System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting real-time data, a means for predicting a traffic state on the basis of the collected real-time data, a means for calculating an optimum route on the basis of a prediction result, a means for transmitting the calculated optimum route to a terminal, and a means for performing route guidance to the terminal by display or voice.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] Current traffic systems have difficulty predicting and avoiding congestion in real time, resulting in the problem of many vehicles getting caught in traffic jams. This results in increased travel time, worsening fuel efficiency, and even increased CO2 emissions. Furthermore, existing navigation systems rely on static map data and are unable to fully utilize real-time traffic information. This makes it difficult to achieve efficient traffic flow. [Means for solving the problem]

[0005] The present invention is a system including a means for collecting real-time data, a means for predicting traffic conditions based on the collected real-time data, a means for calculating an optimal route based on the prediction results, a means for transmitting the calculated optimal route to a terminal, and a means for displaying or providing audio route guidance on the terminal. This makes it possible to predict congestion in real time and provide users with optimal avoidance routes. In particular, collecting real-time data from the terminal with ultra-low latency enables highly accurate congestion predictions, and synchronizing the calculation of the optimal route with a map database enables more accurate route guidance.

[0006] "Real-time data" refers to instantaneous location information, speed information, traffic sign recognition data, and other traffic-related data collected from vehicles and transportation infrastructure.

[0007] A "collection means" is a combination of hardware and software for receiving real-time data and recording it in a database.

[0008] "Prediction means" refers to algorithms and computing resources that analyze traffic conditions based on collected real-time data and predict future congestion.

[0009] The "means of calculation" refers to the process of calculating the optimal route based on the prediction results and the combination of software and hardware that executes this process.

[0010] The "transmitting means" refers to the communication infrastructure and protocols for transmitting the calculated optimum route data to the terminal.

[0011] A "terminal" is a mobile device such as a car system with a navigation function or a smartphone.

[0012] "Means for providing route guidance" refers to a user interface and related software for displaying or providing audio guidance on the terminal about the optimal route.

[0013] A "map database" is a database system that stores map information and related data and makes it accessible and retrievalable. [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 showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[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] The system of the present invention collects real-time data and predicts traffic congestion to provide users with optimal routes. The basic configuration for implementing this system will be described in detail below.

[0036] 1. Real-time data collection

[0037] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0038] 2. Data Preprocessing

[0039] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0040] 3. Implementing traffic congestion prediction

[0041] The server then runs a traffic congestion prediction algorithm based on the pre-processed data, which analyzes current speed and location information to predict where and when future congestion will occur.

[0042] 4. Calculating the optimal route

[0043] The server calculates the optimal route based on the congestion prediction results, which involves searching for alternative routes in a map database and calculating the distance and estimated time for each route.

[0044] 5. Sending optimal route data

[0045] The server then transmits the calculated optimal route information to each terminal, which may be a car navigation system or a smartphone.

[0046] 6. Route guidance

[0047] The device displays or provides audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0048] Specific examples of program processing

[0049] As a concrete example, consider a scenario during the morning commute.

[0050] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0051] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0052] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0053] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0054] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0055] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0056] 7. The user follows the navigation instructions to avoid major road A and take detour route B to avoid traffic jams.

[0057] This system can effectively avoid congestion in real time, improving fuel efficiency and reducing CO2 emissions.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and automatically records this information in a database.

[0061] Step 2:

[0062] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0063] Step 3:

[0064] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0065] Step 4:

[0066] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0067] Step 5:

[0068] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0069] Step 6:

[0070] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0071] Step 7:

[0072] The user follows the guidance from the device and proceeds along the suggested optimal route. Specifically, the driver operates the steering wheel according to the navigation instructions and drives along the instructed route to avoid traffic jams.

[0073] Through this series of steps, the system predicts and avoids traffic congestion in real time and provides efficient route guidance, thereby improving fuel efficiency and reducing CO2 emissions.

[0074] Example 1

[0075] 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."

[0076] Conventional traffic guidance systems have the problem of being unable to accurately predict traffic congestion in real time, making it difficult to provide appropriate route guidance. Furthermore, there is a risk of incorrect predictions if outliers or duplicate data are included. Furthermore, delays in collecting data from terminals can make it difficult to provide timely guidance. To solve these issues, a traffic guidance system with excellent accuracy and immediacy is needed.

[0077] 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.

[0078] In this invention, the server includes a means for collecting real-time data, a means for removing outliers, standardizing the data format, and deleting duplicate data, and a means for predicting traffic conditions based on the collected real-time data, thereby enabling highly accurate traffic congestion predictions and rapid route guidance.

[0079] "Real-time data" refers to digital information such as GPS coordinates, speed information, and traffic sign recognition data that instantly reflects traffic conditions.

[0080] "Abnormal values" are numerical values ​​such as speed and location information that would not occur under normal traffic conditions, and are data that hinder analysis.

[0081] "Data unification" is the process of converting disparately collected data into a consistent format.

[0082] "Duplicate data" refers to data in which the same information is collected multiple times during the same time period, which can reduce the accuracy of analysis.

[0083] "Traffic condition prediction" is the result of analysis by an algorithm that predicts future traffic conditions based on current data.

[0084] An "optimal route" is a route that avoids predicted traffic congestion and estimates the shortest time to reach a destination.

[0085] A "terminal" is a device that provides route guidance to a user, such as a navigation system or a smartphone.

[0086] A "map database" is a database that contains geographical information and road network information and is used for route calculations.

[0087] The system of the present invention collects traffic data in real time, predicts congestion, and provides users with optimal routes. This system is composed of a server, terminals, and users.

[0088] First, the server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, etc. The server records this data in a database to understand detailed traffic conditions.

[0089] The server then preprocesses the collected data, which includes removing outliers, standardizing data formats, and deleting duplicate data, such as data with missing location information or extreme speed information.

[0090] Based on the pre-processed data, the server runs a traffic congestion prediction algorithm. This algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, if vehicle speed in a particular section suddenly drops, it determines that there is a high possibility of congestion occurring in that location.

[0091] Based on the results of the traffic congestion prediction, the server calculates the optimal route. In this process, it searches for multiple alternative routes from a map database and calculates the distance and estimated time for each route. This allows the user to reach their destination in the shortest time.

[0092] The calculated optimal route data is sent to a device with navigation functions (such as an automobile system or a smartphone). The device then uses this data to provide route guidance to the user. Specifically, the device displays the route on a screen and provides voice guidance such as "Turn right at the next intersection."

[0093] As an example of this system, consider a scenario during a morning commute. At 8:00 AM, the server receives GPS information, speed data, and surrounding traffic sign recognition data from the car. The server then preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data. After preprocessing is complete, the server runs a traffic congestion prediction algorithm to predict when congestion will occur on a specific road. To avoid congestion, the server searches for multiple alternative routes and calculates the distance and estimated arrival time for each. The calculated optimal route data is then sent to the car's navigation system, and the user can follow the new route guidance to avoid congestion.

[0094] An example of a prompt sentence is, "There is a possibility of traffic congestion on main road A. Please calculate the optimal route taking into account the state of fatigue on alternative route B."

[0095] The above is a specific embodiment for carrying out the present invention. This system allows users to effectively avoid traffic jams in real time, and is expected to improve fuel efficiency and reduce CO2 emissions.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] Real-time data collection

[0099] The server collects real-time data from vehicles and traffic infrastructure. As input, it receives GPS coordinates, speed information, and traffic sign recognition data. These data are obtained from GPS devices, speed sensors, and traffic sign recognition cameras installed in each vehicle. The received data is recorded in a database. Specifically, the server receives vehicle location information and image data in real time, updated every second.

[0100] Output: The collected real-time data is stored in a database.

[0101] Step 2:

[0102] Data Preprocessing

[0103] The server preprocesses the collected data. It uses real-time collected data as input. Data preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. Specifically, the server detects and deletes abnormal speed data (e.g., 300 km / h), converts it into a unified format, and deletes duplicate data.

[0104] Output: Preprocessed data with outliers removed, formatted uniformly, and duplicates removed.

[0105] Step 3:

[0106] Implementing traffic congestion predictions

[0107] The server uses the preprocessed data to run a traffic congestion prediction algorithm. The preprocessed data is used as input. The algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, the server analyzes the speed data from the past hour, detects a consistent decrease in vehicle speed at a specific location, and predicts congestion at that location.

[0108] Output: Prediction data on the location and time of future congestion is obtained.

[0109] Step 4:

[0110] Optimal Route Calculation

[0111] The server calculates the optimal route based on the congestion prediction results. The predicted congestion data and map database are used as input. Calculating the optimal route involves searching for multiple alternative routes and calculating the distance and predicted time for each route. Specifically, the server searches for multiple routes that avoid major road A, where congestion is predicted, and calculates the arrival time for each.

[0112] Output: The optimal route data to reach the destination in the shortest time is obtained.

[0113] Step 5:

[0114] Sending optimal route data

[0115] The server sends the calculated optimal route data to the terminal. The optimal route data is used as input. The transmission is done in real time, so that the user can use it immediately. Specifically, the server sends the new route data to the terminal and notifies the user that "a new route has been found."

[0116] Output: The optimal route data is sent to the terminal.

[0117] Step 6:

[0118] Execute route guidance

[0119] The terminal provides route guidance to the user based on the received optimal route data. The optimal route data is used as input. The guidance includes display and voice guidance. Specifically, the terminal displays the new route on the display and provides voice guidance such as "Turn right at the next intersection."

[0120] Output: Visual and audio route guidance is provided to the user.

[0121] (Application example 1)

[0122] 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."

[0123] In food delivery, traffic congestion is a major obstacle to efficient delivery, which can delay delivery times and reduce customer satisfaction. Current systems have difficulty predicting traffic congestion in real time and providing optimal routes based on the results. A method is needed to solve this problem and improve delivery efficiency and customer satisfaction.

[0124] 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.

[0125] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction results, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, and means for optimizing routes for delivery work in real time and providing the route to delivery personnel, thereby making it possible to avoid traffic congestion during delivery work and perform deliveries efficiently.

[0126] "Real-time data" refers to data that can be obtained instantly from moving objects and transportation infrastructure, including information on their current status, location, speed, etc.

[0127] "Traffic conditions" refers to information that indicates the current traffic flow, whether there is congestion, speed limits, etc. on a particular road or in an area.

[0128] "Prediction results" are information obtained as a result of predicting future traffic conditions based on collected real-time data.

[0129] An "optimal route" is a route calculated to be the most efficient route between a departure point and a destination point that avoids traffic congestion.

[0130] A "terminal" is a device that has a navigation function and displays or provides audio guidance on optimal routes, examples of which include in-car navigation systems and smartphones.

[0131] "Delivery operations" refers to the general business process of transporting goods from a specific location to a customer, such as food delivery.

[0132] "Delivery person" refers to an individual or group engaged in food delivery work, etc., and whose role is to deliver goods to customers.

[0133] The embodiment of the present invention is a real-time traffic condition prediction and route guidance system that includes a server, a terminal, and a user who operates the terminal. Specifically, the present invention is implemented as a system including the following means.

[0134] 1. Real-time data collection

[0135] The server collects real-time data from mobile devices and transportation infrastructure devices. Specifically, it uses a smartphone as a terminal to receive current GPS coordinates, speed information, traffic sign recognition data, etc. from delivery vehicles. This is done using a cloud environment such as AWS EC2.

[0136] 2. Data Preprocessing

[0137] The server preprocesses the collected real-time data using Python scripts, which includes removing outliers, standardizing data formats, and removing duplicate data using the Pandas library.

[0138] 3. Implementing traffic congestion prediction

[0139] The server uses the preprocessed data to predict congestion using a TensorFlow model, which makes it possible to predict where and when future congestion will occur.

[0140] 4. Calculating the optimal route

[0141] The server calculates the optimal route using the Google Maps API, which finds alternative routes and calculates distance and estimated arrival time.

[0142] 5. Sending optimal route data

[0143] The server sends the calculated optimal route data in real time via a RESTful API to the delivery person's smartphone, which has an application developed using React Native installed on it.

[0144] 6. Route guidance

[0145] The device (smartphone) displays the received optimal route information and provides voice guidance using Siri on iOS or Google Assistant on Android.

[0146] Specific examples

[0147] For example, consider a scenario in which a delivery driver delivers catering during the morning rush hour. The server receives GPS and speed data sent from the delivery vehicle at 8:00 AM. After removing outliers and standardizing the data format, it uses a TensorFlow model to predict congestion and predicts that congestion will occur on main road A. To do this, the server uses the Google Maps API to calculate alternative route B and sends it to the delivery driver's smartphone. The app displays the new route data and provides a voice prompt saying, "Congestion is predicted on main road A. Please proceed to detour route B."

[0148] Prompt Sentence Examples

[0149] An example of a prompt sentence when using a generative AI model is as follows:

[0150] "Generate Python code that predicts the optimal route using given traffic data."

[0151] In this way, the system of the present invention enables food delivery personnel to efficiently avoid traffic congestion and quickly deliver products to customers, which is expected to improve business efficiency and customer satisfaction.

[0152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0153] Step 1:

[0154] The server collects real-time data from smartphones in delivery vehicles and traffic infrastructure devices. Specifically, it receives GPS coordinates, speed information, traffic sign recognition data, etc. and records it in a database. The input here is real-time data from each device, and the output is raw data recorded on the server.

[0155] Step 2:

[0156] The server preprocesses the collected real-time data using a Python script to remove outliers, standardize data formats, and remove duplicate data to generate preprocessed data. The input here is the raw data recorded in step 1, and the output is the preprocessed data.

[0157] Step 3:

[0158] The server uses the preprocessed data to predict traffic congestion using a TensorFlow model. The preprocessed data is input into the model, which generates predictions about the location and time of future congestion. The input here is the preprocessed data, and the output is the prediction.

[0159] Step 4:

[0160] The server uses the Google Maps API to calculate the optimal route based on the traffic congestion forecast. The forecast results are passed to the API, which searches for alternative routes and calculates the distance and estimated arrival time. The input here is the forecast results and map information, and the output is the optimal route information.

[0161] Step 5:

[0162] The server sends the calculated optimal route data to the delivery person's smartphone in real time via a RESTful API. The input here is the optimal route information, and the output is the route data sent to the delivery person's device.

[0163] Step 6:

[0164] The device displays the received optimal route information to the user and provides voice guidance. Specifically, voice guidance is provided using Siri on iOS or Google Assistant on Android. The input here is the optimal route data sent from the server, and the output is route guidance information for the user.

[0165] In this way, the server collects and preprocesses real-time data, predicts traffic conditions, and calculates the optimal route, and the device provides route guidance to the user based on that information. This enables efficient route selection in delivery work and is expected to improve customer satisfaction.

[0166] 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.

[0167] The system of the present invention not only collects real-time data and predicts traffic congestion, but also combines it with an emotion engine that recognizes the user's emotions to provide route guidance tailored to individual needs. The basic configuration for implementing this system is described in detail below.

