prone to frequent road delays due to traffic congestion and accidents, resulting in increased driver stress and fuel consumption. Furthermore, a lack of means to provide accurate traffic information in real time and suggest optimal routes has led to a decline in traffic efficiency. Therefore, there is a need for a system that can collect traffic information in real time and efficiently analyze it to provide drivers with optimal routes.

[0345] 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.

[0346] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, a means for modeling the traffic flow using graph theory, a means for predicting congestion, a means for calculating an optimal route using an algorithm for optimizing a navigation route using the modeled traffic flow, and a means for providing the calculated optimal route to a user terminal, thereby enabling accurate analysis of traffic information in real time and providing an optimal route to a driver.

[0347] "Traffic information" is a general term for data related to vehicle movement, such as road congestion, traffic accident information, road construction information, and speed limits.

[0348] "Graph theory" is a theory for modeling and analyzing various problems using mathematical structures consisting of nodes and edges connecting them.

[0349] "Modeling" is a technique for simulating and analyzing the behavior of actual systems and processes by representing them using mathematical and logical structures.

[0350] A "navigation route" is a route from a starting point to a destination point, which is a route suggested for the driver to travel.

[0351] An "optimization algorithm" is a computational method for finding the most efficient solution under given conditions, and includes the Dijkstra algorithm and the A algorithm in particular.

[0352] A "user terminal" is a device, such as a smartphone or tablet, that a user directly operates and receives information.

[0353] "Real-time" refers to immediate processing and response at the moment an event occurs.

[0354] "Traffic flow" is a concept that indicates the movement of vehicles on a road during a specific time period, and includes factors such as congestion, speed, and vehicle density.

[0355] "Simulation" is a method of virtually recreating real-world systems and processes and testing and analyzing their behavior.

[0356] A "server" is a computer system whose role is to provide data and services to other computers on a network.

[0357] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0358] First, the operation of the entire system requires a server, user terminals (e.g., smartphones), and a communications infrastructure such as the Internet that connects them. The server obtains traffic information from external traffic information services (e.g., traffic data API) and stores it in a database. The traffic information includes road congestion status, traffic accident information, and road construction information.

[0359] Specifically, the server sends a request to the traffic data API every 10 minutes and obtains the necessary information from the returned response data. This information undergoes a series of data processing and is stored in a database. The server then analyzes traffic flow based on the collected data and models it using graph theory. This makes it possible to understand the congestion level and vehicle speed for each road section and predict traffic congestion.

[0360] Machine learning models can be used to predict traffic congestion, allowing for highly accurate analysis. Furthermore, based on the traffic flow model, optimization algorithms such as the Dijkstra algorithm and the A algorithm are used to generate multiple route candidates, and a process is then performed to simulate traffic flow for each candidate. This allows the system to propose the optimal route to the user.

[0361] When a user inputs a destination using a smartphone app, the device sends a request to the server. Upon receiving the request, the server calculates the optimal route in real time and returns the results to the user's device. The device plots the returned optimal route information on a map and visually displays the estimated arrival time to the user.

[0362] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. At this time, the following prompt sentence is generated:

[0363] 1. Data collection prompt:

[0364] The server obtains road congestion and speed information from the traffic data API every 10 minutes and stores it in a database.

[0365] 2. Traffic flow analysis prompt:

[0366] Based on graph theory, the Dijkstra algorithm is used to simulate and determine the optimal route from point A to point B that avoids congestion.

[0367] 3. Prompt for optimal route:

[0368] When a user requests a route from home to the office on their smartphone, the server calculates the optimal route and estimated arrival time and returns it to the device, which then displays this information to the user.

[0369] This system will enable professional drivers and consumers to enjoy an efficient and stress-free driving experience. It will also contribute to reducing environmental impact by minimizing fuel consumption. Furthermore, it is designed with collaboration with autonomous driving technology in mind, and is expected to improve the efficiency and safety of future transportation systems.

[0370] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] Data collection (server)

[0373] Input: Endpoint URL and access key of external traffic information service (e.g. traffic data API)

[0374] Processing: The server sends a request to the traffic data API every 10 minutes. This request includes the specified latitude and longitude range, date and time, and the required traffic information items (e.g., road congestion level, accident information, construction information).

[0375] Specific operations: Sending HTTP requests and receiving API responses

[0376] Output: Dataset of acquired traffic information (e.g., response data in JSON format)

[0377] Step 2:

[0378] Traffic data storage (server)

[0379] Input: Traffic information dataset obtained in step 1

[0380] Processing: The server analyzes the acquired traffic information and extracts necessary data items (e.g., road section ID, congestion level, speed, accident information). The extracted data is saved in a database.

[0381] Specific operations: database update operations, data insertion and updating

[0382] Output: Updated and saved database state

[0383] Step 3:

[0384] Traffic flow analysis and modeling (server)

[0385] Input: Traffic information database saved in step 2

[0386] Processing: The server uses the stored traffic information to model traffic flow using graph theory. This involves representing road sections as edges and intersections as nodes, and assigning congestion levels and vehicle speeds to each edge.

[0387] Specific operations: Creating a graph structure, setting attributes of edges and nodes

[0388] Output: Modeled traffic flow graph

[0389] Step 4:

[0390] Traffic congestion prediction (server)

[0391] Input: Traffic flow graph modeled in step 3

[0392] Processing: The server uses a machine learning model to predict congestion based on the traffic flow graph. The model, which has learned from past data, calculates the probability of congestion occurring for each time period.

[0393] Specific operation: Applying machine learning models and calculating prediction results

[0394] Output: Predicted data of traffic congestion probability

[0395] Step 5:

[0396] Optimal route calculation (server)

[0397] Input: Prediction data from Step 4, route request from user device (current location and destination)

[0398] Processing: The server uses the Dijkstra algorithm and the A algorithm to generate multiple route candidates based on the modeled traffic flow graph and congestion prediction data, performs traffic flow simulations for each route, and selects the optimal route.

[0399] Specific operations: generating multiple route candidates, simulating each route, and selecting the optimal route

[0400] Output: Data on optimal route and expected arrival time

[0401] Step 6:

[0402] Providing optimal route information (server and terminal)

[0403] Input: Optimal route data generated in step 5, request information from user terminal

[0404] Processing: The server generates optimal route information and sends it back to the user's device. The device plots the received optimal route information on a map and provides visual navigation to the user.

[0405] Specific operations: Data transmission by server, route plotting by device, display on user interface

[0406] Output: Optimal route and estimated arrival time displayed on the user's terminal

[0407] Step 7:

[0408] Real-time updates (server and device)

[0409] Input: Real-time information from each traffic data API, periodic location data updates from user devices

[0410] Processing: The server continuously updates new traffic information in real time, and the device periodically obtains new optimal route information from the server. This allows the user to always be guided to the optimal route based on the latest traffic conditions.

[0411] Specific operations: Automatically updating the server database, sending periodic requests from the terminal, and processing the server's responses

[0412] Output: Real-time updated navigation information

[0413] (Application example 1)

[0414] 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."

[0415] As autonomous vehicles become more widespread, it is becoming increasingly necessary to provide optimal routes in real time to deal with traffic congestion and accidents. However, conventional navigation systems lack real-time data analysis and integration with vehicle control systems, making it difficult to provide efficient routes. To address this issue, there is a need for an advanced navigation system that can collect and analyze traffic information in real time and further integrate it into the autonomous vehicle control system.

[0416] 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.

[0417] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, predicting congestion, and calculating an optimal route, a means for providing the calculated optimal route to a user terminal, and a means for integrating the optimal route information into a vehicle control system. This enables real-time collection and analysis of traffic information and provision of optimal routes to autonomous vehicles. Furthermore, optimal route calculation using a network graph and operation optimization of autonomous vehicles enable efficient and safe operation.

[0418] "Traffic information" refers to information related to road traffic conditions, such as road congestion, traffic accident information, and road construction information.

[0419] "Traffic flow" refers to the flow of vehicles on a road during a specific time period, and is a concept that includes the associated speed and congestion.

[0420] A "traffic jam" is a condition in which the flow of vehicles on a road comes to a near halt or becomes very slow.

[0421] An "optimal route" is a route from a specific origin to a destination that is deemed to be the most efficient route based on certain conditions.

[0422] "User terminal" refers to a device that receives information from the navigation system, such as a smartphone or an in-vehicle display.

[0423] A "vehicle control system" is a system that controls the operation, speed, and direction of a vehicle, and includes autonomous driving technology.

[0424] A "network graph" is a mathematical structure that represents a road network as vertices and edges, and is used to analyze traffic flow and calculate optimal routes.

[0425] In graph theory, a "weighted graph" is a graph in which each edge is weighted, and is used to represent road congestion and travel time.

[0426] "Real-time" refers to responding immediately to ongoing events, and is an important concept in traffic information and autonomous driving control.

[0427] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, a user terminal, and a communication network connecting them.

[0428] First, the server periodically collects traffic information from a traffic information service (API). This data includes information on road congestion, traffic accidents, road construction, and more. The server makes API requests for this information every 10 minutes, obtains congestion levels and speed information for each road section, and stores this information in a database. The collected traffic information is used to model traffic flow, predict traffic congestion, and calculate optimal routes.

[0429] The server analyzes traffic flow based on traffic information and uses an algorithm (e.g., Dijkstra algorithm or A algorithm) to optimize the navigation route using graph theory. This generates multiple route candidates and simulates traffic flow for each route. The server then determines the optimal route based on the simulation results.

[0430] When a user inputs their destination into their smartphone or the device of an autonomous vehicle, the device sends their current location and destination to a server. The server calculates the optimal route in real time based on the latest traffic information and returns the results to the user's device. The device then visually displays the optimal route and estimated arrival time to the user. Furthermore, this optimal route information is integrated into the vehicle control system, helping the autonomous vehicle optimize its operation in real time.

[0431] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. For example, enter the following as a prompt:

[0432] current_location = "Tokyo"

[0433] destination = "Yokohama"

[0434] route = nav_sys.provide_navigation(current_location, destination)

[0435] print("Optimal Route from Tokyo to Yokohama:", route)

[0436] This system uses programs written in the Python language. On the server side, it uses the requests module for making API requests and the networkx module for traffic flow modeling and route calculation. For the database, it uses an SQL-based database (e.g., PostgreSQL) to efficiently store traffic information.

[0437] As described above, this invention maximizes traffic efficiency and realizes efficient and safe operation by collecting and analyzing traffic information in real time and providing optimal routes to autonomous vehicles.

[0438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0439] Step 1:

[0440] The server collects traffic information from a traffic information service. Specifically, the server sends API requests every 10 minutes and receives information on road congestion, traffic accidents, road construction, etc. The input is API response data, which is structured in JSON format, etc. The server stores this in a database.

