System

Abstract

A system is provided.SOLUTION: A system including means for receiving a departure point, an arrival point, and a desired date and time input by a user, means for acquiring traffic data, a weather forecast, and event information from a predetermined data source, means for predicting a traffic volume using artificial intelligence based on the acquired data, means for calculating a congestion time period based on a prediction result, and calculating a recommended departure time and a recommended arrival time, and means for notifying the result to a terminal of the user.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] Highway traffic congestion is a major problem for many users, resulting in the risk of not arriving at destinations on time, reduced fuel efficiency, and increased stress. While many conventional traffic information systems provide real-time congestion information, they often lack forward-looking prediction capabilities. This makes it difficult for users to plan optimal departure and arrival times in advance, resulting in wasted waiting time and getting stuck in traffic jams. [Means for solving the problem]

[0005] The present invention relates to a system that obtains traffic data, weather forecasts, and event information from a specified data source based on the user's input of departure and arrival points, desired date, and time, and then uses artificial intelligence to predict traffic volume. Specifically, the system makes predictions based on the obtained data, calculates busy times, and calculates recommended departure and arrival times, and notifies the user of these to help them avoid congestion. Furthermore, the traffic data includes past data, current data, and weather condition data, and a machine learning algorithm is used as an artificial intelligence model to achieve more accurate traffic volume predictions.

[0006] "User" refers to an individual or corporation that uses this system.

[0007] "Starting point" refers to the point where the user begins their journey.

[0008] "Destination" refers to the point where the user intends to end their journey.

[0009] "Desired Date and Time" means the specific date and time at which the User desires to depart and arrive.

[0010] "Data source" refers to various databases, APIs, etc. that provide the necessary information.

[0011] "Traffic Data" refers to data containing information about traffic volume on roads.

[0012] "Weather forecast" refers to predictive information about future weather.

[0013] "Event information" refers to information about an event that occurs at a specific date and time.

[0014] "Artificial intelligence" refers to a computer system that uses machine learning and data analysis techniques to make predictions and perform analysis.

[0015] "Traffic volume" refers to the number of vehicles passing through a particular road section.

[0016] "Congested hours" refers to times when traffic volume on roads increases and it becomes difficult to pass through.

[0017] "Recommended Departure and Arrival Times" refers to the optimal departure and arrival times suggested to a User to avoid congestion.

[0018] "Notifying" refers to conveying information to a user.

[0019] A "machine learning algorithm" is a computational method for finding patterns in large amounts of data and making predictions and classifications. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times by having the user input their departure point, destination, and desired date and time. This system is built around the user's terminal and a server.

[0042] User Actions

[0043] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0044] Server Processing

[0045] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0046] This system preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. After processes such as data cleaning, missing value completion, and normalization, a dataset for efficient prediction is created.

[0047] The server then uses the preprocessed data to run an artificial intelligence model, which uses machine learning algorithms to accurately predict traffic volume based on past and current data. The resulting prediction is how much traffic there will be for a given date and time, including the hours when it is expected to be particularly busy.

[0048] The server then calculates the optimal departure and arrival times based on the peak times. This calculation aims to select the most efficient time for the user to avoid the crowds. For example, if a particular time period is predicted to be peak, the server will recommend departure and arrival times that avoid that time period.

[0049] Terminal display

[0050] The calculated results are sent from the server to the user's device, where the device displays the congestion forecast results to the user. The results include peak times and recommended departure and arrival times to avoid the crowds. The user can use this information to adjust their schedule and choose the optimal departure time.

[0051] Specific examples

[0052] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make a prediction.

[0053] For example, if the prediction result is "It will be particularly busy between 10:00 and 12:00," the server calculates that "Departure at 08:00 or after 13:00 is recommended." This is sent to the terminal and displayed to the user, who can then make plans based on the recommended time displayed.

[0054] The above is a specific embodiment of the present invention, in which the elements of the user, terminal, and server work together to provide a means for efficiently avoiding congestion.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user launches the application and enters the departure point, highway entrance, arrival point, highway exit, desired date and time.

[0058] Step 2:

[0059] The user checks the input information and clicks the registration button. The terminal temporarily stores the input data within the device and prepares to send it to the server.

[0060] Step 3:

[0061] The terminal transmits the data entered by the user to the server, including the departure point, arrival point, desired date and time.

[0062] Step 4:

[0063] The server receives the user's input information sent from the terminal, and the received data is first stored in a database.

[0064] Step 5:

[0065] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0066] Step 6:

[0067] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0068] Step 7:

[0069] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic volume can be expected at a particular time of day.

[0070] Step 8:

[0071] The server analyzes the predictions of the AI ​​model to identify peak times of day, and calculates optimal departure and arrival times based on the identified peak times of day.