[0168] 1. Real-time data collection

[0169] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0170] 2. Data Preprocessing

[0171] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0172] 3. Implementing traffic congestion prediction

[0173] The server runs a traffic congestion prediction algorithm on the pre-processed data, analyzing current speed and location information to predict where and when future congestion will occur.

[0174] 4. Calculating the optimal route

[0175] The server calculates the optimal route based on the congestion forecast, searches for alternative routes in a map database, and calculates the distance and estimated arrival time of each route.

[0176] 5. Sending optimal route data

[0177] The server then sends the calculated optimal route information to each terminal, which may be a car system with a navigation function or a smartphone.

[0178] 6. Route guidance

[0179] The device will then display or provide audio guidance to the user based on the optimal route data it receives, allowing the user to efficiently avoid traffic jams.

[0180] 7. Adding an Emotion Engine

[0181] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, allowing it to determine whether the user is feeling stressed.

[0182] 8. Emotional Data Processing

[0183] The server evaluates the user's stress level based on the received emotional data. Based on the evaluation results, for example, if the stress level is high, it can suggest a more relaxing route with good scenery, helping the user continue driving in a relaxed state.

[0184] Specific examples

[0185] Let us consider a specific scenario during the morning commute.

[0186] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0187] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0188] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0189] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0190] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0191] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0192] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0193] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0194] In this way, the present invention can predict traffic congestion in real time and provide optimal route guidance according to the user's individual emotional state, thereby achieving both efficient driving and user comfort at the same time.

[0195] The processing flow will be explained below.

[0196] Step 1:

[0197] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and receives and automatically records this information transmitted by vehicles in a database.

[0198] Step 2:

[0199] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0200] Step 3:

[0201] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0202] Step 4:

[0203] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0204] Step 5:

[0205] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0206] Step 6:

[0207] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0208] Step 7:

[0209] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, and can determine whether the user is feeling stressed by analyzing the user's facial expressions and tone of voice, for example.

[0210] Step 8:

[0211] The server evaluates the user's stress level based on the received emotional data. Specifically, if the user's stress level is high, the server prioritizes scenic routes that are relaxing.

[0212] Step 9:

[0213] The device receives the new optimal route again and notifies the user. The navigation system displays the new route on the display and asks the voice guidance, "A relaxing route has been found. Would you like to proceed with this route?"

[0214] Step 10:

[0215] The user follows the navigation system's instructions and follows the suggested route. Specifically, the driver follows the navigation system's instructions and operates the steering wheel to drive along the optimal route, reducing stress and providing a comfortable drive.

[0216] Through this series of steps, the system not only predicts and avoids traffic jams in real time, but also provides optimal route guidance according to the user's emotional state, thereby improving both travel efficiency and user comfort.

[0217] Example 2

[0218] 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."

[0219] Conventional traffic congestion prediction systems were able to collect real-time traffic data and provide congestion predictions and route guidance based on that data, but they were unable to provide route guidance that took into account the user's emotional state or stress level. As a result, they were unable to improve the user's mental comfort and lacked a means to reduce stress while driving.

[0220] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting real-time data from automobiles and traffic infrastructure, means for pre-processing the collected real-time data, means for predicting traffic congestion, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for collecting user emotion data through the terminal, means for evaluating the emotion data to determine the user's stress level, and means for suggesting a route according to the user's stress level. This enables optimal route guidance that takes the user's emotional state into consideration.

[0221] "Real-time data" refers to data that is immediately available based on time, such as the latest location information, speed information, and traffic sign recognition data obtained on the spot from vehicles and transportation infrastructure.

[0222] "Preprocessing" refers to processing collected real-time data, such as removing outliers, standardizing data formats, and deleting duplicate data, which can improve the quality of the data.

[0223] "Traffic congestion prediction" refers to predicting future traffic conditions based on preprocessed real-time data, particularly the locations and times when traffic flow will be slow.

[0224] The "optimal route" is the most efficient route calculated based on traffic congestion prediction results. It is a route that avoids predicted congestion and minimizes the distance and time to the destination.

[0225] "Terminal" refers to a device that receives information on optimal routes and provides route guidance to users. Specifically, this refers to an in-vehicle system or smartphone with a navigation function.

[0226] "Emotional Data" means data indicative of a user's emotional state, collected using facial recognition and / or voice analysis technologies, and used to determine a user's stress level or other emotional state.

[0227] "Evaluating emotional data" refers to analyzing collected emotional data to determine the user's stress level or other emotional state.

[0228] "Relaxing routes" are routes suggested based on the user's stress level. They refer to routes that enhance the user's mental comfort, such as scenic roads or routes that avoid crowds.

[0229] This invention is a system that collects real-time data from vehicles and transportation infrastructure to predict traffic congestion. Furthermore, the system can recognize the user's emotional state and provide route guidance according to their stress level.

[0230] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and records it in a database. This is done by collecting data via Apache Kafka and storing it in a database using AWS RDS.

[0231] The collected data is preprocessed using Apache Spark. This preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. The preprocessed data is then used to run a traffic congestion prediction algorithm. Specifically, a machine learning model is applied using Apache Hadoop and TensorFlow to predict the location and time of future congestion.

[0232] The server calculates the optimal route based on the congestion forecast results. This calculation uses Google Maps API and Graphhopper to calculate the distance and predicted arrival time of alternative routes. The calculated optimal route information is sent to each terminal via REST API. The terminals can be automobile systems with navigation functions or smartphones.

[0233] The device will display or voice directions to the user based on the received optimal route data, using the Android or iOS platform to integrate the Google Maps application.

[0234] Additionally, the device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. Specifically, it uses OpenCV and the Google Cloud Speech-to-Text API to determine whether the user is feeling stressed. The collected emotional data is sent to a server, where Google Cloud AI is used to evaluate the user's stress level.

[0235] Based on the evaluation results, if the user's stress level is high, the server will suggest a scenic route that is expected to have a relaxing effect, thereby helping the user continue driving in a relaxed state.

[0236] Specific examples

[0237] Let us consider a specific scenario during the morning commute.

[0238] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0239] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0240] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0241] 4. The server searches for alternative routes B to avoid traffic jams using the Google Maps API and Graphhopper, and calculates the distance and estimated arrival time of each alternative route.

[0242] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0243] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0244] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0245] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0246] Examples of prompts for generative AI models

[0247] For example, you can use the following prompt for a generative AI model:

[0248] Prompt: Please describe in detail the process of a system that combines real-time traffic data and emotion recognition to provide users with optimal route guidance and relaxing routes. Please also name the specific software and hardware.

[0249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0250] Step 1:

[0251] The server collects real-time data from vehicles and traffic infrastructure. Specifically, the inputs include GPS coordinates, speed information, and traffic sign recognition data. Apache Kafka is used to stream the data in real time and record it in an AWS RDS database. This process allows the server to obtain data that reflects the latest traffic conditions.

[0252] Step 2:

[0253] The server preprocesses the collected real-time data. The input for preprocessing is the data collected in step 1, and Apache Spark is used here. Specific preprocessing operations include removing outliers, standardizing data formats, and deleting duplicate data. This process outputs high-quality, analyzable data.

[0254] Step 3:

[0255] The server runs a traffic congestion prediction algorithm based on the preprocessed data. The preprocessed data is input, and a machine learning model is applied using Apache Hadoop and TensorFlow. The congestion prediction algorithm analyzes current speed and location information to predict the location and time of future congestion. The predicted location and time of congestion are output as the result.

[0256] Step 4:

[0257] The server calculates the optimal route based on the congestion forecast results. The input is the congestion forecast results and a map database, and route searches are performed using the Google Maps API and Graphhopper. The distance and predicted arrival time of each alternative route are calculated and the optimal route is selected. The output of this process is the optimal route data.

[0258] Step 5:

[0259] The server sends the calculated optimal route information to each terminal. The input is the optimal route data obtained in step 4, and uses the HTTP protocol via the REST API. The terminals are devices with navigation functions (in-vehicle systems or smartphones). As a result of the transmission, each terminal receives the optimal route data.

[0260] Step 6:

[0261] Based on the received optimal route data, the device displays the route or provides voice guidance to the user. The input is the optimal route data, and the Google Maps application is integrated using the Android or iOS platform. Specifically, the device displays the route on the display and provides voice guidance saying, "Congestion is predicted on main road A. Please proceed to detour route B." The output of this process is route guidance information for the user.

[0262] Step 7:

[0263] The device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. The input is the user's facial expression data and voice data, which are analyzed using OpenCV and the Google Cloud Speech-to-Text API. The device determines whether the user is feeling stressed and sends that information to the server. The output of this process is the user's emotional data.

[0264] Step 8:

[0265] The server evaluates the user's stress level based on the received emotional data. The input is emotional data, which is analyzed using Google Cloud AI. If a high stress level is detected, a scenic route that is expected to have a relaxing effect is calculated, and new route guidance is sent to the device. The output of this process is optimal route guidance data based on the user's emotional state.

[0266] The above explains the specific operations, inputs, and outputs performed at each step. This system can predict traffic congestion and provide optimal route guidance according to the user's emotional state.

[0267] (Application example 2)

[0268] 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."

[0269] In order to provide effective route guidance for autonomous vehicles, it is necessary not only to avoid traffic congestion but also to realize a comfortable driving experience that takes into account the user's emotional state. However, conventional navigation systems only collect real-time traffic data and predict congestion, but do not provide route guidance that takes the user's emotional state into account. This poses a challenge: it is not possible to ensure user comfort while effectively avoiding congestion.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0271] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, means for recognizing the emotional state of a user, and means for adjusting the route based on the recognized emotional state. This makes it possible to grasp traffic conditions in real time and provide an optimal route according to the emotional state of the user.

[0272] "Real-time data" refers to data that reflects the current situation and is collected every moment from vehicles and transportation infrastructure.

[0273] "Traffic conditions" refers to the overall situation, including road congestion, traffic volume, and speed information.

[0274] An "optimal route" is a route that allows for the most efficient and comfortable travel, taking into account the distance to the destination, travel time, and traffic conditions.

[0275] "Terminal" refers to a device operated by a user, such as a car system with navigation function or a smartphone.

[0276] "Emotional state" refers to the user's psychological emotional state, such as stress, relaxation, or impatience.

[0277] "Route guidance" refers to providing the user with guidance to their destination, and is done by display or voice.

[0278] An "emotion engine" refers to a system that uses facial recognition cameras and voice analysis technology to recognize a user's emotional state.

[0279] "Adjustment means" refers to methods and techniques for correcting or optimizing the optimal route based on collected data and forecast results.

[0280] "Server" refers to the central computer system that performs real-time data collection, pre-processing, analysis, route calculation and optimization.

[0281] The system of the present invention combines the functions of collecting real-time data and predicting traffic congestion with the functions of recognizing the user's emotional state and suggesting the optimal route. The program and its processing for realizing this system are described below.

[0282] 1. Real-time data collection

[0283] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records this data in a database.

[0284] 2. Data Preprocessing

[0285] The server removes outliers, standardizes data formats, and deletes duplicate data from the collected real-time data. This preprocessing ensures data quality and enables subsequent processing with high accuracy.

[0286] 3. Implementing traffic congestion prediction

[0287] The server runs a traffic congestion prediction algorithm based on the pre-processed data, analyzing actual vehicle speeds and location information to predict where and when future congestion will occur.

[0288] 4. Calculating the optimal route

[0289] The server searches a map database for multiple alternative routes based on the congestion forecast, calculates the distance and estimated arrival time for each route, and then derives the optimal route to the destination.

[0290] 5. Sending optimal route data

[0291] The server then sends the calculated optimal route information to each terminal, which can be a user's smartphone or a car navigation system.

[0292] 6. Route guidance

[0293] The device will then display or provide audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0294] 7. Use of Emotion Engines

[0295] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology, allowing it to determine whether the user is feeling stressed.

[0296] 8. Emotional Data Processing

[0297] The server evaluates the user's stress level based on the emotion data received from the emotion engine. Based on the evaluation result, for example, if the stress level is high, it will suggest a more relaxing route with a beautiful view.

[0298] Hardware and software used

[0299] This system is implemented using a server, terminals (smartphones and car navigation systems), facial recognition cameras, Geopy (a geographic calculation library), Emotion Recognition API, and more.

[0300] Specific examples

[0301] For example, suppose a user is traveling by car from Tokyo to Osaka. The server processes traffic data collected in real time and predicts congestion. The optimal route is calculated and sent to the user's smartphone. Furthermore, if the user's emotional state is recognized as stressed, the system will suggest a scenic route, providing the user with a relaxing driving experience.

[0302] Prompt Sentence Examples

[0303] "Calculate the optimal route from the user's current location (Tokyo) to their destination (Osaka). Also, analyze real-time traffic data and provide a route that avoids traffic jams. Also consider the user's emotional data and select a scenic route if the user's stress level is high."

[0304] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0305] Step 1:

[0306] The server collects real-time data from vehicles and traffic infrastructure. This input includes GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data. This real-time data is received and recorded in the server's database.

[0307] Step 2:

[0308] The server preprocesses the collected real-time data. It analyzes the collected data as input, removes outliers, standardizes data formats, and removes duplicate data. This preprocessing ensures data quality and enables subsequent processing with high accuracy. The output is clean, consistent, and preprocessed data.

[0309] Step 3:

[0310] The server runs a traffic congestion prediction algorithm based on the preprocessed data. It receives the preprocessed data as input and analyzes current speed and location information. It performs data calculations to predict the location and time of future congestion. The output is congestion prediction data.

[0311] Step 4:

[0312] The server calculates the optimal route based on the congestion prediction results. Inputs include congestion prediction data and a map database. Alternative routes are searched for, and the distance and predicted arrival time of each route are calculated. Data calculations are used to select the most efficient route from multiple routes. The output is the optimal route data.

[0313] Step 5:

[0314] The server sends the calculated optimal route data to the terminal. It receives the optimal route data as input and transmits it to the terminal. The output is the optimal route information sent to the terminal.

[0315] Step 6:

[0316] The terminal displays or voices the route to the user based on the received optimal route data. It receives optimal route information as input and displays it on the display or outputs voice guidance. The output is route guidance provided to the user.

[0317] Step 7:

[0318] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology. It receives face and voice data as input and analyzes the emotional state. The output is the user's emotional data.

[0319] Step 8:

[0320] The server evaluates the user's stress level based on the emotion data received from the emotion engine. It receives emotion data as input and calculates the user's stress level. Based on this, it suggests a scenic route that will allow users with high stress levels to relax. The output is the adjusted optimal route information.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] [Second embodiment]

[0325] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0326] 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.

[0327] 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).

[0328] 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.

[0329] 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.

[0330] 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).

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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."

[0337] The system of the present invention collects real-time data and predicts traffic congestion to provide users with optimal routes. The basic configuration for implementing this system will be described in detail below.