[0441] Step 2:

[0442] The server analyzes traffic flow based on the collected traffic information. It retrieves information stored in a database and analyzes congestion levels and speed information for each road section. The input here is past traffic data retrieved from the database, and the output is an analyzed traffic flow model. Past data trends are used for the analysis.

[0443] Step 3:

[0444] The server uses a traffic flow model to predict congestion and generate multiple route candidates. This uses the Dijkstra algorithm and the A algorithm. The input is the traffic flow model, and the output is predicted congestion information and multiple route candidates to avoid it. The route candidate simulation uses the networkx module in Python.

[0445] Step 4:

[0446] The user inputs their destination into their smartphone or in-car device. The user device acquires the user's current location and destination, and sends a request to the server. The input is the destination information from the user, and the output is the request data sent to the server. The device uses GPS and location information services.

[0447] Step 5:

[0448] The server calculates the optimal route in real time based on the current location and destination received from the user's device. This calculation uses the latest traffic information and existing congestion prediction data. The input is the user's current location and destination, as well as a traffic flow model, and the output is optimal route information. The calculation is performed using a network graph.

[0449] Step 6:

[0450] The server returns the calculated optimal route information to the user's device. The user's device receives this information and displays it visually. The output is the optimal route and expected arrival time, displayed in a format that is easy for the user to understand. This is often displayed in a browser or a dedicated app.

[0451] Step 7:

[0452] The user device sends the optimal route information to the vehicle control system, which then optimizes the operation of the autonomous vehicle based on the received information. The input is the optimal route information from the device, and the output is vehicle operation control commands. The system adjusts operation in real time.

[0453] 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.

[0454] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0455] 1. Data collection (server)

[0456] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0457] 2. Traffic flow analysis and congestion prediction (server)

[0458] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0459] 3. Calculating the optimal route (server)

[0460] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0461] 4. Emotion engine (server and terminal)

[0462] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows it to evaluate the user's stress level and fatigue. For example, if the user is feeling stressed while driving, the emotion engine will detect this and send feedback to the navigation system.

[0463] 5. Navigation interface adjustment (terminal)

[0464] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0465] 6. Providing and updating optimal routes (server)

[0466] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0467] 7. System Evolution

[0468] This navigation system is also designed with future integration with autonomous driving technology in mind. Even in a future where autonomous vehicles are on the rise, it is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision. Furthermore, by utilizing an emotion engine, more user-friendly navigation can be achieved, reducing driver stress.

[0469] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0470] The processing flow will be explained below.

[0471] Step 1: Collecting traffic information (server)

[0472] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0473] Step 2: Traffic flow analysis (server)

[0474] The server analyzes the traffic information it receives and models the traffic flow for each road section, based on factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. This allows it to evaluate the passability of each road section.

[0475] Step 3: Calculate the optimal route (server)

[0476] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0477] Step 4: Collect and analyze emotion data (device)

[0478] The device uses a camera and microphone to collect emotion data from the user's facial expressions and tone of voice, and the device then sends this data to an emotion engine to evaluate the user's stress level and fatigue.

[0479] Step 5: Feedback from the emotion engine (device)

[0480] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or alternative routes to avoid crowds.

[0481] Step 6: Providing the best route (server)

[0482] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device then visually displays the received optimal route information to the user, including route display on a map and voice guidance.

[0483] Step 7: View directions (device)

[0484] The device visually displays the optimal route provided by the server to the user, allowing the user to reach their destination safely and efficiently using visual and audio information. The display includes route details and estimated arrival time.

[0485] Step 8: Real-time updates (server)

[0486] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0487] Step 9: Periodic analysis by the emotion engine (device)

[0488] While the user is driving, the emotion engine periodically analyzes the user's emotional data, allowing it to respond immediately to changes in the user's emotional state. For example, if the user's fatigue level increases during a long drive, the engine can suggest taking a break.

[0489] In this way, by combining traffic information and emotional data, we are able to create a system that provides a safer and less stressful driving experience.

[0490] Example 2

[0491] 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."

[0492] Conventional navigation systems can collect traffic information in real time and provide optimal routes, but they cannot take into account the user's emotional state. As a result, users are required to use the same navigation interface even when they are under high stress or fatigue, which results in insufficient stress relief and safety improvement while driving.

[0493] 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.

[0494] In this invention, the server includes means for collecting traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for adjusting the calculated optimal route in accordance with the emotional state of the user, means for providing the adjusted optimal route to the user terminal, and means for providing an adjusted navigation interface. This makes it possible to provide appropriate navigation information in accordance with the emotional state of the user, reduce stress while driving, and improve safety.

[0495] "Traffic information" is data relating to road usage, such as road congestion, accident information, and construction information.

[0496] "Traffic flow" refers to data that indicates dynamic conditions such as the flow, speed, and density of vehicles on roads.

[0497] A "user terminal" is a device held by a user who uses a navigation system, such as a smartphone or a car navigation system.

[0498] An "optimal route" refers to a route from a starting point to a destination that takes the least amount of time or meets other specified criteria.

[0499] "Emotional state" refers to the psychological and emotional state that a user feels, such as stress level or fatigue level.

[0500] The "navigation interface" refers to a user interface such as a map or voice guidance displayed to the user through a navigation system.

[0501] "Analysis means" refers to technical means for processing data based on collected traffic information and modeling traffic flow.

[0502] "Simulation means" refers to a technical means for virtually reproducing actual traffic flow for multiple route candidates and evaluating the effectiveness of each route.

[0503] "Real-time" means that the process from data acquisition to analysis and provision is carried out without delay.

[0504] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0505] The server periodically collects traffic condition data from an external traffic information service. This data includes road congestion, accident information, construction information, and so on. The server sends an API request to obtain the data and stores it in a database. Specifically, the server obtains the latest traffic data using the endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") for calling the traffic information API.

[0506] The server then analyzes the traffic information using the Python pandas library to model traffic flow, which allows the server to evaluate the passability of each road section and predict the likelihood of congestion.

[0507] The server calculates the optimal route using the results of traffic flow analysis. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to calculate the route. Python's NetworkX library is used for this. This allows the optimal route from the user's starting point to their destination to be calculated and travel time to be evaluated.

[0508] The emotion engine is used to recognize the user's emotions. The user's device is equipped with a camera and microphone, which are used to collect the user's facial expressions and tone of voice. The collected data is sent to a server, which then applies an emotion analysis algorithm (for example, a model using TensorFlow) to evaluate the user's emotional state. This allows the user's stress level and fatigue level to be determined.

[0509] The user device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the displayed information and change the voice guidance to a slower tone. The device will also visually display the received optimal route information and highlight it on a map.

[0510] The system responds to changes in traffic conditions in real time, and the server periodically recollects traffic information and updates the optimal route based on the analysis results, allowing users to continue driving based on the latest information.

[0511] Examples of prompts that can be used to generate systems using generative AI models include:

[0512] "Generate a program for a real-time traffic information analysis system that calculates and presents optimal routes to users. The system will have an emotion engine that will adjust the navigation interface based on the user's emotional state."

[0513] Such a system is expected to provide an efficient and stress-free driving experience and also contribute to reducing environmental impact by minimizing fuel consumption.

[0514] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0515] Step 1: Data collection

[0516] The server sends an API request to an external traffic information service. The input is the API endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") and an authentication token. The output is traffic information (JSON format data). Specifically, the server sends an HTTP GET request and stores the traffic information received as a response in a local database.

[0517] Step 2: Traffic flow analysis

[0518] The server analyzes the stored traffic information. The input is the traffic information obtained in the previous step (JSON data stored in the database). The output is the analysis results (passability and congestion prediction data for each road section). Specifically, the server processes the traffic data using Python's pandas library and calculates the number of vehicles and speeds on each road section.

[0519] Step 3: Calculate the optimal route

[0520] The server receives the user's starting point and destination. The input is the starting point and destination information sent from the user's device. The output is the optimal route (a list of GPS coordinates). Specifically, the server uses Python's NetworkX library to run the Dijkstra algorithm to calculate the optimal route.

[0521] Step 4: Collecting emotion data

[0522] The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The inputs include the user's facial images and voice data. The output is the collected emotion data. Specifically, the device captures the user's facial expressions with the front camera and records the tone of voice with the microphone.

[0523] Step 5: Sentiment Analysis

[0524] The device sends the collected facial and voice data to a server. The input is the user's facial expression data and voice data. The output is an evaluation of the user's emotional state. Specifically, the server uses TensorFlow to run an emotion analysis model and evaluate whether the user is stressed or tired.

[0525] Step 6: Adjusting the navigation interface

[0526] The device receives feedback from the emotion engine. The input is the evaluation of the emotional state sent by the server. The output is an adjusted navigation interface. Specifically, the device changes the tone of the voice guidance and simplifies the information display accordingly.

[0527] Step 7: Provide optimal route

[0528] The server sends the calculated optimal route to the user's device. The input is the optimal route (a list of GPS coordinates). The output is the route information displayed on the user's device. Specifically, the server sends the optimal route data to the user's device in real time and displays it on a map.

[0529] Step 8: Real-time updates

[0530] The server updates route information in real time according to changes in traffic conditions. The input is newly collected traffic information. The output is updated optimal route information sent to the user terminal. Specifically, the server periodically reanalyzes traffic data and updates route information as necessary.

[0531] (Application example 2)

[0532] 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."

[0533] While conventional navigation systems analyze traffic information in real time and provide optimal routes, they are unable to optimize the route taking into account the user's emotions and stress levels. As a result, traffic jams and unexpected traffic conditions can cause significant stress for users, making it difficult to provide a relaxing travel experience. To address these issues, the present invention aims to analyze the user's emotions and provide navigation information that corresponds to their emotional state.

[0534] 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 traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for providing the calculated optimal route to a user terminal, means for analyzing the user's emotions, means for adjusting navigation information based on the analyzed emotions, and means for displaying and providing navigation information on a smart display device by voice. This provides an optimal route that takes the user's emotions into consideration, enabling efficient travel while reducing stress.

[0535] "Traffic information" is data related to traffic conditions, such as road congestion, accident information, and construction information.

[0536] "Traffic flow" refers to information such as the flow, density, and speed of vehicles passing through a specific section.

[0537] The "means for analyzing emotions" is a means for analyzing the user's facial expressions, tone of voice, etc., and evaluating the user's emotional state.

[0538] The "optimal route" refers to the route that can take you from the starting point to the destination in the shortest time, taking into account traffic conditions.

[0539] A "smart display device" is a device that provides visual and audio information to a user, including, for example, smart glasses and head-mounted displays.

[0540] "Navigation information" includes information such as route guidance, traffic information, and current location, and is information that shows the way to the user's destination.

[0541] A "user terminal" is a part of the navigation system, and is a device that provides optimal routes and traffic information provided by the server to the user.

[0542] "Route candidates" indicate multiple possible routes to a destination, and are evaluated taking into consideration factors such as travel time and traffic congestion forecasts for each route.