[0072] Step 9:

[0073] The server generates the calculated recommended departure time and recommended arrival time results and transmits them to the user's terminal.

[0074] Step 10:

[0075] The terminal displays the prediction results received from the server to the user, including the busiest times and recommended departure and arrival times to avoid those times.

[0076] Step 11:

[0077] Based on the displayed information, the user can adjust his / her departure schedule, for example, by deciding to depart during a less busy time or to arrive later.

[0078] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user.

[0079] Example 1

[0080] 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."

[0081] Congestion on expressways is a major source of stress for road users, making it difficult to plan trips. Choosing an appropriate departure time to avoid congestion is particularly important when planning travel or business. However, conventional systems have had difficulty comprehensively predicting traffic conditions, weather, and event information, and suggesting optimal departure and arrival times. Therefore, an efficient system that can predict highway congestion and suggest optimal departure and arrival times to users is needed.

[0082] 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.

[0083] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for preprocessing the acquired data and inputting it into an artificial intelligence model, means for predicting traffic volume using artificial intelligence, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, and means for transmitting the calculated results to the user's terminal and displaying them, thereby enabling the user to receive accurate congestion forecast information and select optimal departure and arrival times.

[0084] "Means for receiving the user's input of departure point, arrival point, desired date and time" refers to the interface that receives the information the user inputs through an application or website.

[0085] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to programs or functions for collecting the necessary data from external data providers or APIs.

[0086] "Means for preprocessing acquired data and inputting it into an artificial intelligence model" refers to a system that converts collected information into a format that can be used by an AI model through processes such as cleaning, missing value completion, and normalization.

[0087] "Means for predicting traffic volume using artificial intelligence" refers to a function that uses machine learning algorithms or other AI models to predict future traffic volume.

[0088] "Means for calculating congested time periods based on the prediction results and calculating recommended departure and arrival times" refers to a program or algorithm that analyzes the traffic volume prediction results from AI and derives optimal departure and arrival times to avoid congestion.

[0089] "Means for sending the calculated results to the user's device and displaying the results" refers to an interface that transfers the prediction results from the server to the user's device (smartphone or computer) and displays them in a format that the user can check.

[0090] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times when a user inputs their departure point, arrival point, and desired date and time. This system is built around the user's terminal and a server.

[0091] User Actions

[0092] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0093] Server Processing

[0094] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0095] Specifically, we collect information from the following data sources:

[0096] 1. Traffic Data: Certain traffic APIs (e.g., Google Maps API)

[0097] 2. Weather forecast: Japan Meteorological Agency API

[0098] 3. Event Information: Regional Event Calendar API

[0099] Data Preprocessing

[0100] The collected data undergoes the following preprocessing:

[0101] Cleaning: Removing invalid data and noise.

[0102] Imputing missing values: Filling incomplete data with guesses or other data.

[0103] Normalization: Aligning the scale of the data.

[0104] This puts the data in a format suitable for AI models.

[0105] Prediction using artificial intelligence models

[0106] The server then runs an AI model based on the preprocessed data, which uses a machine learning algorithm (e.g., a regression model) created using Python's Scikit-learn library, to predict future traffic volumes.

[0107] Calculating optimal departure and arrival times

[0108] The server calculates the optimal departure and arrival times based on the predictions made by the AI ​​model. For example, if the AI ​​model predicts that congestion will occur between 10:00 and 12:00, the server will recommend departing at 08:00 or after 13:00 to avoid that time period.

[0109] Terminal display

[0110] The calculation results are sent from the server to the user's device, which then displays the results received from the server to the user. These results include peak times and recommended departure and arrival times to avoid these peak times. The user can use this information to adjust their schedule.

[0111] Specific examples

[0112] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, and weather forecasts, and uses the AI ​​model to make a prediction.

[0113] If the prediction result is "particularly busy between 10:00 and 12:00," the server will recommend "departing at 08:00 or after 13:00." The calculated information is sent to the terminal, and the user is shown a message saying "recommended departure at 08:00 or after 13:00." The user can use this information to choose the optimal departure time.

[0114] Prompt Sentence Examples

[0115] Specific prompt examples:

[0116] "A user wants to travel from Tokyo IC to Nagoya Nishi IC. The departure date and time is December 24, 2023 at 9:00 AM. Please suggest the best departure time for this user based on past Christmas Eve traffic data, current traffic conditions, and weather forecasts."

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] The user launches the application on the terminal and inputs the departure point, arrival point, desired date and time.

[0120] Input: User-entered departure point, arrival point, desired date and time information

[0121] Output: Trip data entered into the terminal

[0122] Step 2:

[0123] Once the user confirms the information and clicks the registration button, the terminal sends the input data to the server.