[0338] 1. Real-time data collection

[0339] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0340] 2. Data Preprocessing

[0341] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0342] 3. Implementing traffic congestion prediction

[0343] The server then runs a traffic congestion prediction algorithm based on the pre-processed data, which analyzes current speed and location information to predict where and when future congestion will occur.

[0344] 4. Calculating the optimal route

[0345] The server calculates the optimal route based on the congestion prediction results, which involves searching for alternative routes in a map database and calculating the distance and estimated time for each route.

[0346] 5. Sending optimal route data

[0347] The server then transmits the calculated optimal route information to each terminal, which may be a car navigation system or a smartphone.

[0348] 6. Route guidance

[0349] The device displays or provides audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0350] Specific examples of program processing

[0351] As a concrete example, consider a scenario during the morning commute.

[0352] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0353] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0354] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0355] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0356] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0357] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0358] 7. The user follows the navigation instructions to avoid major road A and take detour route B to avoid traffic jams.

[0359] This system can effectively avoid congestion in real time, improving fuel efficiency and reducing CO2 emissions.

[0360] The processing flow will be explained below.

[0361] Step 1:

[0362] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and automatically records this information in a database.

[0363] Step 2:

[0364] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0365] Step 3:

[0366] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0367] Step 4:

[0368] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0369] Step 5:

[0370] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0371] Step 6:

[0372] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0373] Step 7:

[0374] The user follows the guidance from the device and proceeds along the suggested optimal route. Specifically, the driver operates the steering wheel according to the navigation instructions and drives along the instructed route to avoid traffic jams.

[0375] Through this series of steps, the system predicts and avoids traffic congestion in real time and provides efficient route guidance, thereby improving fuel efficiency and reducing CO2 emissions.

[0376] Example 1

[0377] 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."

[0378] Conventional traffic guidance systems have the problem of being unable to accurately predict traffic congestion in real time, making it difficult to provide appropriate route guidance. Furthermore, there is a risk of incorrect predictions if outliers or duplicate data are included. Furthermore, delays in collecting data from terminals can make it difficult to provide timely guidance. To solve these issues, a traffic guidance system with excellent accuracy and immediacy is needed.

[0379] 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.

[0380] In this invention, the server includes a means for collecting real-time data, a means for removing outliers, standardizing the data format, and deleting duplicate data, and a means for predicting traffic conditions based on the collected real-time data, thereby enabling highly accurate traffic congestion predictions and rapid route guidance.

[0381] "Real-time data" refers to digital information such as GPS coordinates, speed information, and traffic sign recognition data that instantly reflects traffic conditions.

[0382] "Abnormal values" are numerical values ​​such as speed and location information that would not occur under normal traffic conditions, and are data that hinder analysis.

[0383] "Data unification" is the process of converting disparately collected data into a consistent format.

[0384] "Duplicate data" refers to data in which the same information is collected multiple times during the same time period, which can reduce the accuracy of analysis.

[0385] "Traffic condition prediction" is the result of analysis by an algorithm that predicts future traffic conditions based on current data.

[0386] An "optimal route" is a route that avoids predicted traffic congestion and estimates the shortest time to reach a destination.

[0387] A "terminal" is a device that provides route guidance to a user, such as a navigation system or a smartphone.

[0388] A "map database" is a database that contains geographical information and road network information and is used for route calculations.

[0389] The system of the present invention collects traffic data in real time, predicts congestion, and provides users with optimal routes. This system is composed of a server, terminals, and users.

[0390] First, the server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, etc. The server records this data in a database to understand detailed traffic conditions.

[0391] The server then preprocesses the collected data, which includes removing outliers, standardizing data formats, and deleting duplicate data, such as data with missing location information or extreme speed information.

[0392] Based on the pre-processed data, the server runs a traffic congestion prediction algorithm. This algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, if vehicle speed in a particular section suddenly drops, it determines that there is a high possibility of congestion occurring in that location.

[0393] Based on the results of the traffic congestion prediction, the server calculates the optimal route. In this process, it searches for multiple alternative routes from a map database and calculates the distance and estimated time for each route. This allows the user to reach their destination in the shortest time.

[0394] The calculated optimal route data is sent to a device with navigation functions (such as an automobile system or a smartphone). The device then uses this data to provide route guidance to the user. Specifically, the device displays the route on a screen and provides voice guidance such as "Turn right at the next intersection."

[0395] As an example of this system, consider a scenario during a morning commute. At 8:00 AM, the server receives GPS information, speed data, and surrounding traffic sign recognition data from the car. The server then preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data. After preprocessing is complete, the server runs a traffic congestion prediction algorithm to predict when congestion will occur on a specific road. To avoid congestion, the server searches for multiple alternative routes and calculates the distance and estimated arrival time for each. The calculated optimal route data is then sent to the car's navigation system, and the user can follow the new route guidance to avoid congestion.

[0396] An example of a prompt sentence is, "There is a possibility of traffic congestion on main road A. Please calculate the optimal route taking into account the state of fatigue on alternative route B."

[0397] The above is a specific embodiment for carrying out the present invention. This system allows users to effectively avoid traffic jams in real time, and is expected to improve fuel efficiency and reduce CO2 emissions.

[0398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0399] Step 1:

[0400] Real-time data collection

[0401] The server collects real-time data from vehicles and traffic infrastructure. As input, it receives GPS coordinates, speed information, and traffic sign recognition data. These data are obtained from GPS devices, speed sensors, and traffic sign recognition cameras installed in each vehicle. The received data is recorded in a database. Specifically, the server receives vehicle location information and image data in real time, updated every second.

[0402] Output: The collected real-time data is stored in a database.

[0403] Step 2:

[0404] Data Preprocessing

[0405] The server preprocesses the collected data. It uses real-time collected data as input. Data preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. Specifically, the server detects and deletes abnormal speed data (e.g., 300 km / h), converts it into a unified format, and deletes duplicate data.

[0406] Output: Preprocessed data with outliers removed, formatted uniformly, and duplicates removed.

[0407] Step 3:

[0408] Implementing traffic congestion predictions

[0409] The server uses the preprocessed data to run a traffic congestion prediction algorithm. The preprocessed data is used as input. The algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, the server analyzes the speed data from the past hour, detects a consistent decrease in vehicle speed at a specific location, and predicts congestion at that location.

[0410] Output: Prediction data on the location and time of future congestion is obtained.

[0411] Step 4:

[0412] Optimal Route Calculation

[0413] The server calculates the optimal route based on the congestion prediction results. The predicted congestion data and map database are used as input. Calculating the optimal route involves searching for multiple alternative routes and calculating the distance and predicted time for each route. Specifically, the server searches for multiple routes that avoid major road A, where congestion is predicted, and calculates the arrival time for each.

[0414] Output: The optimal route data to reach the destination in the shortest time is obtained.

[0415] Step 5:

[0416] Sending optimal route data

[0417] The server sends the calculated optimal route data to the terminal. The optimal route data is used as input. The transmission is done in real time, so that the user can use it immediately. Specifically, the server sends the new route data to the terminal and notifies the user that "a new route has been found."

[0418] Output: The optimal route data is sent to the terminal.

[0419] Step 6:

[0420] Execute route guidance

[0421] The terminal provides route guidance to the user based on the received optimal route data. The optimal route data is used as input. The guidance includes display and voice guidance. Specifically, the terminal displays the new route on the display and provides voice guidance such as "Turn right at the next intersection."

[0422] Output: Visual and audio route guidance is provided to the user.

[0423] (Application example 1)

[0424] 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."

[0425] In food delivery, traffic congestion is a major obstacle to efficient delivery, which can delay delivery times and reduce customer satisfaction. Current systems have difficulty predicting traffic congestion in real time and providing optimal routes based on the results. A method is needed to solve this problem and improve delivery efficiency and customer satisfaction.

[0426] 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.

[0427] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction results, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, and means for optimizing routes for delivery work in real time and providing the route to delivery personnel, thereby making it possible to avoid traffic congestion during delivery work and perform deliveries efficiently.

[0428] "Real-time data" refers to data that can be obtained instantly from moving objects and transportation infrastructure, including information on their current status, location, speed, etc.

[0429] "Traffic conditions" refers to information that indicates the current traffic flow, whether there is congestion, speed limits, etc. on a particular road or in an area.

[0430] "Prediction results" are information obtained as a result of predicting future traffic conditions based on collected real-time data.

[0431] An "optimal route" is a route calculated to be the most efficient route between a departure point and a destination point that avoids traffic congestion.

[0432] A "terminal" is a device that has a navigation function and displays or provides audio guidance on optimal routes, examples of which include in-car navigation systems and smartphones.

[0433] "Delivery operations" refers to the general business process of transporting goods from a specific location to a customer, such as food delivery.

[0434] "Delivery person" refers to an individual or group engaged in food delivery work, etc., and whose role is to deliver goods to customers.

[0435] The embodiment of the present invention is a real-time traffic condition prediction and route guidance system that includes a server, a terminal, and a user who operates the terminal. Specifically, the present invention is implemented as a system including the following means.

[0436] 1. Real-time data collection

[0437] The server collects real-time data from mobile devices and transportation infrastructure devices. Specifically, it uses a smartphone as a terminal to receive current GPS coordinates, speed information, traffic sign recognition data, etc. from delivery vehicles. This is done using a cloud environment such as AWS EC2.

[0438] 2. Data Preprocessing

[0439] The server preprocesses the collected real-time data using Python scripts, which includes removing outliers, standardizing data formats, and removing duplicate data using the Pandas library.

[0440] 3. Implementing traffic congestion prediction

[0441] The server uses the preprocessed data to predict congestion using a TensorFlow model, which makes it possible to predict where and when future congestion will occur.

[0442] 4. Calculating the optimal route

[0443] The server calculates the optimal route using the Google Maps API, which finds alternative routes and calculates distance and estimated arrival time.

[0444] 5. Sending optimal route data

[0445] The server sends the calculated optimal route data in real time via a RESTful API to the delivery person's smartphone, which has an application developed using React Native installed on it.

[0446] 6. Route guidance

[0447] The device (smartphone) displays the received optimal route information and provides voice guidance using Siri on iOS or Google Assistant on Android.

[0448] Specific examples

[0449] For example, consider a scenario in which a delivery driver delivers catering during the morning rush hour. The server receives GPS and speed data sent from the delivery vehicle at 8:00 AM. After removing outliers and standardizing the data format, it uses a TensorFlow model to predict congestion and predicts that congestion will occur on main road A. To do this, the server uses the Google Maps API to calculate alternative route B and sends it to the delivery driver's smartphone. The app displays the new route data and provides a voice prompt saying, "Congestion is predicted on main road A. Please proceed to detour route B."

[0450] Prompt Sentence Examples

[0451] An example of a prompt sentence when using a generative AI model is as follows:

[0452] "Generate Python code that predicts the optimal route using given traffic data."

[0453] In this way, the system of the present invention enables food delivery personnel to efficiently avoid traffic congestion and quickly deliver products to customers, which is expected to improve business efficiency and customer satisfaction.

[0454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0455] Step 1:

[0456] The server collects real-time data from smartphones in delivery vehicles and traffic infrastructure devices. Specifically, it receives GPS coordinates, speed information, traffic sign recognition data, etc. and records it in a database. The input here is real-time data from each device, and the output is raw data recorded on the server.

[0457] Step 2:

[0458] The server preprocesses the collected real-time data using a Python script to remove outliers, standardize data formats, and remove duplicate data to generate preprocessed data. The input here is the raw data recorded in step 1, and the output is the preprocessed data.

[0459] Step 3:

[0460] The server uses the preprocessed data to predict traffic congestion using a TensorFlow model. The preprocessed data is input into the model, which generates predictions about the location and time of future congestion. The input here is the preprocessed data, and the output is the prediction.

[0461] Step 4:

[0462] The server uses the Google Maps API to calculate the optimal route based on the traffic congestion forecast. The forecast results are passed to the API, which searches for alternative routes and calculates the distance and estimated arrival time. The input here is the forecast results and map information, and the output is the optimal route information.

[0463] Step 5:

[0464] The server sends the calculated optimal route data to the delivery person's smartphone in real time via a RESTful API. The input here is the optimal route information, and the output is the route data sent to the delivery person's device.

[0465] Step 6:

[0466] The device displays the received optimal route information to the user and provides voice guidance. Specifically, voice guidance is provided using Siri on iOS or Google Assistant on Android. The input here is the optimal route data sent from the server, and the output is route guidance information for the user.

[0467] In this way, the server collects and preprocesses real-time data, predicts traffic conditions, and calculates the optimal route, and the device provides route guidance to the user based on that information. This enables efficient route selection in delivery work and is expected to improve customer satisfaction.

[0468] 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.

[0469] The system of the present invention not only collects real-time data and predicts traffic congestion, but also combines it with an emotion engine that recognizes the user's emotions to provide route guidance tailored to individual needs. The basic configuration for implementing this system is described in detail below.

[0470] 1. Real-time data collection

[0471] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0472] 2. Data Preprocessing

[0473] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0474] 3. Implementing traffic congestion prediction

[0475] The server runs a traffic congestion prediction algorithm on the pre-processed data, analyzing current speed and location information to predict where and when future congestion will occur.

[0476] 4. Calculating the optimal route

[0477] The server calculates the optimal route based on the congestion forecast, searches for alternative routes in a map database, and calculates the distance and estimated arrival time of each route.

[0478] 5. Sending optimal route data

[0479] The server then sends the calculated optimal route information to each terminal, which may be a car system with a navigation function or a smartphone.

[0480] 6. Route guidance

[0481] The device will then display or provide audio guidance to the user based on the optimal route data it receives, allowing the user to efficiently avoid traffic jams.

[0482] 7. Adding an Emotion Engine

[0483] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, allowing it to determine whether the user is feeling stressed.

[0484] 8. Emotional Data Processing

[0485] The server evaluates the user's stress level based on the received emotional data. Based on the evaluation results, for example, if the stress level is high, it can suggest a more relaxing route with good scenery, helping the user continue driving in a relaxed state.

[0486] Specific examples

[0487] Let us consider a specific scenario during the morning commute.

[0488] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0489] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0490] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0491] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0492] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0493] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0494] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0495] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0496] In this way, the present invention can predict traffic congestion in real time and provide optimal route guidance according to the user's individual emotional state, thereby achieving both efficient driving and user comfort at the same time.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and receives and automatically records this information transmitted by vehicles in a database.

[0500] Step 2:

[0501] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0502] Step 3:

[0503] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0504] Step 4:

[0505] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0506] Step 5:

[0507] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0508] Step 6:

[0509] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0510] Step 7:

[0511] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, and can determine whether the user is feeling stressed by analyzing the user's facial expressions and tone of voice, for example.

[0512] Step 8:

[0513] The server evaluates the user's stress level based on the received emotional data. Specifically, if the user's stress level is high, the server prioritizes scenic routes that are relaxing.

[0514] Step 9:

[0515] The device receives the new optimal route again and notifies the user. The navigation system displays the new route on the display and asks the voice guidance, "A relaxing route has been found. Would you like to proceed with this route?"