[0543] "Traffic information service" is an external service that provides data related to traffic conditions. Data is obtained from this service and used for analysis on the server.

[0544] The "server" is the central part of the entire system, and is an information processing device that collects and analyzes traffic information, calculates optimal routes, analyzes user emotions, and so on.

[0545] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0546] 1. Data collection (server)

[0547] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0548] 2. Traffic flow analysis and congestion prediction (server)

[0549] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0550] 3. Calculating the optimal route (server)

[0551] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0552] 4. Emotion engine (server and terminal)

[0553] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows the system to assess the user's stress level and fatigue. For example, if a user says, "Today's traffic jam is tiring" while driving, the system will analyze the user's stress level from the tone of voice. The system may reevaluate the route and recommend a scenic route that is relaxing.

[0554] 5. Navigation interface adjustment (terminal)

[0555] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0556] 6. Providing and updating optimal routes (server)

[0557] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0558] 7. Use of smart display devices

[0559] Smart display devices, such as smart glasses and head-mounted displays (HMDs), can be used to visually display real-time traffic information and recommended routes, allowing users to obtain navigation information more intuitively.

[0560] Prompt Sentence Examples

[0561] If a user says, "Today's traffic jam is tiring," the system will calculate the optimal route in real time and suggest scenic alternatives to reduce stress, providing visual instructions and audio guidance through smart glasses or an HMD.

[0562] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0563] Step 1: Data Collection (Server)

[0564] The server periodically sends API requests from external traffic information providers to obtain traffic condition data (road congestion, accident information, construction information). The obtained data is stored in a database. The input is the API request, and the output is the traffic information stored in the database.

[0565] Step 2: Traffic flow analysis and congestion prediction (server)

[0566] The server analyzes traffic information stored in the database and models the traffic flow (number of vehicles, vehicle speed, and likelihood of congestion) for each road section. This evaluates the passability of each section. The input is the stored traffic information, and the output is the passability evaluation result for each section.

[0567] Step 3: Calculate the optimal route (server)

[0568] The server calculates the optimal route from the departure point to the destination based on the results of traffic flow analysis. Using algorithms such as the Dijkstra algorithm and A algorithm, it generates multiple route candidates, simulates each one, and selects the optimal one. The input is the traffic flow analysis results, and the output is optimal route information.

[0569] Step 4: User sentiment analysis (device)

[0570] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. The step-analyzed emotional state (stress level and fatigue level) is evaluated. The input is the user's facial expressions and tone of voice, and the output is the evaluation result of the emotional state.

[0571] Step 5: Adjusting the navigation interface (device)

[0572] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device simplifies the displayed information and changes the voice guidance to a slower tone. The input is the emotion analysis result, and the output is the adjusted navigation interface.

[0573] Step 6: Providing and updating optimal routes (server, terminal)

[0574] When a user inputs a destination into the device, the device sends that information to the server. The server recalculates the optimal route in real time and returns the results to the device. The device then provides the user with route information through visual and audio guidance. The route information is continually updated as traffic conditions change. The input is destination information and current location information, and the output is real-time optimal route information.

[0575] Step 7: Using a Smart Display Device (Device)

[0576] Using smart display devices (smart glasses or head-mounted displays), navigation information is displayed in real time, including optimal routes, traffic information, and interface adjustments according to emotional states. The input is optimal route information and emotion analysis results from the server, and the output is navigation information displayed on the display.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] [Third embodiment]

[0581] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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."

[0593] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0594] 1. Data collection (server)

[0595] The server periodically collects traffic information from external traffic information providers (e.g., traffic data API). This data includes road congestion status, traffic accident information, road construction information, etc. Upon receiving this information, the server stores it in a database and prepares it for traffic flow analysis.

[0596] For example, the server makes an API request every 10 minutes to obtain traffic data, and then obtains congestion and speed information for each road section and stores it in a database.

[0597] 2. Traffic flow analysis and congestion prediction (server)

[0598] The server analyzes the collected traffic information and models traffic flow. This includes predicting traffic congestion and generating multiple route candidates to avoid it. In this process, the server uses algorithms (e.g., Dijkstra algorithm and A algorithm) that utilize graph theory to optimize navigation routes.

[0599] For example, when a vehicle travels from point A to point B, the server generates multiple route candidates and simulates traffic flow for each route, thereby selecting the route that will reach the destination in the shortest time while avoiding traffic congestion.

[0600] 3. Providing optimal routes (server and terminal)

[0601] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user.

[0602] When a user requests a route "from home to office" on a smartphone app, the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user.

[0603] 4. System Evolution

[0604] This navigation system has been designed with future integration with autonomous driving technology in mind, and is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision, even in a future where autonomous vehicles become more common.

[0605] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0606] The processing flow will be explained below.

[0607] Step 1: Collecting traffic information (server)

[0608] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0609] Step 2: Traffic flow analysis (server)

[0610] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0611] Step 3: Calculate the optimal route (server)

[0612] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0613] Step 4: Receiving a request from the user (device)

[0614] The user inputs a destination using the device and sends a navigation request. The device sends the current location and destination information to the server. The server receives the request and calculates the optimal route in real time.

[0615] Step 5: Providing the optimal route (server)

[0616] The server calculates the optimal route based on the user's request and returns it to the device. The device then analyzes the route information and visually displays it to the user, including route details and estimated arrival times.

[0617] Step 6: View directions (device)

[0618] The device visually displays the optimal route provided by the server to the user, including route display on a map and audio guidance, allowing the user to reach their destination safely and efficiently using visual and audio information.

[0619] Step 7: Real-time updates (server)

[0620] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0621] Example 1

[0622] 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."

[0623] Modern transportation systems are prone to frequent road delays due to traffic congestion and accidents, resulting in increased driver stress and fuel consumption. Furthermore, a lack of means to provide accurate traffic information in real time and suggest optimal routes has led to a decline in traffic efficiency. Therefore, there is a need for a system that can collect traffic information in real time and efficiently analyze it to provide drivers with optimal routes.

[0624] 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.

[0625] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, a means for modeling the traffic flow using graph theory, a means for predicting congestion, a means for calculating an optimal route using an algorithm for optimizing a navigation route using the modeled traffic flow, and a means for providing the calculated optimal route to a user terminal, thereby enabling accurate analysis of traffic information in real time and providing an optimal route to a driver.

[0626] "Traffic information" is a general term for data related to vehicle movement, such as road congestion, traffic accident information, road construction information, and speed limits.

[0627] "Graph theory" is a theory for modeling and analyzing various problems using mathematical structures consisting of nodes and edges connecting them.

[0628] "Modeling" is a technique for simulating and analyzing the behavior of actual systems and processes by representing them using mathematical and logical structures.

[0629] A "navigation route" is a route from a starting point to a destination point, which is a route suggested for the driver to travel.

[0630] An "optimization algorithm" is a computational method for finding the most efficient solution under given conditions, and includes the Dijkstra algorithm and the A algorithm in particular.

[0631] A "user terminal" is a device, such as a smartphone or tablet, that a user directly operates and receives information.

[0632] "Real-time" refers to immediate processing and response at the moment an event occurs.

[0633] "Traffic flow" is a concept that indicates the movement of vehicles on a road during a specific time period, and includes factors such as congestion, speed, and vehicle density.

[0634] "Simulation" is a method of virtually recreating real-world systems and processes and testing and analyzing their behavior.

[0635] A "server" is a computer system whose role is to provide data and services to other computers on a network.

[0636] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0637] First, the operation of the entire system requires a server, user terminals (e.g., smartphones), and a communications infrastructure such as the Internet that connects them. The server obtains traffic information from external traffic information services (e.g., traffic data API) and stores it in a database. The traffic information includes road congestion status, traffic accident information, and road construction information.

[0638] Specifically, the server sends a request to the traffic data API every 10 minutes and obtains the necessary information from the returned response data. This information undergoes a series of data processing and is stored in a database. The server then analyzes traffic flow based on the collected data and models it using graph theory. This makes it possible to understand the congestion level and vehicle speed for each road section and predict traffic congestion.

[0639] Machine learning models can be used to predict traffic congestion, allowing for highly accurate analysis. Furthermore, based on the traffic flow model, optimization algorithms such as the Dijkstra algorithm and the A algorithm are used to generate multiple route candidates, and a process is then performed to simulate traffic flow for each candidate. This allows the system to propose the optimal route to the user.

[0640] When a user inputs a destination using a smartphone app, the device sends a request to the server. Upon receiving the request, the server calculates the optimal route in real time and returns the results to the user's device. The device plots the returned optimal route information on a map and visually displays the estimated arrival time to the user.

[0641] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. At this time, the following prompt sentence is generated:

[0642] 1. Data collection prompt:

[0643] The server obtains road congestion and speed information from the traffic data API every 10 minutes and stores it in a database.

[0644] 2. Traffic flow analysis prompt:

[0645] Based on graph theory, the Dijkstra algorithm is used to simulate and determine the optimal route from point A to point B that avoids congestion.

[0646] 3. Prompt for optimal route:

[0647] When a user requests a route from home to the office on their smartphone, the server calculates the optimal route and estimated arrival time and returns it to the device, which then displays this information to the user.

[0648] This system will enable professional drivers and consumers to enjoy an efficient and stress-free driving experience. It will also contribute to reducing environmental impact by minimizing fuel consumption. Furthermore, it is designed with collaboration with autonomous driving technology in mind, and is expected to improve the efficiency and safety of future transportation systems.

[0649] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0650] Step 1:

[0651] Data collection (server)

[0652] Input: Endpoint URL and access key of external traffic information service (e.g. traffic data API)

[0653] Processing: The server sends a request to the traffic data API every 10 minutes. This request includes the specified latitude and longitude range, date and time, and the required traffic information items (e.g., road congestion level, accident information, construction information).

[0654] Specific operations: Sending HTTP requests and receiving API responses

[0655] Output: Dataset of acquired traffic information (e.g., response data in JSON format)

[0656] Step 2:

[0657] Traffic data storage (server)

[0658] Input: Traffic information dataset obtained in step 1

[0659] Processing: The server analyzes the acquired traffic information and extracts necessary data items (e.g., road section ID, congestion level, speed, accident information). The extracted data is saved in a database.

[0660] Specific operations: database update operations, data insertion and updating

[0661] Output: Updated and saved database state

[0662] Step 3:

[0663] Traffic flow analysis and modeling (server)

[0664] Input: Traffic information database saved in step 2

[0665] Processing: The server uses the stored traffic information to model traffic flow using graph theory. This involves representing road sections as edges and intersections as nodes, and assigning congestion levels and vehicle speeds to each edge.

[0666] Specific operations: Creating a graph structure, setting attributes of edges and nodes

[0667] Output: Modeled traffic flow graph

[0668] Step 4:

[0669] Traffic congestion prediction (server)

[0670] Input: Traffic flow graph modeled in step 3

[0671] Processing: The server uses a machine learning model to predict congestion based on the traffic flow graph. The model, which has learned from past data, calculates the probability of congestion occurring for each time period.