[0516] Step 10:

[0517] The user follows the navigation system's instructions and follows the suggested route. Specifically, the driver follows the navigation system's instructions and operates the steering wheel to drive along the optimal route, reducing stress and providing a comfortable drive.

[0518] Through this series of steps, the system not only predicts and avoids traffic jams in real time, but also provides optimal route guidance according to the user's emotional state, thereby improving both travel efficiency and user comfort.

[0519] Example 2

[0520] 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."

[0521] Conventional traffic congestion prediction systems were able to collect real-time traffic data and provide congestion predictions and route guidance based on that data, but they were unable to provide route guidance that took into account the user's emotional state or stress level. As a result, they were unable to improve the user's mental comfort and lacked a means to reduce stress while driving.

[0522] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting real-time data from automobiles and traffic infrastructure, means for pre-processing the collected real-time data, means for predicting traffic congestion, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for collecting user emotion data through the terminal, means for evaluating the emotion data to determine the user's stress level, and means for suggesting a route according to the user's stress level. This enables optimal route guidance that takes the user's emotional state into consideration.

[0523] "Real-time data" refers to data that is immediately available based on time, such as the latest location information, speed information, and traffic sign recognition data obtained on the spot from vehicles and transportation infrastructure.

[0524] "Preprocessing" refers to processing collected real-time data, such as removing outliers, standardizing data formats, and deleting duplicate data, which can improve the quality of the data.

[0525] "Traffic congestion prediction" refers to predicting future traffic conditions based on preprocessed real-time data, particularly the locations and times when traffic flow will be slow.

[0526] The "optimal route" is the most efficient route calculated based on traffic congestion prediction results. It is a route that avoids predicted congestion and minimizes the distance and time to the destination.

[0527] "Terminal" refers to a device that receives information on optimal routes and provides route guidance to users. Specifically, this refers to an in-vehicle system or smartphone with a navigation function.

[0528] "Emotional Data" means data indicative of a user's emotional state, collected using facial recognition and / or voice analysis technologies, and used to determine a user's stress level or other emotional state.

[0529] "Evaluating emotional data" refers to analyzing collected emotional data to determine the user's stress level or other emotional state.

[0530] "Relaxing routes" are routes suggested based on the user's stress level. They refer to routes that enhance the user's mental comfort, such as scenic roads or routes that avoid crowds.

[0531] This invention is a system that collects real-time data from vehicles and transportation infrastructure to predict traffic congestion. Furthermore, the system can recognize the user's emotional state and provide route guidance according to their stress level.

[0532] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and records it in a database. This is done by collecting data via Apache Kafka and storing it in a database using AWS RDS.

[0533] The collected data is preprocessed using Apache Spark. This preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. The preprocessed data is then used to run a traffic congestion prediction algorithm. Specifically, a machine learning model is applied using Apache Hadoop and TensorFlow to predict the location and time of future congestion.

[0534] The server calculates the optimal route based on the congestion forecast results. This calculation uses Google Maps API and Graphhopper to calculate the distance and predicted arrival time of alternative routes. The calculated optimal route information is sent to each terminal via REST API. The terminals can be automobile systems with navigation functions or smartphones.

[0535] The device will display or voice directions to the user based on the received optimal route data, using the Android or iOS platform to integrate the Google Maps application.

[0536] Additionally, the device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. Specifically, it uses OpenCV and the Google Cloud Speech-to-Text API to determine whether the user is feeling stressed. The collected emotional data is sent to a server, where Google Cloud AI is used to evaluate the user's stress level.

[0537] Based on the evaluation results, if the user's stress level is high, the server will suggest a scenic route that is expected to have a relaxing effect, thereby helping the user continue driving in a relaxed state.

[0538] Specific examples

[0539] Let us consider a specific scenario during the morning commute.

[0540] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0541] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0542] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0543] 4. The server searches for alternative routes B to avoid traffic jams using the Google Maps API and Graphhopper, and calculates the distance and estimated arrival time of each alternative route.

[0544] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0545] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0546] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0547] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0548] Examples of prompts for generative AI models

[0549] For example, you can use the following prompt for a generative AI model:

[0550] Prompt: Please describe in detail the process of a system that combines real-time traffic data and emotion recognition to provide users with optimal route guidance and relaxing routes. Please also name the specific software and hardware.

[0551] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0552] Step 1:

[0553] The server collects real-time data from vehicles and traffic infrastructure. Specifically, the inputs include GPS coordinates, speed information, and traffic sign recognition data. Apache Kafka is used to stream the data in real time and record it in an AWS RDS database. This process allows the server to obtain data that reflects the latest traffic conditions.

[0554] Step 2:

[0555] The server preprocesses the collected real-time data. The input for preprocessing is the data collected in step 1, and Apache Spark is used here. Specific preprocessing operations include removing outliers, standardizing data formats, and deleting duplicate data. This process outputs high-quality, analyzable data.

[0556] Step 3:

[0557] The server runs a traffic congestion prediction algorithm based on the preprocessed data. The preprocessed data is input, and a machine learning model is applied using Apache Hadoop and TensorFlow. The congestion prediction algorithm analyzes current speed and location information to predict the location and time of future congestion. The predicted location and time of congestion are output as the result.

[0558] Step 4:

[0559] The server calculates the optimal route based on the congestion forecast results. The input is the congestion forecast results and a map database, and route searches are performed using the Google Maps API and Graphhopper. The distance and predicted arrival time of each alternative route are calculated and the optimal route is selected. The output of this process is the optimal route data.

[0560] Step 5:

[0561] The server sends the calculated optimal route information to each terminal. The input is the optimal route data obtained in step 4, and uses the HTTP protocol via the REST API. The terminals are devices with navigation functions (in-vehicle systems or smartphones). As a result of the transmission, each terminal receives the optimal route data.

[0562] Step 6:

[0563] Based on the received optimal route data, the device displays the route or provides voice guidance to the user. The input is the optimal route data, and the Google Maps application is integrated using the Android or iOS platform. Specifically, the device displays the route on the display and provides voice guidance saying, "Congestion is predicted on main road A. Please proceed to detour route B." The output of this process is route guidance information for the user.

[0564] Step 7:

[0565] The device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. The input is the user's facial expression data and voice data, which are analyzed using OpenCV and the Google Cloud Speech-to-Text API. The device determines whether the user is feeling stressed and sends that information to the server. The output of this process is the user's emotional data.

[0566] Step 8:

[0567] The server evaluates the user's stress level based on the received emotional data. The input is emotional data, which is analyzed using Google Cloud AI. If a high stress level is detected, a scenic route that is expected to have a relaxing effect is calculated, and new route guidance is sent to the device. The output of this process is optimal route guidance data based on the user's emotional state.

[0568] The above explains the specific operations, inputs, and outputs performed at each step. This system can predict traffic congestion and provide optimal route guidance according to the user's emotional state.

[0569] (Application example 2)

[0570] 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."

[0571] In order to provide effective route guidance for autonomous vehicles, it is necessary not only to avoid traffic congestion but also to realize a comfortable driving experience that takes into account the user's emotional state. However, conventional navigation systems only collect real-time traffic data and predict congestion, but do not provide route guidance that takes the user's emotional state into account. This poses a challenge: it is not possible to ensure user comfort while effectively avoiding congestion.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0573] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, means for recognizing the emotional state of a user, and means for adjusting the route based on the recognized emotional state. This makes it possible to grasp traffic conditions in real time and provide an optimal route according to the emotional state of the user.

[0574] "Real-time data" refers to data that reflects the current situation and is collected every moment from vehicles and transportation infrastructure.

[0575] "Traffic conditions" refers to the overall situation, including road congestion, traffic volume, and speed information.

[0576] An "optimal route" is a route that allows for the most efficient and comfortable travel, taking into account the distance to the destination, travel time, and traffic conditions.

[0577] "Terminal" refers to a device operated by a user, such as a car system with navigation function or a smartphone.

[0578] "Emotional state" refers to the user's psychological emotional state, such as stress, relaxation, or impatience.

[0579] "Route guidance" refers to providing the user with guidance to their destination, and is done by display or voice.

[0580] An "emotion engine" refers to a system that uses facial recognition cameras and voice analysis technology to recognize a user's emotional state.

[0581] "Adjustment means" refers to methods and techniques for correcting or optimizing the optimal route based on collected data and forecast results.

[0582] "Server" refers to the central computer system that performs real-time data collection, pre-processing, analysis, route calculation and optimization.

[0583] The system of the present invention combines the functions of collecting real-time data and predicting traffic congestion with the functions of recognizing the user's emotional state and suggesting the optimal route. The program and its processing for realizing this system are described below.

[0584] 1. Real-time data collection

[0585] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records this data in a database.

[0586] 2. Data Preprocessing

[0587] The server removes outliers, standardizes data formats, and deletes duplicate data from the collected real-time data. This preprocessing ensures data quality and enables subsequent processing with high accuracy.

[0588] 3. Implementing traffic congestion prediction

[0589] The server runs a traffic congestion prediction algorithm based on the pre-processed data, analyzing actual vehicle speeds and location information to predict where and when future congestion will occur.

[0590] 4. Calculating the optimal route

[0591] The server searches a map database for multiple alternative routes based on the congestion forecast, calculates the distance and estimated arrival time for each route, and then derives the optimal route to the destination.

[0592] 5. Sending optimal route data

[0593] The server then sends the calculated optimal route information to each terminal, which can be a user's smartphone or a car navigation system.

[0594] 6. Route guidance

[0595] The device will then display or provide audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0596] 7. Use of Emotion Engines

[0597] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology, allowing it to determine whether the user is feeling stressed.

[0598] 8. Emotional Data Processing

[0599] The server evaluates the user's stress level based on the emotion data received from the emotion engine. Based on the evaluation result, for example, if the stress level is high, it will suggest a more relaxing route with a beautiful view.

[0600] Hardware and software used

[0601] This system is implemented using a server, terminals (smartphones and car navigation systems), facial recognition cameras, Geopy (a geographic calculation library), Emotion Recognition API, and more.

[0602] Specific examples

[0603] For example, suppose a user is traveling by car from Tokyo to Osaka. The server processes traffic data collected in real time and predicts congestion. The optimal route is calculated and sent to the user's smartphone. Furthermore, if the user's emotional state is recognized as stressed, the system will suggest a scenic route, providing the user with a relaxing driving experience.

[0604] Prompt Sentence Examples

[0605] "Calculate the optimal route from the user's current location (Tokyo) to their destination (Osaka). Also, analyze real-time traffic data and provide a route that avoids traffic jams. Also consider the user's emotional data and select a scenic route if the user's stress level is high."

[0606] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0607] Step 1:

[0608] The server collects real-time data from vehicles and traffic infrastructure. This input includes GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data. This real-time data is received and recorded in the server's database.

[0609] Step 2:

[0610] The server preprocesses the collected real-time data. It analyzes the collected data as input, removes outliers, standardizes data formats, and removes duplicate data. This preprocessing ensures data quality and enables subsequent processing with high accuracy. The output is clean, consistent, and preprocessed data.

[0611] Step 3:

[0612] The server runs a traffic congestion prediction algorithm based on the preprocessed data. It receives the preprocessed data as input and analyzes current speed and location information. It performs data calculations to predict the location and time of future congestion. The output is congestion prediction data.

[0613] Step 4:

[0614] The server calculates the optimal route based on the congestion prediction results. Inputs include congestion prediction data and a map database. Alternative routes are searched for, and the distance and predicted arrival time of each route are calculated. Data calculations are used to select the most efficient route from multiple routes. The output is the optimal route data.

[0615] Step 5:

[0616] The server sends the calculated optimal route data to the terminal. It receives the optimal route data as input and transmits it to the terminal. The output is the optimal route information sent to the terminal.

[0617] Step 6:

[0618] The terminal displays or voices the route to the user based on the received optimal route data. It receives optimal route information as input and displays it on the display or outputs voice guidance. The output is route guidance provided to the user.

[0619] Step 7:

[0620] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology. It receives face and voice data as input and analyzes the emotional state. The output is the user's emotional data.

[0621] Step 8:

[0622] The server evaluates the user's stress level based on the emotion data received from the emotion engine. It receives emotion data as input and calculates the user's stress level. Based on this, it suggests a scenic route that will allow users with high stress levels to relax. The output is the adjusted optimal route information.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] [Third embodiment]

[0627] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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."

[0639] The system of the present invention collects real-time data and predicts traffic congestion to provide users with optimal routes. The basic configuration for implementing this system will be described in detail below.

[0640] 1. Real-time data collection

[0641] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0642] 2. Data Preprocessing

[0643] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0644] 3. Implementing traffic congestion prediction

[0645] The server then runs a traffic congestion prediction algorithm based on the pre-processed data, which analyzes current speed and location information to predict where and when future congestion will occur.

[0646] 4. Calculating the optimal route

[0647] The server calculates the optimal route based on the congestion prediction results, which involves searching for alternative routes in a map database and calculating the distance and estimated time for each route.

[0648] 5. Sending optimal route data

[0649] The server then transmits the calculated optimal route information to each terminal, which may be a car navigation system or a smartphone.

[0650] 6. Route guidance

[0651] The device displays or provides audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0652] Specific examples of program processing

[0653] As a concrete example, consider a scenario during the morning commute.

[0654] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0655] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0656] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0657] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0658] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0659] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0660] 7. The user follows the navigation instructions to avoid major road A and take detour route B to avoid traffic jams.

[0661] This system can effectively avoid congestion in real time, improving fuel efficiency and reducing CO2 emissions.

[0662] The processing flow will be explained below.

[0663] Step 1:

[0664] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and automatically records this information in a database.

[0665] Step 2:

[0666] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0667] Step 3:

[0668] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0669] Step 4:

[0670] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0671] Step 5:

[0672] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0673] Step 6:

[0674] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0675] Step 7:

[0676] The user follows the guidance from the device and proceeds along the suggested optimal route. Specifically, the driver operates the steering wheel according to the navigation instructions and drives along the instructed route to avoid traffic jams.

[0677] Through this series of steps, the system predicts and avoids traffic congestion in real time and provides efficient route guidance, thereby improving fuel efficiency and reducing CO2 emissions.

[0678] Example 1

[0679] 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."

[0680] Conventional traffic guidance systems have the problem of being unable to accurately predict traffic congestion in real time, making it difficult to provide appropriate route guidance. Furthermore, there is a risk of incorrect predictions if outliers or duplicate data are included. Furthermore, delays in collecting data from terminals can make it difficult to provide timely guidance. To solve these issues, a traffic guidance system with excellent accuracy and immediacy is needed.

[0681] 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.

[0682] In this invention, the server includes a means for collecting real-time data, a means for removing outliers, standardizing the data format, and deleting duplicate data, and a means for predicting traffic conditions based on the collected real-time data, thereby enabling highly accurate traffic congestion predictions and rapid route guidance.