[0672] Specific operation: Applying machine learning models and calculating prediction results

[0673] Output: Predicted data of traffic congestion probability

[0674] Step 5:

[0675] Optimal route calculation (server)

[0676] Input: Prediction data from Step 4, route request from user device (current location and destination)

[0677] Processing: The server uses the Dijkstra algorithm and the A algorithm to generate multiple route candidates based on the modeled traffic flow graph and congestion prediction data, performs traffic flow simulations for each route, and selects the optimal route.

[0678] Specific operations: generating multiple route candidates, simulating each route, and selecting the optimal route

[0679] Output: Data on optimal route and expected arrival time

[0680] Step 6:

[0681] Providing optimal route information (server and terminal)

[0682] Input: Optimal route data generated in step 5, request information from user terminal

[0683] Processing: The server generates optimal route information and sends it back to the user's device. The device plots the received optimal route information on a map and provides visual navigation to the user.

[0684] Specific operations: Data transmission by server, route plotting by device, display on user interface

[0685] Output: Optimal route and estimated arrival time displayed on the user's terminal

[0686] Step 7:

[0687] Real-time updates (server and device)

[0688] Input: Real-time information from each traffic data API, periodic location data updates from user devices

[0689] Processing: The server continuously updates new traffic information in real time, and the device periodically obtains new optimal route information from the server. This allows the user to always be guided to the optimal route based on the latest traffic conditions.

[0690] Specific operations: Automatically updating the server database, sending periodic requests from the terminal, and processing the server's responses

[0691] Output: Real-time updated navigation information

[0692] (Application example 1)

[0693] 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."

[0694] As autonomous vehicles become more widespread, it is becoming increasingly necessary to provide optimal routes in real time to deal with traffic congestion and accidents. However, conventional navigation systems lack real-time data analysis and integration with vehicle control systems, making it difficult to provide efficient routes. To address this issue, there is a need for an advanced navigation system that can collect and analyze traffic information in real time and further integrate it into the autonomous vehicle control system.

[0695] 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.

[0696] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, predicting congestion, and calculating an optimal route, a means for providing the calculated optimal route to a user terminal, and a means for integrating the optimal route information into a vehicle control system. This enables real-time collection and analysis of traffic information and provision of optimal routes to autonomous vehicles. Furthermore, optimal route calculation using a network graph and operation optimization of autonomous vehicles enable efficient and safe operation.

[0697] "Traffic information" refers to information related to road traffic conditions, such as road congestion, traffic accident information, and road construction information.

[0698] "Traffic flow" refers to the flow of vehicles on a road during a specific time period, and is a concept that includes the associated speed and congestion.

[0699] A "traffic jam" is a condition in which the flow of vehicles on a road comes to a near halt or becomes very slow.

[0700] An "optimal route" is a route from a specific origin to a destination that is deemed to be the most efficient route based on certain conditions.

[0701] "User terminal" refers to a device that receives information from the navigation system, such as a smartphone or an in-vehicle display.

[0702] A "vehicle control system" is a system that controls the operation, speed, and direction of a vehicle, and includes autonomous driving technology.

[0703] A "network graph" is a mathematical structure that represents a road network as vertices and edges, and is used to analyze traffic flow and calculate optimal routes.

[0704] In graph theory, a "weighted graph" is a graph in which each edge is weighted, and is used to represent road congestion and travel time.

[0705] "Real-time" refers to responding immediately to ongoing events, and is an important concept in traffic information and autonomous driving control.

[0706] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, a user terminal, and a communication network connecting them.

[0707] First, the server periodically collects traffic information from a traffic information service (API). This data includes information on road congestion, traffic accidents, road construction, and more. The server makes API requests for this information every 10 minutes, obtains congestion levels and speed information for each road section, and stores this information in a database. The collected traffic information is used to model traffic flow, predict traffic congestion, and calculate optimal routes.

[0708] The server analyzes traffic flow based on traffic information and uses an algorithm (e.g., Dijkstra algorithm or A algorithm) to optimize the navigation route using graph theory. This generates multiple route candidates and simulates traffic flow for each route. The server then determines the optimal route based on the simulation results.

[0709] When a user inputs their destination into their smartphone or the device of an autonomous vehicle, the device sends their current location and destination to a server. The server calculates the optimal route in real time based on the latest traffic information and returns the results to the user's device. The device then visually displays the optimal route and estimated arrival time to the user. Furthermore, this optimal route information is integrated into the vehicle control system, helping the autonomous vehicle optimize its operation in real time.

[0710] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. For example, enter the following as a prompt:

[0711] current_location = "Tokyo"

[0712] destination = "Yokohama"

[0713] route = nav_sys.provide_navigation(current_location, destination)

[0714] print("Optimal Route from Tokyo to Yokohama:", route)

[0715] This system uses programs written in the Python language. On the server side, it uses the requests module for making API requests and the networkx module for traffic flow modeling and route calculation. For the database, it uses an SQL-based database (e.g., PostgreSQL) to efficiently store traffic information.

[0716] As described above, this invention maximizes traffic efficiency and realizes efficient and safe operation by collecting and analyzing traffic information in real time and providing optimal routes to autonomous vehicles.

[0717] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0718] Step 1:

[0719] The server collects traffic information from a traffic information service. Specifically, the server sends API requests every 10 minutes and receives information on road congestion, traffic accidents, road construction, etc. The input is API response data, which is structured in JSON format, etc. The server stores this in a database.

[0720] Step 2:

[0721] The server analyzes traffic flow based on the collected traffic information. It retrieves information stored in a database and analyzes congestion levels and speed information for each road section. The input here is past traffic data retrieved from the database, and the output is an analyzed traffic flow model. Past data trends are used for the analysis.

[0722] Step 3:

[0723] The server uses a traffic flow model to predict congestion and generate multiple route candidates. This uses the Dijkstra algorithm and the A algorithm. The input is the traffic flow model, and the output is predicted congestion information and multiple route candidates to avoid it. The route candidate simulation uses the networkx module in Python.

[0724] Step 4:

[0725] The user inputs their destination into their smartphone or in-car device. The user device acquires the user's current location and destination, and sends a request to the server. The input is the destination information from the user, and the output is the request data sent to the server. The device uses GPS and location information services.

[0726] Step 5:

[0727] The server calculates the optimal route in real time based on the current location and destination received from the user's device. This calculation uses the latest traffic information and existing congestion prediction data. The input is the user's current location and destination, as well as a traffic flow model, and the output is optimal route information. The calculation is performed using a network graph.

[0728] Step 6:

[0729] The server returns the calculated optimal route information to the user's device. The user's device receives this information and displays it visually. The output is the optimal route and expected arrival time, displayed in a format that is easy for the user to understand. This is often displayed in a browser or a dedicated app.

[0730] Step 7:

[0731] The user device sends the optimal route information to the vehicle control system, which then optimizes the operation of the autonomous vehicle based on the received information. The input is the optimal route information from the device, and the output is vehicle operation control commands. The system adjusts operation in real time.

[0732] 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.

[0733] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0734] 1. Data collection (server)

[0735] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0736] 2. Traffic flow analysis and congestion prediction (server)

[0737] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0738] 3. Calculating the optimal route (server)

[0739] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0740] 4. Emotion engine (server and terminal)

[0741] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows it to evaluate the user's stress level and fatigue. For example, if the user is feeling stressed while driving, the emotion engine will detect this and send feedback to the navigation system.

[0742] 5. Navigation interface adjustment (terminal)

[0743] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0744] 6. Providing and updating optimal routes (server)

[0745] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0746] 7. System Evolution

[0747] This navigation system is also designed with future integration with autonomous driving technology in mind. Even in a future where autonomous vehicles are on the rise, it is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision. Furthermore, by utilizing an emotion engine, more user-friendly navigation can be achieved, reducing driver stress.

[0748] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0749] The processing flow will be explained below.

[0750] Step 1: Collecting traffic information (server)

[0751] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0752] Step 2: Traffic flow analysis (server)

[0753] The server analyzes the traffic information it receives and models the traffic flow for each road section, based on factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. This allows it to evaluate the passability of each road section.

[0754] Step 3: Calculate the optimal route (server)

[0755] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0756] Step 4: Collect and analyze emotion data (device)

[0757] The device uses a camera and microphone to collect emotion data from the user's facial expressions and tone of voice, and the device then sends this data to an emotion engine to evaluate the user's stress level and fatigue.

[0758] Step 5: Feedback from the emotion engine (device)

[0759] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or alternative routes to avoid crowds.

[0760] Step 6: Providing the best route (server)

[0761] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device then visually displays the received optimal route information to the user, including route display on a map and voice guidance.

[0762] Step 7: View directions (device)

[0763] The device visually displays the optimal route provided by the server to the user, allowing the user to reach their destination safely and efficiently using visual and audio information. The display includes route details and estimated arrival time.

[0764] Step 8: Real-time updates (server)

[0765] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0766] Step 9: Periodic analysis by the emotion engine (device)

[0767] While the user is driving, the emotion engine periodically analyzes the user's emotional data, allowing it to respond immediately to changes in the user's emotional state. For example, if the user's fatigue level increases during a long drive, the engine can suggest taking a break.

[0768] In this way, by combining traffic information and emotional data, we are able to create a system that provides a safer and less stressful driving experience.

[0769] Example 2

[0770] 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."

[0771] Conventional navigation systems can collect traffic information in real time and provide optimal routes, but they cannot take into account the user's emotional state. As a result, users are required to use the same navigation interface even when they are under high stress or fatigue, which results in insufficient stress relief and safety improvement while driving.

[0772] 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.

[0773] In this invention, the server includes means for collecting traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for adjusting the calculated optimal route in accordance with the emotional state of the user, means for providing the adjusted optimal route to the user terminal, and means for providing an adjusted navigation interface. This makes it possible to provide appropriate navigation information in accordance with the emotional state of the user, reduce stress while driving, and improve safety.

[0774] "Traffic information" is data relating to road usage, such as road congestion, accident information, and construction information.

[0775] "Traffic flow" refers to data that indicates dynamic conditions such as the flow, speed, and density of vehicles on roads.

[0776] A "user terminal" is a device held by a user who uses a navigation system, such as a smartphone or a car navigation system.

[0777] An "optimal route" refers to a route from a starting point to a destination that takes the least amount of time or meets other specified criteria.

[0778] "Emotional state" refers to the psychological and emotional state that a user feels, such as stress level or fatigue level.

[0779] The "navigation interface" refers to a user interface such as a map or voice guidance displayed to the user through a navigation system.

[0780] "Analysis means" refers to technical means for processing data based on collected traffic information and modeling traffic flow.

[0781] "Simulation means" refers to a technical means for virtually reproducing actual traffic flow for multiple route candidates and evaluating the effectiveness of each route.

[0782] "Real-time" means that the process from data acquisition to analysis and provision is carried out without delay.

[0783] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0784] The server periodically collects traffic condition data from an external traffic information service. This data includes road congestion, accident information, construction information, and so on. The server sends an API request to obtain the data and stores it in a database. Specifically, the server obtains the latest traffic data using the endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") for calling the traffic information API.

[0785] The server then analyzes the traffic information using the Python pandas library to model traffic flow, which allows the server to evaluate the passability of each road section and predict the likelihood of congestion.

[0786] The server calculates the optimal route using the results of traffic flow analysis. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to calculate the route. Python's NetworkX library is used for this. This allows the optimal route from the user's starting point to their destination to be calculated and travel time to be evaluated.

[0787] The emotion engine is used to recognize the user's emotions. The user's device is equipped with a camera and microphone, which are used to collect the user's facial expressions and tone of voice. The collected data is sent to a server, which then applies an emotion analysis algorithm (for example, a model using TensorFlow) to evaluate the user's emotional state. This allows the user's stress level and fatigue level to be determined.

[0788] The user device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the displayed information and change the voice guidance to a slower tone. The device will also visually display the received optimal route information and highlight it on a map.

[0789] The system responds to changes in traffic conditions in real time, and the server periodically recollects traffic information and updates the optimal route based on the analysis results, allowing users to continue driving based on the latest information.

[0790] Examples of prompts that can be used to generate systems using generative AI models include:

[0791] "Generate a program for a real-time traffic information analysis system that calculates and presents optimal routes to users. The system will have an emotion engine that will adjust the navigation interface based on the user's emotional state."

[0792] Such a system is expected to provide an efficient and stress-free driving experience and also contribute to reducing environmental impact by minimizing fuel consumption.

[0793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0794] Step 1: Data collection

[0795] The server sends an API request to an external traffic information service. The input is the API endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") and an authentication token. The output is traffic information (JSON format data). Specifically, the server sends an HTTP GET request and stores the traffic information received as a response in a local database.

[0796] Step 2: Traffic flow analysis

[0797] The server analyzes the stored traffic information. The input is the traffic information obtained in the previous step (JSON data stored in the database). The output is the analysis results (passability and congestion prediction data for each road section). Specifically, the server processes the traffic data using Python's pandas library and calculates the number of vehicles and speeds on each road section.

[0798] Step 3: Calculate the optimal route

[0799] The server receives the user's starting point and destination. The input is the starting point and destination information sent from the user's device. The output is the optimal route (a list of GPS coordinates). Specifically, the server uses Python's NetworkX library to run the Dijkstra algorithm to calculate the optimal route.

[0800] Step 4: Collecting emotion data

[0801] The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The inputs include the user's facial images and voice data. The output is the collected emotion data. Specifically, the device captures the user's facial expressions with the front camera and records the tone of voice with the microphone.

[0802] Step 5: Sentiment Analysis

[0803] The device sends the collected facial and voice data to a server. The input is the user's facial expression data and voice data. The output is an evaluation of the user's emotional state. Specifically, the server uses TensorFlow to run an emotion analysis model and evaluate whether the user is stressed or tired.

[0804] Step 6: Adjusting the navigation interface

[0805] The device receives feedback from the emotion engine. The input is the evaluation of the emotional state sent by the server. The output is an adjusted navigation interface. Specifically, the device changes the tone of the voice guidance and simplifies the information display accordingly.

[0806] Step 7: Provide optimal route

[0807] The server sends the calculated optimal route to the user's device. The input is the optimal route (a list of GPS coordinates). The output is the route information displayed on the user's device. Specifically, the server sends the optimal route data to the user's device in real time and displays it on a map.

[0808] Step 8: Real-time updates

[0809] The server updates route information in real time according to changes in traffic conditions. The input is newly collected traffic information. The output is updated optimal route information sent to the user terminal. Specifically, the server periodically reanalyzes traffic data and updates route information as necessary.

[0810] (Application example 2)

[0811] 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."

[0812] While conventional navigation systems analyze traffic information in real time and provide optimal routes, they are unable to optimize the route taking into account the user's emotions and stress levels. As a result, traffic jams and unexpected traffic conditions can cause significant stress for users, making it difficult to provide a relaxing travel experience. To address these issues, the present invention aims to analyze the user's emotions and provide navigation information that corresponds to their emotional state.

[0813] 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 traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for providing the calculated optimal route to a user terminal, means for analyzing the user's emotions, means for adjusting navigation information based on the analyzed emotions, and means for displaying and providing navigation information on a smart display device by voice. This provides an optimal route that takes the user's emotions into consideration, enabling efficient travel while reducing stress.

[0814] "Traffic information" is data related to traffic conditions, such as road congestion, accident information, and construction information.

[0815] "Traffic flow" refers to information such as the flow, density, and speed of vehicles passing through a specific section.

[0816] The "means for analyzing emotions" is a means for analyzing the user's facial expressions, tone of voice, etc., and evaluating the user's emotional state.

[0817] The "optimal route" refers to the route that can take you from the starting point to the destination in the shortest time, taking into account traffic conditions.

[0818] A "smart display device" is a device that provides visual and audio information to a user, including, for example, smart glasses and head-mounted displays.

[0819] "Navigation information" includes information such as route guidance, traffic information, and current location, and is information that shows the way to the user's destination.

[0820] A "user terminal" is a part of the navigation system, and is a device that provides optimal routes and traffic information provided by the server to the user.

[0821] "Route candidates" indicate multiple possible routes to a destination, and are evaluated taking into consideration factors such as travel time and traffic congestion forecasts for each route.

[0822] "Traffic information service" is an external service that provides data related to traffic conditions. Data is obtained from this service and used for analysis on the server.

[0823] The "server" is the central part of the entire system, and is an information processing device that collects and analyzes traffic information, calculates optimal routes, analyzes user emotions, and so on.

[0824] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[0825] 1. Data collection (server)

[0826] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0827] 2. Traffic flow analysis and congestion prediction (server)

[0828] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0829] 3. Calculating the optimal route (server)

[0830] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0831] 4. Emotion engine (server and terminal)

[0832] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows the system to assess the user's stress level and fatigue. For example, if a user says, "Today's traffic jam is tiring" while driving, the system will analyze the user's stress level from the tone of voice. The system may reevaluate the route and recommend a scenic route that is relaxing.

[0833] 5. Navigation interface adjustment (terminal)

[0834] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[0835] 6. Providing and updating optimal routes (server)

[0836] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[0837] 7. Use of smart display devices

[0838] Smart display devices, such as smart glasses and head-mounted displays (HMDs), can be used to visually display real-time traffic information and recommended routes, allowing users to obtain navigation information more intuitively.

[0839] Prompt Sentence Examples

[0840] If a user says, "Today's traffic jam is tiring," the system will calculate the optimal route in real time and suggest scenic alternatives to reduce stress, providing visual instructions and audio guidance through smart glasses or an HMD.

[0841] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0842] Step 1: Data Collection (Server)

[0843] The server periodically sends API requests from external traffic information providers to obtain traffic condition data (road congestion, accident information, construction information). The obtained data is stored in a database. The input is the API request, and the output is the traffic information stored in the database.

[0844] Step 2: Traffic flow analysis and congestion prediction (server)

[0845] The server analyzes traffic information stored in the database and models the traffic flow (number of vehicles, vehicle speed, and likelihood of congestion) for each road section. This evaluates the passability of each section. The input is the stored traffic information, and the output is the passability evaluation result for each section.

[0846] Step 3: Calculate the optimal route (server)

[0847] The server calculates the optimal route from the departure point to the destination based on the results of traffic flow analysis. Using algorithms such as the Dijkstra algorithm and A algorithm, it generates multiple route candidates, simulates each one, and selects the optimal one. The input is the traffic flow analysis results, and the output is optimal route information.

[0848] Step 4: User sentiment analysis (device)

[0849] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. The step-analyzed emotional state (stress level and fatigue level) is evaluated. The input is the user's facial expressions and tone of voice, and the output is the evaluation result of the emotional state.

[0850] Step 5: Adjusting the navigation interface (device)

[0851] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device simplifies the displayed information and changes the voice guidance to a slower tone. The input is the emotion analysis result, and the output is the adjusted navigation interface.

[0852] Step 6: Providing and updating optimal routes (server, terminal)

[0853] When a user inputs a destination into the device, the device sends that information to the server. The server recalculates the optimal route in real time and returns the results to the device. The device then provides the user with route information through visual and audio guidance. The route information is continually updated as traffic conditions change. The input is destination information and current location information, and the output is real-time optimal route information.

[0854] Step 7: Using a Smart Display Device (Device)

[0855] Using smart display devices (smart glasses or head-mounted displays), navigation information is displayed in real time, including optimal routes, traffic information, and interface adjustments according to emotional states. The input is optimal route information and emotion analysis results from the server, and the output is navigation information displayed on the display.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] [Fourth embodiment]

[0860] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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."

[0873] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0874] 1. Data collection (server)

[0875] The server periodically collects traffic information from external traffic information providers (e.g., traffic data API). This data includes road congestion status, traffic accident information, road construction information, etc. Upon receiving this information, the server stores it in a database and prepares it for traffic flow analysis.

[0876] For example, the server makes an API request every 10 minutes to obtain traffic data, and then obtains congestion and speed information for each road section and stores it in a database.

[0877] 2. Traffic flow analysis and congestion prediction (server)

[0878] The server analyzes the collected traffic information and models traffic flow. This includes predicting traffic congestion and generating multiple route candidates to avoid it. In this process, the server uses algorithms (e.g., Dijkstra algorithm and A algorithm) that utilize graph theory to optimize navigation routes.

[0879] For example, when a vehicle travels from point A to point B, the server generates multiple route candidates and simulates traffic flow for each route, thereby selecting the route that will reach the destination in the shortest time while avoiding traffic congestion.

[0880] 3. Providing optimal routes (server and terminal)

[0881] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user.

[0882] When a user requests a route "from home to office" on a smartphone app, the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user.

[0883] 4. System Evolution

[0884] This navigation system has been designed with future integration with autonomous driving technology in mind, and is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision, even in a future where autonomous vehicles become more common.

[0885] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[0886] The processing flow will be explained below.

[0887] Step 1: Collecting traffic information (server)

[0888] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[0889] Step 2: Traffic flow analysis (server)

[0890] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[0891] Step 3: Calculate the optimal route (server)

[0892] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[0893] Step 4: Receiving a request from the user (device)

[0894] The user inputs a destination using the device and sends a navigation request. The device sends the current location and destination information to the server. The server receives the request and calculates the optimal route in real time.

[0895] Step 5: Providing the optimal route (server)

[0896] The server calculates the optimal route based on the user's request and returns it to the device. The device then analyzes the route information and visually displays it to the user, including route details and estimated arrival times.