[0683] "Real-time data" refers to digital information such as GPS coordinates, speed information, and traffic sign recognition data that instantly reflects traffic conditions.

[0684] "Abnormal values" are numerical values ​​such as speed and location information that would not occur under normal traffic conditions, and are data that hinder analysis.

[0685] "Data unification" is the process of converting disparately collected data into a consistent format.

[0686] "Duplicate data" refers to data in which the same information is collected multiple times during the same time period, which can reduce the accuracy of analysis.

[0687] "Traffic condition prediction" is the result of analysis by an algorithm that predicts future traffic conditions based on current data.

[0688] An "optimal route" is a route that avoids predicted traffic congestion and estimates the shortest time to reach a destination.

[0689] A "terminal" is a device that provides route guidance to a user, such as a navigation system or a smartphone.

[0690] A "map database" is a database that contains geographical information and road network information and is used for route calculations.

[0691] The system of the present invention collects traffic data in real time, predicts congestion, and provides users with optimal routes. This system is composed of a server, terminals, and users.

[0692] First, the server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, etc. The server records this data in a database to understand detailed traffic conditions.

[0693] The server then preprocesses the collected data, which includes removing outliers, standardizing data formats, and deleting duplicate data, such as data with missing location information or extreme speed information.

[0694] Based on the pre-processed data, the server runs a traffic congestion prediction algorithm. This algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, if vehicle speed in a particular section suddenly drops, it determines that there is a high possibility of congestion occurring in that location.

[0695] Based on the results of the traffic congestion prediction, the server calculates the optimal route. In this process, it searches for multiple alternative routes from a map database and calculates the distance and estimated time for each route. This allows the user to reach their destination in the shortest time.

[0696] The calculated optimal route data is sent to a device with navigation functions (such as an automobile system or a smartphone). The device then uses this data to provide route guidance to the user. Specifically, the device displays the route on a screen and provides voice guidance such as "Turn right at the next intersection."

[0697] As an example of this system, consider a scenario during a morning commute. At 8:00 AM, the server receives GPS information, speed data, and surrounding traffic sign recognition data from the car. The server then preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data. After preprocessing is complete, the server runs a traffic congestion prediction algorithm to predict when congestion will occur on a specific road. To avoid congestion, the server searches for multiple alternative routes and calculates the distance and estimated arrival time for each. The calculated optimal route data is then sent to the car's navigation system, and the user can follow the new route guidance to avoid congestion.

[0698] An example of a prompt sentence is, "There is a possibility of traffic congestion on main road A. Please calculate the optimal route taking into account the state of fatigue on alternative route B."

[0699] The above is a specific embodiment for carrying out the present invention. This system allows users to effectively avoid traffic jams in real time, and is expected to improve fuel efficiency and reduce CO2 emissions.

[0700] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0701] Step 1:

[0702] Real-time data collection

[0703] The server collects real-time data from vehicles and traffic infrastructure. As input, it receives GPS coordinates, speed information, and traffic sign recognition data. These data are obtained from GPS devices, speed sensors, and traffic sign recognition cameras installed in each vehicle. The received data is recorded in a database. Specifically, the server receives vehicle location information and image data in real time, updated every second.

[0704] Output: The collected real-time data is stored in a database.

[0705] Step 2:

[0706] Data Preprocessing

[0707] The server preprocesses the collected data. It uses real-time collected data as input. Data preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. Specifically, the server detects and deletes abnormal speed data (e.g., 300 km / h), converts it into a unified format, and deletes duplicate data.

[0708] Output: Preprocessed data with outliers removed, formatted uniformly, and duplicates removed.

[0709] Step 3:

[0710] Implementing traffic congestion predictions

[0711] The server uses the preprocessed data to run a traffic congestion prediction algorithm. The preprocessed data is used as input. The algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, the server analyzes the speed data from the past hour, detects a consistent decrease in vehicle speed at a specific location, and predicts congestion at that location.

[0712] Output: Prediction data on the location and time of future congestion is obtained.

[0713] Step 4:

[0714] Optimal Route Calculation

[0715] The server calculates the optimal route based on the congestion prediction results. The predicted congestion data and map database are used as input. Calculating the optimal route involves searching for multiple alternative routes and calculating the distance and predicted time for each route. Specifically, the server searches for multiple routes that avoid major road A, where congestion is predicted, and calculates the arrival time for each.

[0716] Output: The optimal route data to reach the destination in the shortest time is obtained.

[0717] Step 5:

[0718] Sending optimal route data

[0719] The server sends the calculated optimal route data to the terminal. The optimal route data is used as input. The transmission is done in real time, so that the user can use it immediately. Specifically, the server sends the new route data to the terminal and notifies the user that "a new route has been found."

[0720] Output: The optimal route data is sent to the terminal.

[0721] Step 6:

[0722] Execute route guidance

[0723] The terminal provides route guidance to the user based on the received optimal route data. The optimal route data is used as input. The guidance includes display and voice guidance. Specifically, the terminal displays the new route on the display and provides voice guidance such as "Turn right at the next intersection."

[0724] Output: Visual and audio route guidance is provided to the user.

[0725] (Application example 1)

[0726] 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."

[0727] In food delivery, traffic congestion is a major obstacle to efficient delivery, which can delay delivery times and reduce customer satisfaction. Current systems have difficulty predicting traffic congestion in real time and providing optimal routes based on the results. A method is needed to solve this problem and improve delivery efficiency and customer satisfaction.

[0728] 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.

[0729] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction results, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, and means for optimizing routes for delivery work in real time and providing the route to delivery personnel, thereby making it possible to avoid traffic congestion during delivery work and perform deliveries efficiently.

[0730] "Real-time data" refers to data that can be obtained instantly from moving objects and transportation infrastructure, including information on their current status, location, speed, etc.

[0731] "Traffic conditions" refers to information that indicates the current traffic flow, whether there is congestion, speed limits, etc. on a particular road or in an area.

[0732] "Prediction results" are information obtained as a result of predicting future traffic conditions based on collected real-time data.

[0733] An "optimal route" is a route calculated to be the most efficient route between a departure point and a destination point that avoids traffic congestion.

[0734] A "terminal" is a device that has a navigation function and displays or provides audio guidance on optimal routes, examples of which include in-car navigation systems and smartphones.

[0735] "Delivery operations" refers to the general business process of transporting goods from a specific location to a customer, such as food delivery.

[0736] "Delivery person" refers to an individual or group engaged in food delivery work, etc., and whose role is to deliver goods to customers.

[0737] The embodiment of the present invention is a real-time traffic condition prediction and route guidance system that includes a server, a terminal, and a user who operates the terminal. Specifically, the present invention is implemented as a system including the following means.

[0738] 1. Real-time data collection

[0739] The server collects real-time data from mobile devices and transportation infrastructure devices. Specifically, it uses a smartphone as a terminal to receive current GPS coordinates, speed information, traffic sign recognition data, etc. from delivery vehicles. This is done using a cloud environment such as AWS EC2.

[0740] 2. Data Preprocessing

[0741] The server preprocesses the collected real-time data using Python scripts, which includes removing outliers, standardizing data formats, and removing duplicate data using the Pandas library.

[0742] 3. Implementing traffic congestion prediction

[0743] The server uses the preprocessed data to predict congestion using a TensorFlow model, which makes it possible to predict where and when future congestion will occur.

[0744] 4. Calculating the optimal route

[0745] The server calculates the optimal route using the Google Maps API, which finds alternative routes and calculates distance and estimated arrival time.

[0746] 5. Sending optimal route data

[0747] The server sends the calculated optimal route data in real time via a RESTful API to the delivery person's smartphone, which has an application developed using React Native installed on it.

[0748] 6. Route guidance

[0749] The device (smartphone) displays the received optimal route information and provides voice guidance using Siri on iOS or Google Assistant on Android.

[0750] Specific examples

[0751] For example, consider a scenario in which a delivery driver delivers catering during the morning rush hour. The server receives GPS and speed data sent from the delivery vehicle at 8:00 AM. After removing outliers and standardizing the data format, it uses a TensorFlow model to predict congestion and predicts that congestion will occur on main road A. To do this, the server uses the Google Maps API to calculate alternative route B and sends it to the delivery driver's smartphone. The app displays the new route data and provides a voice prompt saying, "Congestion is predicted on main road A. Please proceed to detour route B."

[0752] Prompt Sentence Examples

[0753] An example of a prompt sentence when using a generative AI model is as follows:

[0754] "Generate Python code that predicts the optimal route using given traffic data."

[0755] In this way, the system of the present invention enables food delivery personnel to efficiently avoid traffic congestion and quickly deliver products to customers, which is expected to improve business efficiency and customer satisfaction.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The server collects real-time data from smartphones in delivery vehicles and traffic infrastructure devices. Specifically, it receives GPS coordinates, speed information, traffic sign recognition data, etc. and records it in a database. The input here is real-time data from each device, and the output is raw data recorded on the server.

[0759] Step 2:

[0760] The server preprocesses the collected real-time data using a Python script to remove outliers, standardize data formats, and remove duplicate data to generate preprocessed data. The input here is the raw data recorded in step 1, and the output is the preprocessed data.

[0761] Step 3:

[0762] The server uses the preprocessed data to predict traffic congestion using a TensorFlow model. The preprocessed data is input into the model, which generates predictions about the location and time of future congestion. The input here is the preprocessed data, and the output is the prediction.

[0763] Step 4:

[0764] The server uses the Google Maps API to calculate the optimal route based on the traffic congestion forecast. The forecast results are passed to the API, which searches for alternative routes and calculates the distance and estimated arrival time. The input here is the forecast results and map information, and the output is the optimal route information.

[0765] Step 5:

[0766] The server sends the calculated optimal route data to the delivery person's smartphone in real time via a RESTful API. The input here is the optimal route information, and the output is the route data sent to the delivery person's device.

[0767] Step 6:

[0768] The device displays the received optimal route information to the user and provides voice guidance. Specifically, voice guidance is provided using Siri on iOS or Google Assistant on Android. The input here is the optimal route data sent from the server, and the output is route guidance information for the user.

[0769] In this way, the server collects and preprocesses real-time data, predicts traffic conditions, and calculates the optimal route, and the device provides route guidance to the user based on that information. This enables efficient route selection in delivery work and is expected to improve customer satisfaction.

[0770] 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.

[0771] The system of the present invention not only collects real-time data and predicts traffic congestion, but also combines it with an emotion engine that recognizes the user's emotions to provide route guidance tailored to individual needs. The basic configuration for implementing this system is described in detail below.

[0772] 1. Real-time data collection

[0773] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0774] 2. Data Preprocessing

[0775] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0776] 3. Implementing traffic congestion prediction

[0777] The server runs a traffic congestion prediction algorithm on the pre-processed data, analyzing current speed and location information to predict where and when future congestion will occur.

[0778] 4. Calculating the optimal route

[0779] The server calculates the optimal route based on the congestion forecast, searches for alternative routes in a map database, and calculates the distance and estimated arrival time of each route.

[0780] 5. Sending optimal route data

[0781] The server then sends the calculated optimal route information to each terminal, which may be a car system with a navigation function or a smartphone.

[0782] 6. Route guidance

[0783] The device will then display or provide audio guidance to the user based on the optimal route data it receives, allowing the user to efficiently avoid traffic jams.

[0784] 7. Adding an Emotion Engine

[0785] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, allowing it to determine whether the user is feeling stressed.

[0786] 8. Emotional Data Processing

[0787] The server evaluates the user's stress level based on the received emotional data. Based on the evaluation results, for example, if the stress level is high, it can suggest a more relaxing route with good scenery, helping the user continue driving in a relaxed state.

[0788] Specific examples

[0789] Let us consider a specific scenario during the morning commute.

[0790] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0791] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0792] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0793] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0794] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0795] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0796] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0797] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0798] In this way, the present invention can predict traffic congestion in real time and provide optimal route guidance according to the user's individual emotional state, thereby achieving both efficient driving and user comfort at the same time.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and receives and automatically records this information transmitted by vehicles in a database.

[0802] Step 2:

[0803] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0804] Step 3:

[0805] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0806] Step 4:

[0807] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0808] Step 5:

[0809] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0810] Step 6:

[0811] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0812] Step 7:

[0813] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, and can determine whether the user is feeling stressed by analyzing the user's facial expressions and tone of voice, for example.

[0814] Step 8:

[0815] The server evaluates the user's stress level based on the received emotional data. Specifically, if the user's stress level is high, the server prioritizes scenic routes that are relaxing.

[0816] Step 9:

[0817] The device receives the new optimal route again and notifies the user. The navigation system displays the new route on the display and asks the voice guidance, "A relaxing route has been found. Would you like to proceed with this route?"

[0818] Step 10:

[0819] The user follows the navigation system's instructions and follows the suggested route. Specifically, the driver follows the navigation system's instructions and operates the steering wheel to drive along the optimal route, reducing stress and providing a comfortable drive.

[0820] Through this series of steps, the system not only predicts and avoids traffic jams in real time, but also provides optimal route guidance according to the user's emotional state, thereby improving both travel efficiency and user comfort.

[0821] Example 2

[0822] 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."

[0823] Conventional traffic congestion prediction systems were able to collect real-time traffic data and provide congestion predictions and route guidance based on that data, but they were unable to provide route guidance that took into account the user's emotional state or stress level. As a result, they were unable to improve the user's mental comfort and lacked a means to reduce stress while driving.

[0824] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting real-time data from automobiles and traffic infrastructure, means for pre-processing the collected real-time data, means for predicting traffic congestion, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for collecting user emotion data through the terminal, means for evaluating the emotion data to determine the user's stress level, and means for suggesting a route according to the user's stress level. This enables optimal route guidance that takes the user's emotional state into consideration.

[0825] "Real-time data" refers to data that is immediately available based on time, such as the latest location information, speed information, and traffic sign recognition data obtained on the spot from vehicles and transportation infrastructure.

[0826] "Preprocessing" refers to processing collected real-time data, such as removing outliers, standardizing data formats, and deleting duplicate data, which can improve the quality of the data.

[0827] "Traffic congestion prediction" refers to predicting future traffic conditions based on preprocessed real-time data, particularly the locations and times when traffic flow will be slow.

[0828] The "optimal route" is the most efficient route calculated based on traffic congestion prediction results. It is a route that avoids predicted congestion and minimizes the distance and time to the destination.

[0829] "Terminal" refers to a device that receives information on optimal routes and provides route guidance to users. Specifically, this refers to an in-vehicle system or smartphone with a navigation function.

[0830] "Emotional Data" means data indicative of a user's emotional state, collected using facial recognition and / or voice analysis technologies, and used to determine a user's stress level or other emotional state.

[0831] "Evaluating emotional data" refers to analyzing collected emotional data to determine the user's stress level or other emotional state.

[0832] "Relaxing routes" are routes suggested based on the user's stress level. They refer to routes that enhance the user's mental comfort, such as scenic roads or routes that avoid crowds.