[0897] Step 6: View directions (device)

[0898] The device visually displays the optimal route provided by the server to the user, including route display on a map and audio guidance, allowing the user to reach their destination safely and efficiently using visual and audio information.

[0899] Step 7: Real-time updates (server)

[0900] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[0901] Example 1

[0902] 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."

[0903] Modern transportation systems are prone to frequent road delays due to traffic congestion and accidents, resulting in increased driver stress and fuel consumption. Furthermore, a lack of means to provide accurate traffic information in real time and suggest optimal routes has led to a decline in traffic efficiency. Therefore, there is a need for a system that can collect traffic information in real time and efficiently analyze it to provide drivers with optimal routes.

[0904] 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.

[0905] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, a means for modeling the traffic flow using graph theory, a means for predicting congestion, a means for calculating an optimal route using an algorithm for optimizing a navigation route using the modeled traffic flow, and a means for providing the calculated optimal route to a user terminal, thereby enabling accurate analysis of traffic information in real time and providing an optimal route to a driver.

[0906] "Traffic information" is a general term for data related to vehicle movement, such as road congestion, traffic accident information, road construction information, and speed limits.

[0907] "Graph theory" is a theory for modeling and analyzing various problems using mathematical structures consisting of nodes and edges connecting them.

[0908] "Modeling" is a technique for simulating and analyzing the behavior of actual systems and processes by representing them using mathematical and logical structures.

[0909] A "navigation route" is a route from a starting point to a destination point, which is a route suggested for the driver to travel.

[0910] An "optimization algorithm" is a computational method for finding the most efficient solution under given conditions, and includes the Dijkstra algorithm and the A algorithm in particular.

[0911] A "user terminal" is a device, such as a smartphone or tablet, that a user directly operates and receives information.

[0912] "Real-time" refers to immediate processing and response at the moment an event occurs.

[0913] "Traffic flow" is a concept that indicates the movement of vehicles on a road during a specific time period, and includes factors such as congestion, speed, and vehicle density.

[0914] "Simulation" is a method of virtually recreating real-world systems and processes and testing and analyzing their behavior.

[0915] A "server" is a computer system whose role is to provide data and services to other computers on a network.

[0916] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, user terminals, and a communication network connecting them.

[0917] First, the operation of the entire system requires a server, user terminals (e.g., smartphones), and a communications infrastructure such as the Internet that connects them. The server obtains traffic information from external traffic information services (e.g., traffic data API) and stores it in a database. The traffic information includes road congestion status, traffic accident information, and road construction information.

[0918] Specifically, the server sends a request to the traffic data API every 10 minutes and obtains the necessary information from the returned response data. This information undergoes a series of data processing and is stored in a database. The server then analyzes traffic flow based on the collected data and models it using graph theory. This makes it possible to understand the congestion level and vehicle speed for each road section and predict traffic congestion.

[0919] Machine learning models can be used to predict traffic congestion, allowing for highly accurate analysis. Furthermore, based on the traffic flow model, optimization algorithms such as the Dijkstra algorithm and the A algorithm are used to generate multiple route candidates, and a process is then performed to simulate traffic flow for each candidate. This allows the system to propose the optimal route to the user.

[0920] When a user inputs a destination using a smartphone app, the device sends a request to the server. Upon receiving the request, the server calculates the optimal route in real time and returns the results to the user's device. The device plots the returned optimal route information on a map and visually displays the estimated arrival time to the user.

[0921] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. At this time, the following prompt sentence is generated:

[0922] 1. Data collection prompt:

[0923] The server obtains road congestion and speed information from the traffic data API every 10 minutes and stores it in a database.

[0924] 2. Traffic flow analysis prompt:

[0925] Based on graph theory, the Dijkstra algorithm is used to simulate and determine the optimal route from point A to point B that avoids congestion.

[0926] 3. Prompt for optimal route:

[0927] When a user requests a route from home to the office on their smartphone, the server calculates the optimal route and estimated arrival time and returns it to the device, which then displays this information to the user.

[0928] This system will enable professional drivers and consumers to enjoy an efficient and stress-free driving experience. It will also contribute to reducing environmental impact by minimizing fuel consumption. Furthermore, it is designed with collaboration with autonomous driving technology in mind, and is expected to improve the efficiency and safety of future transportation systems.

[0929] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0930] Step 1:

[0931] Data collection (server)

[0932] Input: Endpoint URL and access key of external traffic information service (e.g. traffic data API)

[0933] Processing: The server sends a request to the traffic data API every 10 minutes. This request includes the specified latitude and longitude range, date and time, and the required traffic information items (e.g., road congestion level, accident information, construction information).

[0934] Specific operations: Sending HTTP requests and receiving API responses

[0935] Output: Dataset of acquired traffic information (e.g., response data in JSON format)

[0936] Step 2:

[0937] Traffic data storage (server)

[0938] Input: Traffic information dataset obtained in step 1

[0939] Processing: The server analyzes the acquired traffic information and extracts necessary data items (e.g., road section ID, congestion level, speed, accident information). The extracted data is saved in a database.

[0940] Specific operations: database update operations, data insertion and updating

[0941] Output: Updated and saved database state

[0942] Step 3:

[0943] Traffic flow analysis and modeling (server)

[0944] Input: Traffic information database saved in step 2

[0945] Processing: The server uses the stored traffic information to model traffic flow using graph theory. This involves representing road sections as edges and intersections as nodes, and assigning congestion levels and vehicle speeds to each edge.

[0946] Specific operations: Creating a graph structure, setting attributes of edges and nodes

[0947] Output: Modeled traffic flow graph

[0948] Step 4:

[0949] Traffic congestion prediction (server)

[0950] Input: Traffic flow graph modeled in step 3

[0951] Processing: The server uses a machine learning model to predict congestion based on the traffic flow graph. The model, which has learned from past data, calculates the probability of congestion occurring for each time period.

[0952] Specific operation: Applying machine learning models and calculating prediction results

[0953] Output: Predicted data of traffic congestion probability

[0954] Step 5:

[0955] Optimal route calculation (server)

[0956] Input: Prediction data from Step 4, route request from user device (current location and destination)

[0957] Processing: The server uses the Dijkstra algorithm and the A algorithm to generate multiple route candidates based on the modeled traffic flow graph and congestion prediction data, performs traffic flow simulations for each route, and selects the optimal route.

[0958] Specific operations: generating multiple route candidates, simulating each route, and selecting the optimal route

[0959] Output: Data on optimal route and expected arrival time

[0960] Step 6:

[0961] Providing optimal route information (server and terminal)

[0962] Input: Optimal route data generated in step 5, request information from user terminal

[0963] Processing: The server generates optimal route information and sends it back to the user's device. The device plots the received optimal route information on a map and provides visual navigation to the user.

[0964] Specific operations: Data transmission by server, route plotting by device, display on user interface

[0965] Output: Optimal route and estimated arrival time displayed on the user's terminal

[0966] Step 7:

[0967] Real-time updates (server and device)

[0968] Input: Real-time information from each traffic data API, periodic location data updates from user devices

[0969] Processing: The server continuously updates new traffic information in real time, and the device periodically obtains new optimal route information from the server. This allows the user to always be guided to the optimal route based on the latest traffic conditions.

[0970] Specific operations: Automatically updating the server database, sending periodic requests from the terminal, and processing the server's responses

[0971] Output: Real-time updated navigation information

[0972] (Application example 1)

[0973] 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."

[0974] As autonomous vehicles become more widespread, it is becoming increasingly necessary to provide optimal routes in real time to deal with traffic congestion and accidents. However, conventional navigation systems lack real-time data analysis and integration with vehicle control systems, making it difficult to provide efficient routes. To address this issue, there is a need for an advanced navigation system that can collect and analyze traffic information in real time and further integrate it into the autonomous vehicle control system.

[0975] 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.

[0976] In this invention, the server includes a means for collecting traffic information, a means for analyzing traffic flow based on the collected traffic information, predicting congestion, and calculating an optimal route, a means for providing the calculated optimal route to a user terminal, and a means for integrating the optimal route information into a vehicle control system. This enables real-time collection and analysis of traffic information and provision of optimal routes to autonomous vehicles. Furthermore, optimal route calculation using a network graph and operation optimization of autonomous vehicles enable efficient and safe operation.

[0977] "Traffic information" refers to information related to road traffic conditions, such as road congestion, traffic accident information, and road construction information.

[0978] "Traffic flow" refers to the flow of vehicles on a road during a specific time period, and is a concept that includes the associated speed and congestion.

[0979] A "traffic jam" is a condition in which the flow of vehicles on a road comes to a near halt or becomes very slow.

[0980] An "optimal route" is a route from a specific origin to a destination that is deemed to be the most efficient route based on certain conditions.

[0981] "User terminal" refers to a device that receives information from the navigation system, such as a smartphone or an in-vehicle display.

[0982] A "vehicle control system" is a system that controls the operation, speed, and direction of a vehicle, and includes autonomous driving technology.

[0983] A "network graph" is a mathematical structure that represents a road network as vertices and edges, and is used to analyze traffic flow and calculate optimal routes.

[0984] In graph theory, a "weighted graph" is a graph in which each edge is weighted, and is used to represent road congestion and travel time.

[0985] "Real-time" refers to responding immediately to ongoing events, and is an important concept in traffic information and autonomous driving control.

[0986] This invention relates to a navigation system that collects and analyzes traffic information in real time to provide optimal routes. This system is composed of a server, a user terminal, and a communication network connecting them.

[0987] First, the server periodically collects traffic information from a traffic information service (API). This data includes information on road congestion, traffic accidents, road construction, and more. The server makes API requests for this information every 10 minutes, obtains congestion levels and speed information for each road section, and stores this information in a database. The collected traffic information is used to model traffic flow, predict traffic congestion, and calculate optimal routes.

[0988] The server analyzes traffic flow based on traffic information and uses an algorithm (e.g., Dijkstra algorithm or A algorithm) to optimize the navigation route using graph theory. This generates multiple route candidates and simulates traffic flow for each route. The server then determines the optimal route based on the simulation results.

[0989] When a user inputs their destination into their smartphone or the device of an autonomous vehicle, the device sends their current location and destination to a server. The server calculates the optimal route in real time based on the latest traffic information and returns the results to the user's device. The device then visually displays the optimal route and estimated arrival time to the user. Furthermore, this optimal route information is integrated into the vehicle control system, helping the autonomous vehicle optimize its operation in real time.