[0833] This invention is a system that collects real-time data from vehicles and transportation infrastructure to predict traffic congestion. Furthermore, the system can recognize the user's emotional state and provide route guidance according to their stress level.

[0834] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and records it in a database. This is done by collecting data via Apache Kafka and storing it in a database using AWS RDS.

[0835] The collected data is preprocessed using Apache Spark. This preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. The preprocessed data is then used to run a traffic congestion prediction algorithm. Specifically, a machine learning model is applied using Apache Hadoop and TensorFlow to predict the location and time of future congestion.

[0836] The server calculates the optimal route based on the congestion forecast results. This calculation uses Google Maps API and Graphhopper to calculate the distance and predicted arrival time of alternative routes. The calculated optimal route information is sent to each terminal via REST API. The terminals can be automobile systems with navigation functions or smartphones.

[0837] The device will display or voice directions to the user based on the received optimal route data, using the Android or iOS platform to integrate the Google Maps application.

[0838] Additionally, the device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. Specifically, it uses OpenCV and the Google Cloud Speech-to-Text API to determine whether the user is feeling stressed. The collected emotional data is sent to a server, where Google Cloud AI is used to evaluate the user's stress level.

[0839] Based on the evaluation results, if the user's stress level is high, the server will suggest a scenic route that is expected to have a relaxing effect, thereby helping the user continue driving in a relaxed state.

[0840] Specific examples

[0841] Let us consider a specific scenario during the morning commute.

[0842] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0843] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0844] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0845] 4. The server searches for alternative routes B to avoid traffic jams using the Google Maps API and Graphhopper, and calculates the distance and estimated arrival time of each alternative route.

[0846] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0847] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0848] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[0849] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[0850] Examples of prompts for generative AI models

[0851] For example, you can use the following prompt for a generative AI model:

[0852] Prompt: Please describe in detail the process of a system that combines real-time traffic data and emotion recognition to provide users with optimal route guidance and relaxing routes. Please also name the specific software and hardware.

[0853] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0854] Step 1:

[0855] The server collects real-time data from vehicles and traffic infrastructure. Specifically, the inputs include GPS coordinates, speed information, and traffic sign recognition data. Apache Kafka is used to stream the data in real time and record it in an AWS RDS database. This process allows the server to obtain data that reflects the latest traffic conditions.

[0856] Step 2:

[0857] The server preprocesses the collected real-time data. The input for preprocessing is the data collected in step 1, and Apache Spark is used here. Specific preprocessing operations include removing outliers, standardizing data formats, and deleting duplicate data. This process outputs high-quality, analyzable data.

[0858] Step 3:

[0859] The server runs a traffic congestion prediction algorithm based on the preprocessed data. The preprocessed data is input, and a machine learning model is applied using Apache Hadoop and TensorFlow. The congestion prediction algorithm analyzes current speed and location information to predict the location and time of future congestion. The predicted location and time of congestion are output as the result.

[0860] Step 4:

[0861] The server calculates the optimal route based on the congestion forecast results. The input is the congestion forecast results and a map database, and route searches are performed using the Google Maps API and Graphhopper. The distance and predicted arrival time of each alternative route are calculated and the optimal route is selected. The output of this process is the optimal route data.

[0862] Step 5:

[0863] The server sends the calculated optimal route information to each terminal. The input is the optimal route data obtained in step 4, and uses the HTTP protocol via the REST API. The terminals are devices with navigation functions (in-vehicle systems or smartphones). As a result of the transmission, each terminal receives the optimal route data.

[0864] Step 6:

[0865] Based on the received optimal route data, the device displays the route or provides voice guidance to the user. The input is the optimal route data, and the Google Maps application is integrated using the Android or iOS platform. Specifically, the device displays the route on the display and provides voice guidance saying, "Congestion is predicted on main road A. Please proceed to detour route B." The output of this process is route guidance information for the user.

[0866] Step 7:

[0867] The device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. The input is the user's facial expression data and voice data, which are analyzed using OpenCV and the Google Cloud Speech-to-Text API. The device determines whether the user is feeling stressed and sends that information to the server. The output of this process is the user's emotional data.

[0868] Step 8:

[0869] The server evaluates the user's stress level based on the received emotional data. The input is emotional data, which is analyzed using Google Cloud AI. If a high stress level is detected, a scenic route that is expected to have a relaxing effect is calculated, and new route guidance is sent to the device. The output of this process is optimal route guidance data based on the user's emotional state.

[0870] The above explains the specific operations, inputs, and outputs performed at each step. This system can predict traffic congestion and provide optimal route guidance according to the user's emotional state.

[0871] (Application example 2)

[0872] 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."

[0873] In order to provide effective route guidance for autonomous vehicles, it is necessary not only to avoid traffic congestion but also to realize a comfortable driving experience that takes into account the user's emotional state. However, conventional navigation systems only collect real-time traffic data and predict congestion, but do not provide route guidance that takes the user's emotional state into account. This poses a challenge: it is not possible to ensure user comfort while effectively avoiding congestion.

[0874] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0875] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, means for recognizing the emotional state of a user, and means for adjusting the route based on the recognized emotional state. This makes it possible to grasp traffic conditions in real time and provide an optimal route according to the emotional state of the user.

[0876] "Real-time data" refers to data that reflects the current situation and is collected every moment from vehicles and transportation infrastructure.

[0877] "Traffic conditions" refers to the overall situation, including road congestion, traffic volume, and speed information.

[0878] An "optimal route" is a route that allows for the most efficient and comfortable travel, taking into account the distance to the destination, travel time, and traffic conditions.

[0879] "Terminal" refers to a device operated by a user, such as a car system with navigation function or a smartphone.

[0880] "Emotional state" refers to the user's psychological emotional state, such as stress, relaxation, or impatience.

[0881] "Route guidance" refers to providing the user with guidance to their destination, and is done by display or voice.

[0882] An "emotion engine" refers to a system that uses facial recognition cameras and voice analysis technology to recognize a user's emotional state.

[0883] "Adjustment means" refers to methods and techniques for correcting or optimizing the optimal route based on collected data and forecast results.

[0884] "Server" refers to the central computer system that performs real-time data collection, pre-processing, analysis, route calculation and optimization.

[0885] The system of the present invention combines the functions of collecting real-time data and predicting traffic congestion with the functions of recognizing the user's emotional state and suggesting the optimal route. The program and its processing for realizing this system are described below.

[0886] 1. Real-time data collection

[0887] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records this data in a database.

[0888] 2. Data Preprocessing

[0889] The server removes outliers, standardizes data formats, and deletes duplicate data from the collected real-time data. This preprocessing ensures data quality and enables subsequent processing with high accuracy.

[0890] 3. Implementing traffic congestion prediction

[0891] The server runs a traffic congestion prediction algorithm based on the pre-processed data, analyzing actual vehicle speeds and location information to predict where and when future congestion will occur.

[0892] 4. Calculating the optimal route

[0893] The server searches a map database for multiple alternative routes based on the congestion forecast, calculates the distance and estimated arrival time for each route, and then derives the optimal route to the destination.

[0894] 5. Sending optimal route data

[0895] The server then sends the calculated optimal route information to each terminal, which can be a user's smartphone or a car navigation system.

[0896] 6. Route guidance

[0897] The device will then display or provide audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0898] 7. Use of Emotion Engines

[0899] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology, allowing it to determine whether the user is feeling stressed.

[0900] 8. Emotional Data Processing

[0901] The server evaluates the user's stress level based on the emotion data received from the emotion engine. Based on the evaluation result, for example, if the stress level is high, it will suggest a more relaxing route with a beautiful view.

[0902] Hardware and software used

[0903] This system is implemented using a server, terminals (smartphones and car navigation systems), facial recognition cameras, Geopy (a geographic calculation library), Emotion Recognition API, and more.

[0904] Specific examples

[0905] For example, suppose a user is traveling by car from Tokyo to Osaka. The server processes traffic data collected in real time and predicts congestion. The optimal route is calculated and sent to the user's smartphone. Furthermore, if the user's emotional state is recognized as stressed, the system will suggest a scenic route, providing the user with a relaxing driving experience.

[0906] Prompt Sentence Examples

[0907] "Calculate the optimal route from the user's current location (Tokyo) to their destination (Osaka). Also, analyze real-time traffic data and provide a route that avoids traffic jams. Also consider the user's emotional data and select a scenic route if the user's stress level is high."

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Step 1:

[0910] The server collects real-time data from vehicles and traffic infrastructure. This input includes GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data. This real-time data is received and recorded in the server's database.

[0911] Step 2:

[0912] The server preprocesses the collected real-time data. It analyzes the collected data as input, removes outliers, standardizes data formats, and removes duplicate data. This preprocessing ensures data quality and enables subsequent processing with high accuracy. The output is clean, consistent, and preprocessed data.

[0913] Step 3:

[0914] The server runs a traffic congestion prediction algorithm based on the preprocessed data. It receives the preprocessed data as input and analyzes current speed and location information. It performs data calculations to predict the location and time of future congestion. The output is congestion prediction data.

[0915] Step 4:

[0916] The server calculates the optimal route based on the congestion prediction results. Inputs include congestion prediction data and a map database. Alternative routes are searched for, and the distance and predicted arrival time of each route are calculated. Data calculations are used to select the most efficient route from multiple routes. The output is the optimal route data.

[0917] Step 5:

[0918] The server sends the calculated optimal route data to the terminal. It receives the optimal route data as input and transmits it to the terminal. The output is the optimal route information sent to the terminal.

[0919] Step 6:

[0920] The terminal displays or voices the route to the user based on the received optimal route data. It receives optimal route information as input and displays it on the display or outputs voice guidance. The output is route guidance provided to the user.

[0921] Step 7:

[0922] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology. It receives face and voice data as input and analyzes the emotional state. The output is the user's emotional data.

[0923] Step 8:

[0924] The server evaluates the user's stress level based on the emotion data received from the emotion engine. It receives emotion data as input and calculates the user's stress level. Based on this, it suggests a scenic route that will allow users with high stress levels to relax. The output is the adjusted optimal route information.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] [Fourth embodiment]

[0929] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0930] 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.

[0931] 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).

[0932] 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.

[0933] 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.

[0934] 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).

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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."

[0942] The system of the present invention collects real-time data and predicts traffic congestion to provide users with optimal routes. The basic configuration for implementing this system will be described in detail below.

[0943] 1. Real-time data collection

[0944] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[0945] 2. Data Preprocessing

[0946] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[0947] 3. Implementing traffic congestion prediction

[0948] The server then runs a traffic congestion prediction algorithm based on the pre-processed data, which analyzes current speed and location information to predict where and when future congestion will occur.

[0949] 4. Calculating the optimal route

[0950] The server calculates the optimal route based on the congestion prediction results, which involves searching for alternative routes in a map database and calculating the distance and estimated time for each route.

[0951] 5. Sending optimal route data

[0952] The server then transmits the calculated optimal route information to each terminal, which may be a car navigation system or a smartphone.

[0953] 6. Route guidance

[0954] The device displays or provides audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[0955] Specific examples of program processing

[0956] As a concrete example, consider a scenario during the morning commute.

[0957] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[0958] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[0959] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[0960] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[0961] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[0962] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[0963] 7. The user follows the navigation instructions to avoid major road A and take detour route B to avoid traffic jams.

[0964] This system can effectively avoid congestion in real time, improving fuel efficiency and reducing CO2 emissions.

[0965] The processing flow will be explained below.

[0966] Step 1:

[0967] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and automatically records this information in a database.

[0968] Step 2:

[0969] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[0970] Step 3:

[0971] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[0972] Step 4:

[0973] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0974] Step 5:

[0975] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[0976] Step 6:

[0977] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[0978] Step 7:

[0979] The user follows the guidance from the device and proceeds along the suggested optimal route. Specifically, the driver operates the steering wheel according to the navigation instructions and drives along the instructed route to avoid traffic jams.

[0980] Through this series of steps, the system predicts and avoids traffic congestion in real time and provides efficient route guidance, thereby improving fuel efficiency and reducing CO2 emissions.

[0981] Example 1

[0982] 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."

[0983] Conventional traffic guidance systems have the problem of being unable to accurately predict traffic congestion in real time, making it difficult to provide appropriate route guidance. Furthermore, there is a risk of incorrect predictions if outliers or duplicate data are included. Furthermore, delays in collecting data from terminals can make it difficult to provide timely guidance. To solve these issues, a traffic guidance system with excellent accuracy and immediacy is needed.

[0984] 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.

[0985] In this invention, the server includes a means for collecting real-time data, a means for removing outliers, standardizing the data format, and deleting duplicate data, and a means for predicting traffic conditions based on the collected real-time data, thereby enabling highly accurate traffic congestion predictions and rapid route guidance.

[0986] "Real-time data" refers to digital information such as GPS coordinates, speed information, and traffic sign recognition data that instantly reflects traffic conditions.

[0987] "Abnormal values" are numerical values ​​such as speed and location information that would not occur under normal traffic conditions, and are data that hinder analysis.

[0988] "Data unification" is the process of converting disparately collected data into a consistent format.

[0989] "Duplicate data" refers to data in which the same information is collected multiple times during the same time period, which can reduce the accuracy of analysis.

[0990] "Traffic condition prediction" is the result of analysis by an algorithm that predicts future traffic conditions based on current data.

[0991] An "optimal route" is a route that avoids predicted traffic congestion and estimates the shortest time to reach a destination.

[0992] A "terminal" is a device that provides route guidance to a user, such as a navigation system or a smartphone.

[0993] A "map database" is a database that contains geographical information and road network information and is used for route calculations.

[0994] The system of the present invention collects traffic data in real time, predicts congestion, and provides users with optimal routes. This system is composed of a server, terminals, and users.

[0995] First, the server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, etc. The server records this data in a database to understand detailed traffic conditions.

[0996] The server then preprocesses the collected data, which includes removing outliers, standardizing data formats, and deleting duplicate data, such as data with missing location information or extreme speed information.

[0997] Based on the pre-processed data, the server runs a traffic congestion prediction algorithm. This algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, if vehicle speed in a particular section suddenly drops, it determines that there is a high possibility of congestion occurring in that location.

[0998] Based on the results of the traffic congestion prediction, the server calculates the optimal route. In this process, it searches for multiple alternative routes from a map database and calculates the distance and estimated time for each route. This allows the user to reach their destination in the shortest time.

[0999] The calculated optimal route data is sent to a device with navigation functions (such as an automobile system or a smartphone). The device then uses this data to provide route guidance to the user. Specifically, the device displays the route on a screen and provides voice guidance such as "Turn right at the next intersection."

[1000] As an example of this system, consider a scenario during a morning commute. At 8:00 AM, the server receives GPS information, speed data, and surrounding traffic sign recognition data from the car. The server then preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data. After preprocessing is complete, the server runs a traffic congestion prediction algorithm to predict when congestion will occur on a specific road. To avoid congestion, the server searches for multiple alternative routes and calculates the distance and estimated arrival time for each. The calculated optimal route data is then sent to the car's navigation system, and the user can follow the new route guidance to avoid congestion.

[1001] An example of a prompt sentence is, "There is a possibility of traffic congestion on main road A. Please calculate the optimal route taking into account the state of fatigue on alternative route B."

[1002] The above is a specific embodiment for carrying out the present invention. This system allows users to effectively avoid traffic jams in real time, and is expected to improve fuel efficiency and reduce CO2 emissions.

[1003] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1004] Step 1:

[1005] Real-time data collection

[1006] The server collects real-time data from vehicles and traffic infrastructure. As input, it receives GPS coordinates, speed information, and traffic sign recognition data. These data are obtained from GPS devices, speed sensors, and traffic sign recognition cameras installed in each vehicle. The received data is recorded in a database. Specifically, the server receives vehicle location information and image data in real time, updated every second.

[1007] Output: The collected real-time data is stored in a database.

[1008] Step 2:

[1009] Data Preprocessing

[1010] The server preprocesses the collected data. It uses real-time collected data as input. Data preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. Specifically, the server detects and deletes abnormal speed data (e.g., 300 km / h), converts it into a unified format, and deletes duplicate data.

[1011] Output: Preprocessed data with outliers removed, formatted uniformly, and duplicates removed.

[1012] Step 3:

[1013] Implementing traffic congestion predictions

[1014] The server uses the preprocessed data to run a traffic congestion prediction algorithm. The preprocessed data is used as input. The algorithm analyzes current speed and location information to predict where and when future congestion will occur. Specifically, the server analyzes the speed data from the past hour, detects a consistent decrease in vehicle speed at a specific location, and predicts congestion at that location.

[1015] Output: Prediction data on the location and time of future congestion is obtained.

[1016] Step 4:

[1017] Optimal Route Calculation

[1018] The server calculates the optimal route based on the congestion prediction results. The predicted congestion data and map database are used as input. Calculating the optimal route involves searching for multiple alternative routes and calculating the distance and predicted time for each route. Specifically, the server searches for multiple routes that avoid major road A, where congestion is predicted, and calculates the arrival time for each.

[1019] Output: The optimal route data to reach the destination in the shortest time is obtained.

[1020] Step 5:

[1021] Sending optimal route data

[1022] The server sends the calculated optimal route data to the terminal. The optimal route data is used as input. The transmission is done in real time, so that the user can use it immediately. Specifically, the server sends the new route data to the terminal and notifies the user that "a new route has been found."

[1023] Output: The optimal route data is sent to the terminal.

[1024] Step 6:

[1025] Execute route guidance

[1026] The terminal provides route guidance to the user based on the received optimal route data. The optimal route data is used as input. The guidance includes display and voice guidance. Specifically, the terminal displays the new route on the display and provides voice guidance such as "Turn right at the next intersection."

[1027] Output: Visual and audio route guidance is provided to the user.

[1028] (Application example 1)

[1029] 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."

[1030] In food delivery, traffic congestion is a major obstacle to efficient delivery, which can delay delivery times and reduce customer satisfaction. Current systems have difficulty predicting traffic congestion in real time and providing optimal routes based on the results. A method is needed to solve this problem and improve delivery efficiency and customer satisfaction.

[1031] 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.

[1032] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction results, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, and means for optimizing routes for delivery work in real time and providing the route to delivery personnel, thereby making it possible to avoid traffic congestion during delivery work and perform deliveries efficiently.

[1033] "Real-time data" refers to data that can be obtained instantly from moving objects and transportation infrastructure, including information on their current status, location, speed, etc.

[1034] "Traffic conditions" refers to information that indicates the current traffic flow, whether there is congestion, speed limits, etc. on a particular road or in an area.

[1035] "Prediction results" are information obtained as a result of predicting future traffic conditions based on collected real-time data.

[1036] An "optimal route" is a route calculated to be the most efficient route between a departure point and a destination point that avoids traffic congestion.

[1037] A "terminal" is a device that has a navigation function and displays or provides audio guidance on optimal routes, examples of which include in-car navigation systems and smartphones.

[1038] "Delivery operations" refers to the general business process of transporting goods from a specific location to a customer, such as food delivery.

[1039] "Delivery person" refers to an individual or group engaged in food delivery work, etc., and whose role is to deliver goods to customers.

[1040] The embodiment of the present invention is a real-time traffic condition prediction and route guidance system that includes a server, a terminal, and a user who operates the terminal. Specifically, the present invention is implemented as a system including the following means.

[1041] 1. Real-time data collection

[1042] The server collects real-time data from mobile devices and transportation infrastructure devices. Specifically, it uses a smartphone as a terminal to receive current GPS coordinates, speed information, traffic sign recognition data, etc. from delivery vehicles. This is done using a cloud environment such as AWS EC2.

[1043] 2. Data Preprocessing

[1044] The server preprocesses the collected real-time data using Python scripts, which includes removing outliers, standardizing data formats, and removing duplicate data using the Pandas library.

[1045] 3. Implementing traffic congestion prediction

[1046] The server uses the preprocessed data to predict congestion using a TensorFlow model, which makes it possible to predict where and when future congestion will occur.

[1047] 4. Calculating the optimal route

[1048] The server calculates the optimal route using the Google Maps API, which finds alternative routes and calculates distance and estimated arrival time.

[1049] 5. Sending optimal route data

[1050] The server sends the calculated optimal route data in real time via a RESTful API to the delivery person's smartphone, which has an application developed using React Native installed on it.

[1051] 6. Route guidance

[1052] The device (smartphone) displays the received optimal route information and provides voice guidance using Siri on iOS or Google Assistant on Android.

[1053] Specific examples

[1054] For example, consider a scenario in which a delivery driver delivers catering during the morning rush hour. The server receives GPS and speed data sent from the delivery vehicle at 8:00 AM. After removing outliers and standardizing the data format, it uses a TensorFlow model to predict congestion and predicts that congestion will occur on main road A. To do this, the server uses the Google Maps API to calculate alternative route B and sends it to the delivery driver's smartphone. The app displays the new route data and provides a voice prompt saying, "Congestion is predicted on main road A. Please proceed to detour route B."

[1055] Prompt Sentence Examples

[1056] An example of a prompt sentence when using a generative AI model is as follows:

[1057] "Generate Python code that predicts the optimal route using given traffic data."

[1058] In this way, the system of the present invention enables food delivery personnel to efficiently avoid traffic congestion and quickly deliver products to customers, which is expected to improve business efficiency and customer satisfaction.

[1059] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1060] Step 1:

[1061] The server collects real-time data from smartphones in delivery vehicles and traffic infrastructure devices. Specifically, it receives GPS coordinates, speed information, traffic sign recognition data, etc. and records it in a database. The input here is real-time data from each device, and the output is raw data recorded on the server.

[1062] Step 2:

[1063] The server preprocesses the collected real-time data using a Python script to remove outliers, standardize data formats, and remove duplicate data to generate preprocessed data. The input here is the raw data recorded in step 1, and the output is the preprocessed data.

[1064] Step 3:

[1065] The server uses the preprocessed data to predict traffic congestion using a TensorFlow model. The preprocessed data is input into the model, which generates predictions about the location and time of future congestion. The input here is the preprocessed data, and the output is the prediction.

[1066] Step 4:

[1067] The server uses the Google Maps API to calculate the optimal route based on the traffic congestion forecast. The forecast results are passed to the API, which searches for alternative routes and calculates the distance and estimated arrival time. The input here is the forecast results and map information, and the output is the optimal route information.

[1068] Step 5:

[1069] The server sends the calculated optimal route data to the delivery person's smartphone in real time via a RESTful API. The input here is the optimal route information, and the output is the route data sent to the delivery person's device.

[1070] Step 6:

[1071] The device displays the received optimal route information to the user and provides voice guidance. Specifically, voice guidance is provided using Siri on iOS or Google Assistant on Android. The input here is the optimal route data sent from the server, and the output is route guidance information for the user.

[1072] In this way, the server collects and preprocesses real-time data, predicts traffic conditions, and calculates the optimal route, and the device provides route guidance to the user based on that information. This enables efficient route selection in delivery work and is expected to improve customer satisfaction.

[1073] 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.

[1074] The system of the present invention not only collects real-time data and predicts traffic congestion, but also combines it with an emotion engine that recognizes the user's emotions to provide route guidance tailored to individual needs. The basic configuration for implementing this system is described in detail below.

[1075] 1. Real-time data collection

[1076] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records it in a database.

[1077] 2. Data Preprocessing

[1078] The server pre-processes the collected real-time data, which includes removing outliers, standardizing data formats, and deleting duplicate data.

[1079] 3. Implementing traffic congestion prediction

[1080] The server runs a traffic congestion prediction algorithm on the pre-processed data, analyzing current speed and location information to predict where and when future congestion will occur.

[1081] 4. Calculating the optimal route

[1082] The server calculates the optimal route based on the congestion forecast, searches for alternative routes in a map database, and calculates the distance and estimated arrival time of each route.

[1083] 5. Sending optimal route data

[1084] The server then sends the calculated optimal route information to each terminal, which may be a car system with a navigation function or a smartphone.

[1085] 6. Route guidance

[1086] The device will then display or provide audio guidance to the user based on the optimal route data it receives, allowing the user to efficiently avoid traffic jams.

[1087] 7. Adding an Emotion Engine

[1088] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, allowing it to determine whether the user is feeling stressed.

[1089] 8. Emotional Data Processing

[1090] The server evaluates the user's stress level based on the received emotional data. Based on the evaluation results, for example, if the stress level is high, it can suggest a more relaxing route with good scenery, helping the user continue driving in a relaxed state.

[1091] Specific examples

[1092] Let us consider a specific scenario during the morning commute.

[1093] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[1094] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[1095] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[1096] 4. The server searches the map database for alternative routes B to avoid traffic jams and calculates the distance and estimated arrival time of each alternative route.

[1097] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[1098] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[1099] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[1100] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[1101] In this way, the present invention can predict traffic congestion in real time and provide optimal route guidance according to the user's individual emotional state, thereby achieving both efficient driving and user comfort at the same time.

[1102] The processing flow will be explained below.

[1103] Step 1:

[1104] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and receives and automatically records this information transmitted by vehicles in a database.

[1105] Step 2:

[1106] The server preprocesses the received real-time data. This preprocessing includes removing outliers (e.g., extremely high speed data), standardizing data formats (e.g., converting latitude and longitude information), and removing duplicate data. This preprocessing process generates a dataset suitable for analysis.

[1107] Step 3:

[1108] The server then runs a traffic congestion prediction algorithm based on the preprocessed data. This algorithm analyzes current vehicle speeds and locations to predict where congestion will occur in the future. Specifically, it uses machine learning models and statistical modeling to analyze traffic conditions several seconds in the future.

[1109] Step 4:

[1110] The server calculates the optimal route based on the congestion prediction results. This calculation involves searching for alternative routes from a map database and calculating the distance and estimated arrival time of each route. Specifically, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[1111] Step 5:

[1112] The server then sends the calculated optimal route data to each device, such as a car navigation system or a smartphone. To enable real-time data transmission, the optimal route information is sent via an API.

[1113] Step 6:

[1114] Based on the received optimal route data, the device displays or provides voice guidance to the user on the route. Specifically, the navigation system displays the new route on the display and provides voice guidance such as, "Congestion is predicted on main road A. Please proceed to detour route B."

[1115] Step 7:

[1116] The device uses facial recognition cameras and voice analysis technology to recognize the user's emotional state in real time, and can determine whether the user is feeling stressed by analyzing the user's facial expressions and tone of voice, for example.

[1117] Step 8:

[1118] The server evaluates the user's stress level based on the received emotional data. Specifically, if the user's stress level is high, the server prioritizes scenic routes that are relaxing.

[1119] Step 9:

[1120] The device receives the new optimal route again and notifies the user. The navigation system displays the new route on the display and asks the voice guidance, "A relaxing route has been found. Would you like to proceed with this route?"

[1121] Step 10:

[1122] The user follows the navigation system's instructions and follows the suggested route. Specifically, the driver follows the navigation system's instructions and operates the steering wheel to drive along the optimal route, reducing stress and providing a comfortable drive.

[1123] Through this series of steps, the system not only predicts and avoids traffic jams in real time, but also provides optimal route guidance according to the user's emotional state, thereby improving both travel efficiency and user comfort.

[1124] Example 2

[1125] 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."

[1126] Conventional traffic congestion prediction systems were able to collect real-time traffic data and provide congestion predictions and route guidance based on that data, but they were unable to provide route guidance that took into account the user's emotional state or stress level. As a result, they were unable to improve the user's mental comfort and lacked a means to reduce stress while driving.

[1127] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting real-time data from automobiles and traffic infrastructure, means for pre-processing the collected real-time data, means for predicting traffic congestion, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for collecting user emotion data through the terminal, means for evaluating the emotion data to determine the user's stress level, and means for suggesting a route according to the user's stress level. This enables optimal route guidance that takes the user's emotional state into consideration.

[1128] "Real-time data" refers to data that is immediately available based on time, such as the latest location information, speed information, and traffic sign recognition data obtained on the spot from vehicles and transportation infrastructure.

[1129] "Preprocessing" refers to processing collected real-time data, such as removing outliers, standardizing data formats, and deleting duplicate data, which can improve the quality of the data.

[1130] "Traffic congestion prediction" refers to predicting future traffic conditions based on preprocessed real-time data, particularly the locations and times when traffic flow will be slow.

[1131] The "optimal route" is the most efficient route calculated based on traffic congestion prediction results. It is a route that avoids predicted congestion and minimizes the distance and time to the destination.

[1132] "Terminal" refers to a device that receives information on optimal routes and provides route guidance to users. Specifically, this refers to an in-vehicle system or smartphone with a navigation function.

[1133] "Emotional Data" means data indicative of a user's emotional state, collected using facial recognition and / or voice analysis technologies, and used to determine a user's stress level or other emotional state.

[1134] "Evaluating emotional data" refers to analyzing collected emotional data to determine the user's stress level or other emotional state.

[1135] "Relaxing routes" are routes suggested based on the user's stress level. They refer to routes that enhance the user's mental comfort, such as scenic roads or routes that avoid crowds.

[1136] This invention is a system that collects real-time data from vehicles and transportation infrastructure to predict traffic congestion. Furthermore, the system can recognize the user's emotional state and provide route guidance according to their stress level.

[1137] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, and traffic sign recognition data, and records it in a database. This is done by collecting data via Apache Kafka and storing it in a database using AWS RDS.

[1138] The collected data is preprocessed using Apache Spark. This preprocessing includes removing outliers, standardizing data formats, and deleting duplicate data. The preprocessed data is then used to run a traffic congestion prediction algorithm. Specifically, a machine learning model is applied using Apache Hadoop and TensorFlow to predict the location and time of future congestion.

[1139] The server calculates the optimal route based on the congestion forecast results. This calculation uses Google Maps API and Graphhopper to calculate the distance and predicted arrival time of alternative routes. The calculated optimal route information is sent to each terminal via REST API. The terminals can be automobile systems with navigation functions or smartphones.