[0990] For example, if a user requests a route from "home to office," the device sends the current location and destination to the server. The server calculates the optimal route based on the latest traffic information and returns the result to the device. The device then displays the optimal route and estimated arrival time to the user. For example, enter the following as a prompt:

[0991] current_location = "Tokyo"

[0992] destination = "Yokohama"

[0993] route = nav_sys.provide_navigation(current_location, destination)

[0994] print("Optimal Route from Tokyo to Yokohama:", route)

[0995] This system uses programs written in the Python language. On the server side, it uses the requests module for making API requests and the networkx module for traffic flow modeling and route calculation. For the database, it uses an SQL-based database (e.g., PostgreSQL) to efficiently store traffic information.

[0996] As described above, this invention maximizes traffic efficiency and realizes efficient and safe operation by collecting and analyzing traffic information in real time and providing optimal routes to autonomous vehicles.

[0997] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0998] Step 1:

[0999] The server collects traffic information from a traffic information service. Specifically, the server sends API requests every 10 minutes and receives information on road congestion, traffic accidents, road construction, etc. The input is API response data, which is structured in JSON format, etc. The server stores this in a database.

[1000] Step 2:

[1001] The server analyzes traffic flow based on the collected traffic information. It retrieves information stored in a database and analyzes congestion levels and speed information for each road section. The input here is past traffic data retrieved from the database, and the output is an analyzed traffic flow model. Past data trends are used for the analysis.

[1002] Step 3:

[1003] The server uses a traffic flow model to predict congestion and generate multiple route candidates. This uses the Dijkstra algorithm and the A algorithm. The input is the traffic flow model, and the output is predicted congestion information and multiple route candidates to avoid it. The route candidate simulation uses the networkx module in Python.

[1004] Step 4:

[1005] The user inputs their destination into their smartphone or in-car device. The user device acquires the user's current location and destination, and sends a request to the server. The input is the destination information from the user, and the output is the request data sent to the server. The device uses GPS and location information services.

[1006] Step 5:

[1007] The server calculates the optimal route in real time based on the current location and destination received from the user's device. This calculation uses the latest traffic information and existing congestion prediction data. The input is the user's current location and destination, as well as a traffic flow model, and the output is optimal route information. The calculation is performed using a network graph.

[1008] Step 6:

[1009] The server returns the calculated optimal route information to the user's device. The user's device receives this information and displays it visually. The output is the optimal route and expected arrival time, displayed in a format that is easy for the user to understand. This is often displayed in a browser or a dedicated app.

[1010] Step 7:

[1011] The user device sends the optimal route information to the vehicle control system, which then optimizes the operation of the autonomous vehicle based on the received information. The input is the optimal route information from the device, and the output is vehicle operation control commands. The system adjusts operation in real time.

[1012] 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.

[1013] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[1014] 1. Data collection (server)

[1015] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[1016] 2. Traffic flow analysis and congestion prediction (server)

[1017] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[1018] 3. Calculating the optimal route (server)

[1019] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[1020] 4. Emotion engine (server and terminal)

[1021] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows it to evaluate the user's stress level and fatigue. For example, if the user is feeling stressed while driving, the emotion engine will detect this and send feedback to the navigation system.

[1022] 5. Navigation interface adjustment (terminal)

[1023] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[1024] 6. Providing and updating optimal routes (server)

[1025] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[1026] 7. System Evolution

[1027] This navigation system is also designed with future integration with autonomous driving technology in mind. Even in a future where autonomous vehicles are on the rise, it is expected to maximize traffic efficiency through real-time traffic information analysis and optimal route provision. Furthermore, by utilizing an emotion engine, more user-friendly navigation can be achieved, reducing driver stress.

[1028] This system will enable professional drivers, including those in the logistics industry, as well as ordinary consumers, to enjoy an efficient and stress-free driving experience, while also contributing to reducing environmental impact by minimizing fuel consumption.

[1029] The processing flow will be explained below.

[1030] Step 1: Collecting traffic information (server)

[1031] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[1032] Step 2: Traffic flow analysis (server)

[1033] The server analyzes the traffic information it receives and models the traffic flow for each road section, based on factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. This allows it to evaluate the passability of each road section.

[1034] Step 3: Calculate the optimal route (server)

[1035] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[1036] Step 4: Collect and analyze emotion data (device)

[1037] The device uses a camera and microphone to collect emotion data from the user's facial expressions and tone of voice, and the device then sends this data to an emotion engine to evaluate the user's stress level and fatigue.

[1038] Step 5: Feedback from the emotion engine (device)

[1039] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or alternative routes to avoid crowds.

[1040] Step 6: Providing the best route (server)

[1041] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device then visually displays the received optimal route information to the user, including route display on a map and voice guidance.

[1042] Step 7: View directions (device)

[1043] The device visually displays the optimal route provided by the server to the user, allowing the user to reach their destination safely and efficiently using visual and audio information. The display includes route details and estimated arrival time.

[1044] Step 8: Real-time updates (server)

[1045] The server updates route information in real time according to changes in traffic conditions. If a new traffic jam occurs while the user is traveling, the server recalculates the optimal route and provides the latest information to the device. This allows the user to continue driving based on the latest information.

[1046] Step 9: Periodic analysis by the emotion engine (device)

[1047] While the user is driving, the emotion engine periodically analyzes the user's emotional data, allowing it to respond immediately to changes in the user's emotional state. For example, if the user's fatigue level increases during a long drive, the engine can suggest taking a break.

[1048] In this way, by combining traffic information and emotional data, we are able to create a system that provides a safer and less stressful driving experience.

[1049] Example 2

[1050] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1051] Conventional navigation systems can collect traffic information in real time and provide optimal routes, but they cannot take into account the user's emotional state. As a result, users are required to use the same navigation interface even when they are under high stress or fatigue, which results in insufficient stress relief and safety improvement while driving.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1053] In this invention, the server includes means for collecting traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for adjusting the calculated optimal route in accordance with the emotional state of the user, means for providing the adjusted optimal route to the user terminal, and means for providing an adjusted navigation interface. This makes it possible to provide appropriate navigation information in accordance with the emotional state of the user, reduce stress while driving, and improve safety.

[1054] "Traffic information" is data relating to road usage, such as road congestion, accident information, and construction information.

[1055] "Traffic flow" refers to data that indicates dynamic conditions such as the flow, speed, and density of vehicles on roads.

[1056] A "user terminal" is a device held by a user who uses a navigation system, such as a smartphone or a car navigation system.

[1057] An "optimal route" refers to a route from a starting point to a destination that takes the least amount of time or meets other specified criteria.

[1058] "Emotional state" refers to the psychological and emotional state that a user feels, such as stress level or fatigue level.

[1059] The "navigation interface" refers to a user interface such as a map or voice guidance displayed to the user through a navigation system.

[1060] "Analysis means" refers to technical means for processing data based on collected traffic information and modeling traffic flow.

[1061] "Simulation means" refers to a technical means for virtually reproducing actual traffic flow for multiple route candidates and evaluating the effectiveness of each route.

[1062] "Real-time" means that the process from data acquisition to analysis and provision is carried out without delay.

[1063] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[1064] The server periodically collects traffic condition data from an external traffic information service. This data includes road congestion, accident information, construction information, and so on. The server sends an API request to obtain the data and stores it in a database. Specifically, the server obtains the latest traffic data using the endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") for calling the traffic information API.

[1065] The server then analyzes the traffic information using the Python pandas library to model traffic flow, which allows the server to evaluate the passability of each road section and predict the likelihood of congestion.

[1066] The server calculates the optimal route using the results of traffic flow analysis. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to calculate the route. Python's NetworkX library is used for this. This allows the optimal route from the user's starting point to their destination to be calculated and travel time to be evaluated.

[1067] The emotion engine is used to recognize the user's emotions. The user's device is equipped with a camera and microphone, which are used to collect the user's facial expressions and tone of voice. The collected data is sent to a server, which then applies an emotion analysis algorithm (for example, a model using TensorFlow) to evaluate the user's emotional state. This allows the user's stress level and fatigue level to be determined.

[1068] The user device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the displayed information and change the voice guidance to a slower tone. The device will also visually display the received optimal route information and highlight it on a map.

[1069] The system responds to changes in traffic conditions in real time, and the server periodically recollects traffic information and updates the optimal route based on the analysis results, allowing users to continue driving based on the latest information.

[1070] Examples of prompts that can be used to generate systems using generative AI models include:

[1071] "Generate a program for a real-time traffic information analysis system that calculates and presents optimal routes to users. The system will have an emotion engine that will adjust the navigation interface based on the user's emotional state."

[1072] Such a system is expected to provide an efficient and stress-free driving experience and also contribute to reducing environmental impact by minimizing fuel consumption.

[1073] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1074] Step 1: Data collection

[1075] The server sends an API request to an external traffic information service. The input is the API endpoint URL (e.g., "https: / / api.trafficinfo.com / v1 / latest") and an authentication token. The output is traffic information (JSON format data). Specifically, the server sends an HTTP GET request and stores the traffic information received as a response in a local database.

[1076] Step 2: Traffic flow analysis

[1077] The server analyzes the stored traffic information. The input is the traffic information obtained in the previous step (JSON data stored in the database). The output is the analysis results (passability and congestion prediction data for each road section). Specifically, the server processes the traffic data using Python's pandas library and calculates the number of vehicles and speeds on each road section.

[1078] Step 3: Calculate the optimal route

[1079] The server receives the user's starting point and destination. The input is the starting point and destination information sent from the user's device. The output is the optimal route (a list of GPS coordinates). Specifically, the server uses Python's NetworkX library to run the Dijkstra algorithm to calculate the optimal route.

[1080] Step 4: Collecting emotion data

[1081] The device uses a camera and microphone to collect the user's facial expressions and tone of voice. The inputs include the user's facial images and voice data. The output is the collected emotion data. Specifically, the device captures the user's facial expressions with the front camera and records the tone of voice with the microphone.

[1082] Step 5: Sentiment Analysis

[1083] The device sends the collected facial and voice data to a server. The input is the user's facial expression data and voice data. The output is an evaluation of the user's emotional state. Specifically, the server uses TensorFlow to run an emotion analysis model and evaluate whether the user is stressed or tired.

[1084] Step 6: Adjusting the navigation interface

[1085] The device receives feedback from the emotion engine. The input is the evaluation of the emotional state sent by the server. The output is an adjusted navigation interface. Specifically, the device changes the tone of the voice guidance and simplifies the information display accordingly.

[1086] Step 7: Provide optimal route

[1087] The server sends the calculated optimal route to the user's device. The input is the optimal route (a list of GPS coordinates). The output is the route information displayed on the user's device. Specifically, the server sends the optimal route data to the user's device in real time and displays it on a map.

[1088] Step 8: Real-time updates

[1089] The server updates route information in real time according to changes in traffic conditions. The input is newly collected traffic information. The output is updated optimal route information sent to the user terminal. Specifically, the server periodically reanalyzes traffic data and updates route information as necessary.

[1090] (Application example 2)

[1091] 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."

[1092] While conventional navigation systems analyze traffic information in real time and provide optimal routes, they are unable to optimize the route taking into account the user's emotions and stress levels. As a result, traffic jams and unexpected traffic conditions can cause significant stress for users, making it difficult to provide a relaxing travel experience. To address these issues, the present invention aims to analyze the user's emotions and provide navigation information that corresponds to their emotional state.

[1093] 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 traffic information, means for analyzing traffic flow based on the collected traffic information, means for predicting congestion and calculating an optimal route, means for providing the calculated optimal route to a user terminal, means for analyzing the user's emotions, means for adjusting navigation information based on the analyzed emotions, and means for displaying and providing navigation information on a smart display device by voice. This provides an optimal route that takes the user's emotions into consideration, enabling efficient travel while reducing stress.

[1094] "Traffic information" is data related to traffic conditions, such as road congestion, accident information, and construction information.

[1095] "Traffic flow" refers to information such as the flow, density, and speed of vehicles passing through a specific section.

[1096] The "means for analyzing emotions" is a means for analyzing the user's facial expressions, tone of voice, etc., and evaluating the user's emotional state.

[1097] The "optimal route" refers to the route that can take you from the starting point to the destination in the shortest time, taking into account traffic conditions.

[1098] A "smart display device" is a device that provides visual and audio information to a user, including, for example, smart glasses and head-mounted displays.

[1099] "Navigation information" includes information such as route guidance, traffic information, and current location, and is information that shows the way to the user's destination.

[1100] A "user terminal" is a part of the navigation system, and is a device that provides optimal routes and traffic information provided by the server to the user.

[1101] "Route candidates" indicate multiple possible routes to a destination, and are evaluated taking into consideration factors such as travel time and traffic congestion forecasts for each route.

[1102] "Traffic information service" is an external service that provides data related to traffic conditions. Data is obtained from this service and used for analysis on the server.

[1103] The "server" is the central part of the entire system, and is an information processing device that collects and analyzes traffic information, calculates optimal routes, analyzes user emotions, and so on.

[1104] This invention combines an emotion engine with a navigation system that collects and analyzes traffic information in real time to provide optimal routes. The system is composed of a server, a user terminal, an emotion engine, and a communication network connecting them.

[1105] 1. Data collection (server)

[1106] The server periodically collects traffic condition data from external traffic information services. This data includes road congestion, accident information, construction information, etc. The server sends an API request to obtain the data and then stores it in a database. This ensures that the latest traffic information is always available.

[1107] 2. Traffic flow analysis and congestion prediction (server)

[1108] The server analyzes the traffic information it receives and models the traffic flow for each road segment, including factors such as the number of vehicles on the road, vehicle speed, and the likelihood of congestion. Based on the analysis results, the server evaluates the passability of each road segment.

[1109] 3. Calculating the optimal route (server)

[1110] The server uses the traffic flow analysis results to calculate the optimal route from the user's starting point to their destination. Graph search algorithms such as the Dijkstra algorithm and the A algorithm are used to evaluate the travel time for each route candidate. Multiple routes are simulated, and the route that avoids congestion and arrives in the shortest time is selected.

[1111] 4. Emotion engine (server and terminal)

[1112] The emotion engine recognizes the user's emotions. It uses the camera and microphone installed on the user's device to analyze the user's emotions from facial expressions and tone of voice. This allows the system to assess the user's stress level and fatigue. For example, if a user says, "Today's traffic jam is tiring" while driving, the system will analyze the user's stress level from the tone of voice. The system may reevaluate the route and recommend a scenic route that is relaxing.

[1113] 5. Navigation interface adjustment (terminal)

[1114] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device will simplify the information displayed and change the voice guidance to a slower tone. If the user is stressed, the device will suggest relaxation activities or recommend alternative routes to avoid crowds.

[1115] 6. Providing and updating optimal routes (server)

[1116] When a user inputs a destination into the device, the device sends a request to the server. The server calculates the optimal route in real time based on the received request and returns the result to the user's device. The device visually displays the received optimal route information to the user. It also updates the route information in real time according to changes in traffic conditions, allowing the user to continue driving based on the latest information.

[1117] 7. Use of smart display devices

[1118] Smart display devices, such as smart glasses and head-mounted displays (HMDs), can be used to visually display real-time traffic information and recommended routes, allowing users to obtain navigation information more intuitively.

[1119] Prompt Sentence Examples

[1120] If a user says, "Today's traffic jam is tiring," the system will calculate the optimal route in real time and suggest scenic alternatives to reduce stress, providing visual instructions and audio guidance through smart glasses or an HMD.

[1121] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1122] Step 1: Data Collection (Server)

[1123] The server periodically sends API requests from external traffic information providers to obtain traffic condition data (road congestion, accident information, construction information). The obtained data is stored in a database. The input is the API request, and the output is the traffic information stored in the database.

[1124] Step 2: Traffic flow analysis and congestion prediction (server)

[1125] The server analyzes traffic information stored in the database and models the traffic flow (number of vehicles, vehicle speed, and likelihood of congestion) for each road section. This evaluates the passability of each section. The input is the stored traffic information, and the output is the passability evaluation result for each section.

[1126] Step 3: Calculate the optimal route (server)

[1127] The server calculates the optimal route from the departure point to the destination based on the results of traffic flow analysis. Using algorithms such as the Dijkstra algorithm and A algorithm, it generates multiple route candidates, simulates each one, and selects the optimal one. The input is the traffic flow analysis results, and the output is optimal route information.

[1128] Step 4: User sentiment analysis (device)

[1129] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. The step-analyzed emotional state (stress level and fatigue level) is evaluated. The input is the user's facial expressions and tone of voice, and the output is the evaluation result of the emotional state.

[1130] Step 5: Adjusting the navigation interface (device)

[1131] The device receives feedback from the emotion engine and adjusts the navigation interface to match the user's emotional state. For example, if the user is tired, the device simplifies the displayed information and changes the voice guidance to a slower tone. The input is the emotion analysis result, and the output is the adjusted navigation interface.

[1132] Step 6: Providing and updating optimal routes (server, terminal)

[1133] When a user inputs a destination into the device, the device sends that information to the server. The server recalculates the optimal route in real time and returns the results to the device. The device then provides the user with route information through visual and audio guidance. The route information is continually updated as traffic conditions change. The input is destination information and current location information, and the output is real-time optimal route information.

[1134] Step 7: Using a Smart Display Device (Device)

[1135] Using smart display devices (smart glasses or head-mounted displays), navigation information is displayed in real time, including optimal routes, traffic information, and interface adjustments according to emotional states. The input is optimal route information and emotion analysis results from the server, and the output is navigation information displayed on the display.

[1136] 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.

[1137] 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.

[1138] 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.

[1139] 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.

[1140] 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.

[1141] 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.

[1142] 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).

[1143] 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.

[1144] 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."

[1145] 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.

[1146] 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).

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] 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.

[1157] The following is further disclosed regarding the above embodiment.

[1158] (Claim 1)

[1159] a means for collecting traffic information;

[1160] A means of analyzing traffic flow based on collected traffic information, predicting congestion, and calculating optimal routes;

[1161] means for providing the calculated optimum route to a user terminal;

[1162] A system including:

[1163] (Claim 2)

[1164] 10. The system of claim 1, further comprising means for generating a plurality of candidate routes and simulating traffic flow for each of the candidate routes during the calculation of the optimal route.

[1165] (Claim 3)

[1166] 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server returning an optimal route in real time.

[1167] "Example 1"

[1168] (Claim 1)

[1169] a means for collecting traffic information;

[1170] A method for analyzing traffic flow based on collected traffic information, modeling traffic flow using graph theory, and predicting congestion.

[1171] a means for calculating an optimal route using an algorithm that optimizes a navigation route by utilizing the modeled traffic flow;

[1172] means for providing the calculated optimum route to a user terminal;

[1173] A system including:

[1174] (Claim 2)

[1175] 2. The system according to claim 1, further comprising means for generating a plurality of route candidates, simulating traffic flow for each route, and determining an optimal route.

[1176] (Claim 3)

[1177] 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server to calculate and return an optimal route in real time.

[1178] "Application Example 1"

[1179] (Claim 1)

[1180] a means for collecting traffic information;

[1181] A means of analyzing traffic flow based on collected traffic information, predicting congestion, and calculating optimal routes;

[1182] means for providing the calculated optimum route to a user terminal;

[1183] means for integrating the optimal route information into a vehicle control system;

[1184] A system including:

[1185] (Claim 2)

[1186] 10. The system of claim 1, further comprising means for generating a plurality of candidate routes and simulating traffic flow for each of the candidate routes during the calculation of the optimal route.

[1187] (Claim 3)

[1188] 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server returning an optimal route in real time.

[1189] (Claim 4)

[1190] 2. The system according to claim 1, further comprising means for updating the network graph based on the collected traffic information and calculating an optimal route using a weighted graph.

[1191] (Claim 5)

[1192] 10. The system of claim 1, further comprising means for providing optimal route information to the autonomous vehicle to optimize vehicle operation in real time.

[1193] "Example 2: Combining Emotion Engines"

[1194] (Claim 1)

[1195] a means for collecting traffic information;

[1196] A means of analyzing traffic flow based on collected traffic information, predicting congestion, and calculating optimal routes;

[1197] means for adjusting the calculated optimal route in response to the emotional state of the user;

[1198] means for providing the adjusted optimum route to a user terminal;

[1199] means for providing a coordinated navigation interface;

[1200] A system including:

[1201] (Claim 2)

[1202] 10. The system of claim 1, further comprising means for generating a plurality of candidate routes and simulating traffic flow for each of the candidate routes during the calculation of the optimal route.

[1203] (Claim 3)

[1204] 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server returning an optimal route in real time.

[1205] "Application example 2 when combining emotion engines"

[1206] (Claim 1)

[1207] a means for collecting traffic information;

[1208] A means of analyzing traffic flow based on collected traffic information, predicting congestion, and calculating optimal routes;

[1209] means for providing the calculated optimum route to a user terminal;

[1210] means for analyzing user emotions;

[1211] means for adjusting navigation information based on the analyzed emotions;

[1212] means for displaying and audibly providing navigation information on a smart display device;

[1213] A system including:

[1214] (Claim 2)

[1215] 10. The system of claim 1, further comprising means for generating a plurality of candidate routes and simulating traffic flow for each of the candidate routes during the calculation of the optimal route.

[1216] (Claim 3)

[1217] 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server returning an optimal route in real time. [Explanation of symbols]

[1218] 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 traffic information; A means of analyzing traffic flow based on collected traffic information, predicting congestion, and calculating optimal routes; means for providing the calculated optimum route to a user terminal; A system including:

2. 2. The system of claim 1, further comprising means for generating a plurality of candidate routes and simulating traffic flow for each of the candidate routes during the calculation of the optimum route.

3. 2. The system according to claim 1, further comprising means for the user terminal to transmit a current location and a destination to the server, and the server returning an optimal route in real time.

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