[1140] The device will display or voice directions to the user based on the received optimal route data, using the Android or iOS platform to integrate the Google Maps application.

[1141] Additionally, the device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. Specifically, it uses OpenCV and the Google Cloud Speech-to-Text API to determine whether the user is feeling stressed. The collected emotional data is sent to a server, where Google Cloud AI is used to evaluate the user's stress level.

[1142] Based on the evaluation results, if the user's stress level is high, the server will suggest a scenic route that is expected to have a relaxing effect, thereby helping the user continue driving in a relaxed state.

[1143] Specific examples

[1144] Let us consider a specific scenario during the morning commute.

[1145] 1. The server receives GPS information, speed data, and recognition data of surrounding traffic signs sent from each vehicle at 8:00 a.m.

[1146] 2. The server preprocesses the received data, removing outliers, standardizing data formats, and deleting duplicate data.

[1147] 3. The server runs a traffic congestion prediction algorithm based on the preprocessed data and predicts that congestion will occur on major road A in 3 seconds.

[1148] 4. The server searches for alternative routes B to avoid traffic jams using the Google Maps API and Graphhopper, and calculates the distance and estimated arrival time of each alternative route.

[1149] 5. The server sends the calculated optimal route data to each vehicle's navigation system in real time.

[1150] 6. The terminal (car navigation system) displays the received new route data on the display and provides voice guidance to the user saying, "Traffic congestion is predicted on main road A. Please proceed to detour route B."

[1151] 7. The device will use facial recognition cameras and voice analysis technology to recognize the user's emotional state and detect when the user is feeling stressed.

[1152] 8. Based on the emotional data, the server determines that the user's stress level is high and suggests a scenic route that is expected to have a relaxing effect.

[1153] Examples of prompts for generative AI models

[1154] For example, you can use the following prompt for a generative AI model:

[1155] Prompt: Please describe in detail the process of a system that combines real-time traffic data and emotion recognition to provide users with optimal route guidance and relaxing routes. Please also name the specific software and hardware.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1:

[1158] The server collects real-time data from vehicles and traffic infrastructure. Specifically, the inputs include GPS coordinates, speed information, and traffic sign recognition data. Apache Kafka is used to stream the data in real time and record it in an AWS RDS database. This process allows the server to obtain data that reflects the latest traffic conditions.

[1159] Step 2:

[1160] The server preprocesses the collected real-time data. The input for preprocessing is the data collected in step 1, and Apache Spark is used here. Specific preprocessing operations include removing outliers, standardizing data formats, and deleting duplicate data. This process outputs high-quality, analyzable data.

[1161] Step 3:

[1162] The server runs a traffic congestion prediction algorithm based on the preprocessed data. The preprocessed data is input, and a machine learning model is applied using Apache Hadoop and TensorFlow. The congestion prediction algorithm analyzes current speed and location information to predict the location and time of future congestion. The predicted location and time of congestion are output as the result.

[1163] Step 4:

[1164] The server calculates the optimal route based on the congestion forecast results. The input is the congestion forecast results and a map database, and route searches are performed using the Google Maps API and Graphhopper. The distance and predicted arrival time of each alternative route are calculated and the optimal route is selected. The output of this process is the optimal route data.

[1165] Step 5:

[1166] The server sends the calculated optimal route information to each terminal. The input is the optimal route data obtained in step 4, and uses the HTTP protocol via the REST API. The terminals are devices with navigation functions (in-vehicle systems or smartphones). As a result of the transmission, each terminal receives the optimal route data.

[1167] Step 6:

[1168] Based on the received optimal route data, the device displays the route or provides voice guidance to the user. The input is the optimal route data, and the Google Maps application is integrated using the Android or iOS platform. Specifically, the device displays the route on the display and provides voice guidance saying, "Congestion is predicted on main road A. Please proceed to detour route B." The output of this process is route guidance information for the user.

[1169] Step 7:

[1170] The device uses a facial recognition camera and voice analysis technology to recognize the user's emotional state in real time. The input is the user's facial expression data and voice data, which are analyzed using OpenCV and the Google Cloud Speech-to-Text API. The device determines whether the user is feeling stressed and sends that information to the server. The output of this process is the user's emotional data.

[1171] Step 8:

[1172] The server evaluates the user's stress level based on the received emotional data. The input is emotional data, which is analyzed using Google Cloud AI. If a high stress level is detected, a scenic route that is expected to have a relaxing effect is calculated, and new route guidance is sent to the device. The output of this process is optimal route guidance data based on the user's emotional state.

[1173] The above explains the specific operations, inputs, and outputs performed at each step. This system can predict traffic congestion and provide optimal route guidance according to the user's emotional state.

[1174] (Application example 2)

[1175] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1176] In order to provide effective route guidance for autonomous vehicles, it is necessary not only to avoid traffic congestion but also to realize a comfortable driving experience that takes into account the user's emotional state. However, conventional navigation systems only collect real-time traffic data and predict congestion, but do not provide route guidance that takes the user's emotional state into account. This poses a challenge: it is not possible to ensure user comfort while effectively avoiding congestion.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1178] In this invention, the server includes means for collecting real-time data, means for predicting traffic conditions based on the collected real-time data, means for calculating an optimal route based on the prediction result, means for transmitting the calculated optimal route to a terminal, means for displaying or providing audio route guidance on the terminal, means for recognizing the emotional state of a user, and means for adjusting the route based on the recognized emotional state. This makes it possible to grasp traffic conditions in real time and provide an optimal route according to the emotional state of the user.

[1179] "Real-time data" refers to data that reflects the current situation and is collected every moment from vehicles and transportation infrastructure.

[1180] "Traffic conditions" refers to the overall situation, including road congestion, traffic volume, and speed information.

[1181] An "optimal route" is a route that allows for the most efficient and comfortable travel, taking into account the distance to the destination, travel time, and traffic conditions.

[1182] "Terminal" refers to a device operated by a user, such as a car system with navigation function or a smartphone.

[1183] "Emotional state" refers to the user's psychological emotional state, such as stress, relaxation, or impatience.

[1184] "Route guidance" refers to providing the user with guidance to their destination, and is done by display or voice.

[1185] An "emotion engine" refers to a system that uses facial recognition cameras and voice analysis technology to recognize a user's emotional state.

[1186] "Adjustment means" refers to methods and techniques for correcting or optimizing the optimal route based on collected data and forecast results.

[1187] "Server" refers to the central computer system that performs real-time data collection, pre-processing, analysis, route calculation and optimization.

[1188] The system of the present invention combines the functions of collecting real-time data and predicting traffic congestion with the functions of recognizing the user's emotional state and suggesting the optimal route. The program and its processing for realizing this system are described below.

[1189] 1. Real-time data collection

[1190] The server collects real-time data from vehicles and traffic infrastructure, including GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data, and records this data in a database.

[1191] 2. Data Preprocessing

[1192] The server removes outliers, standardizes data formats, and deletes duplicate data from the collected real-time data. This preprocessing ensures data quality and enables subsequent processing with high accuracy.

[1193] 3. Implementing traffic congestion prediction

[1194] The server runs a traffic congestion prediction algorithm based on the pre-processed data, analyzing actual vehicle speeds and location information to predict where and when future congestion will occur.

[1195] 4. Calculating the optimal route

[1196] The server searches a map database for multiple alternative routes based on the congestion forecast, calculates the distance and estimated arrival time for each route, and then derives the optimal route to the destination.

[1197] 5. Sending optimal route data

[1198] The server then sends the calculated optimal route information to each terminal, which can be a user's smartphone or a car navigation system.

[1199] 6. Route guidance

[1200] The device will then display or provide audio guidance on the route based on the received optimal route data, allowing the user to efficiently avoid traffic jams.

[1201] 7. Use of Emotion Engines

[1202] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology, allowing it to determine whether the user is feeling stressed.

[1203] 8. Emotional Data Processing

[1204] The server evaluates the user's stress level based on the emotion data received from the emotion engine. Based on the evaluation result, for example, if the stress level is high, it will suggest a more relaxing route with a beautiful view.

[1205] Hardware and software used

[1206] This system is implemented using a server, terminals (smartphones and car navigation systems), facial recognition cameras, Geopy (a geographic calculation library), Emotion Recognition API, and more.

[1207] Specific examples

[1208] For example, suppose a user is traveling by car from Tokyo to Osaka. The server processes traffic data collected in real time and predicts congestion. The optimal route is calculated and sent to the user's smartphone. Furthermore, if the user's emotional state is recognized as stressed, the system will suggest a scenic route, providing the user with a relaxing driving experience.

[1209] Prompt Sentence Examples

[1210] "Calculate the optimal route from the user's current location (Tokyo) to their destination (Osaka). Also, analyze real-time traffic data and provide a route that avoids traffic jams. Also consider the user's emotional data and select a scenic route if the user's stress level is high."

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] The server collects real-time data from vehicles and traffic infrastructure. This input includes GPS coordinates, speed information, traffic sign recognition data, and other traffic-related data. This real-time data is received and recorded in the server's database.

[1214] Step 2:

[1215] The server preprocesses the collected real-time data. It analyzes the collected data as input, removes outliers, standardizes data formats, and removes duplicate data. This preprocessing ensures data quality and enables subsequent processing with high accuracy. The output is clean, consistent, and preprocessed data.

[1216] Step 3:

[1217] The server runs a traffic congestion prediction algorithm based on the preprocessed data. It receives the preprocessed data as input and analyzes current speed and location information. It performs data calculations to predict the location and time of future congestion. The output is congestion prediction data.

[1218] Step 4:

[1219] The server calculates the optimal route based on the congestion prediction results. Inputs include congestion prediction data and a map database. Alternative routes are searched for, and the distance and predicted arrival time of each route are calculated. Data calculations are used to select the most efficient route from multiple routes. The output is the optimal route data.

[1220] Step 5:

[1221] The server sends the calculated optimal route data to the terminal. It receives the optimal route data as input and transmits it to the terminal. The output is the optimal route information sent to the terminal.

[1222] Step 6:

[1223] The terminal displays or voices the route to the user based on the received optimal route data. It receives optimal route information as input and displays it on the display or outputs voice guidance. The output is route guidance provided to the user.

[1224] Step 7:

[1225] The device utilizes an emotion engine to recognize the user's emotional state in real time using a facial recognition camera and voice analysis technology. It receives face and voice data as input and analyzes the emotional state. The output is the user's emotional data.

[1226] Step 8:

[1227] The server evaluates the user's stress level based on the emotion data received from the emotion engine. It receives emotion data as input and calculates the user's stress level. Based on this, it suggests a scenic route that will allow users with high stress levels to relax. The output is the adjusted optimal route information.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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.

[1234] 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).

[1235] 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.

[1236] 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."

[1237] 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.

[1238] 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).

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] The following is further disclosed regarding the above embodiment.

[1250] (Claim 1)

[1251] a means for collecting real-time data;

[1252] A means for predicting traffic conditions based on collected real-time data;

[1253] A means for calculating an optimal route based on the prediction result;

[1254] means for transmitting the calculated optimal route to the terminal;

[1255] A means for displaying or providing audio route guidance on the terminal;

[1256] A system including:

[1257] (Claim 2)

[1258] 10. The system of claim 1, wherein real-time data is collected from the terminal with ultra-low latency.

[1259] (Claim 3)

[1260] 10. The system of claim 1, wherein the system synchronizes with a map database when calculating the optimum route.

[1261] "Example 1"

[1262] (Claim 1)

[1263] a means for collecting real-time data;

[1264] A method to remove outliers, standardize data formats, and remove duplicate data;

[1265] A means for predicting traffic conditions based on collected real-time data;

[1266] A means for calculating an optimal route based on the prediction result;

[1267] means for transmitting the calculated optimal route to the terminal;

[1268] A means for displaying or providing audio route guidance on the terminal;

[1269] A system including:

[1270] (Claim 2)

[1271] 10. The system of claim 1, wherein real-time data is collected from the terminal with ultra-low latency.

[1272] (Claim 3)

[1273] 10. The system of claim 1, wherein the system synchronizes with a map database when calculating the optimum route.

[1274] "Application Example 1"

[1275] (Claim 1)

[1276] a means for collecting real-time data;

[1277] A means for predicting traffic conditions based on collected real-time data;

[1278] A means for calculating an optimal route based on the prediction result;

[1279] means for transmitting the calculated optimal route to the terminal;

[1280] A means for displaying or providing audio route guidance on the terminal;

[1281] A means for optimizing delivery routes in real time and providing them to delivery personnel;

[1282] A system including:

[1283] (Claim 2)

[1284] 10. The system of claim 1, wherein real-time data is collected from the terminal with ultra-low latency.

[1285] (Claim 3)

[1286] 10. The system of claim 1, wherein the system synchronizes with a map database when calculating the optimum route.

[1287] "Example 2: Combining Emotion Engines"

[1288] (Claim 1)

[1289] A means of collecting real-time data from vehicles and transportation infrastructure;

[1290] means for pre-processing the collected real-time data;

[1291] a means for predicting traffic congestion;

[1292] A means for calculating an optimal route based on the prediction result;

[1293] means for transmitting the calculated optimal route to the terminal;

[1294] A means for displaying or providing audio route guidance on the terminal;

[1295] A means for collecting user emotional data through the device;

[1296] a means for evaluating the emotional data to determine the user's stress level;

[1297] A means of suggesting routes based on the user's stress level;

[1298] A system including:

[1299] (Claim 2)

[1300] 10. The system of claim 1, wherein real-time data is collected from the terminal with ultra-low latency.

[1301] (Claim 3)

[1302] 10. The system of claim 1, wherein the system synchronizes with a map database when calculating the optimum route.

[1303] "Application example 2 when combining emotion engines"

[1304] (Claim 1)

[1305] a means for collecting real-time data;

[1306] A means for predicting traffic conditions based on collected real-time data;

[1307] A means for calculating an optimal route based on the prediction result;

[1308] means for transmitting the calculated optimal route to the terminal;

[1309] A means for displaying or providing audio route guidance on the terminal;

[1310] means for recognizing the emotional state of a user;

[1311] means for adjusting the path based on the perceived emotional state;

[1312] A system including:

[1313] (Claim 2)

[1314] 10. The system of claim 1, wherein real-time data is collected from the terminal with ultra-low latency.

[1315] (Claim 3)

[1316] 10. The system of claim 1, wherein the system synchronizes with a map database when calculating the optimum route. [Explanation of symbols]

[1317] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting real-time data; A means for predicting traffic conditions based on collected real-time data; A means for calculating an optimal route based on the prediction result; means for transmitting the calculated optimal route to the terminal; A means for displaying or providing audio route guidance on the terminal; A system including:

2. The system of claim 1 , which collects real-time data from terminals with ultra-low latency.

3. 2. The system of claim 1, wherein the optimum route calculation is synchronized with a map database.

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