[0124] Input: Travel data entered into the terminal

[0125] Output: Trip data transferred to the server

[0126] Step 3:

[0127] The server receives the user's input information sent from the terminal and stores it in a database.

[0128] Input: Travel data sent from the device

[0129] Output: Trip data stored in a database

[0130] Step 4:

[0131] The server then collects traffic data, weather forecasts, and event information from predetermined data sources.

[0132] Input: API request for a given data source

[0133] Output: Traffic data, weather forecast, and event information collected on the server

[0134] Step 5:

[0135] The server performs preprocessing on the collected data, which includes cleaning the data, imputing missing values, and normalizing it.

[0136] Input: Raw data collected

[0137] Output: Preprocessed data

[0138] Step 6:

[0139] The server runs an artificial intelligence model based on the preprocessed data to predict traffic volume.

[0140] Input: Preprocessed data

[0141] Output: Predicted traffic volume data

[0142] Step 7:

[0143] The server calculates the busy times based on the prediction results and calculates the recommended departure time and recommended arrival time.

[0144] Input: Predicted traffic data

[0145] Output: Calculated optimal departure and arrival times

[0146] Step 8:

[0147] The server sends the calculation results to the user's terminal.

[0148] Input: Calculated optimal departure and arrival times

[0149] Output: The calculation result sent to the user's device

[0150] Step 9:

[0151] The terminal displays the results received from the server to the user, who can then view the forecast information and suggested departure and arrival times.

[0152] Input: Calculation result sent from the server

[0153] Output: Displayed forecast information and recommended departure and arrival times

[0154] (Application example 1)

[0155] 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."

[0156] Conventional congestion prediction systems mainly rely on users checking traffic information before departure and planning departure times. However, autonomous vehicles are required to optimize routes in real time and avoid congestion. Furthermore, they must respond quickly to abnormal weather or sudden events. There is a problem with the lack of real-time congestion prediction and route suggestions for optimizing operations in autonomous vehicles.

[0157] 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.

[0158] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure times and recommended arrival times, and means for notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time, thereby enabling the autonomous vehicle to plan the optimal departure time and route that reflects traffic conditions in real time and reach the destination quickly and efficiently.

[0159] "User-entered departure point, arrival point, desired date and time" refers to the information in the system about the user's departure point, arrival point, and the specific date and time of desired travel.

[0160] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to the functionality of a system, device, or software for obtaining current and historical traffic conditions, weather forecasts, and information about major events in a specific area from different data sources.

[0161] "Means for predicting traffic volume using artificial intelligence based on acquired data" refers to the function of analyzing collected traffic data, weather forecasts, and event information, and using artificial intelligence and machine learning algorithms to predict future traffic volume with high accuracy.

[0162] "Means for calculating busy times based on the prediction results and calculating recommended departure and arrival times" refers to a function that identifies which times are expected to be crowded based on prediction data generated by an artificial intelligence model and determines the optimal departure and arrival times.

[0163] "Means of notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time" refers to the function of sending the calculated congestion forecast results and recommended departure and arrival times to the onboard system of the autonomous vehicle and using that information to optimize the route in real time.

[0164] The present invention is a highway congestion prediction system for an autonomous vehicle. The system includes: means for receiving a departure point, arrival point, and desired date and time input by a user; means for acquiring traffic data, weather forecasts, and event information from predetermined data sources; means for predicting traffic volume based on the acquired data using artificial intelligence; means for calculating congestion times based on the prediction results and calculating recommended departure and arrival times; and means for notifying the autonomous vehicle's onboard system of the results and optimizing the route in real time.

[0165] System Program

[0166] The main components of the system are the user terminal, the server, and the autonomous vehicle's onboard system. The user terminal provides an interface for inputting the departure point, arrival point, desired date and time. The server collects and processes traffic data, weather forecasts, and event information, and uses an artificial intelligence model to predict congestion. The autonomous vehicle's onboard system receives the results sent from the server and optimizes the route in real time.

[0167] Processing Details

[0168] Hardware and Software

[0169] Hardware: On-board computer systems for autonomous vehicles

[0170] Software: Python, requests library, scikit-learn (to run machine learning models)

[0171] Data processing and calculation

[0172] 1. Data collection: The server receives the departure point, arrival point, desired date and time from the user's device, and then obtains traffic data, weather forecasts, and event information through API requests.

[0173] 2. Data preprocessing: Cleaning the collected data and converting it into a format suitable for artificial intelligence models, including imputing missing values ​​and normalizing the data.

[0174] 3. Artificial intelligence prediction: Based on preprocessed data, congestion predictions are made using a machine learning model using scikit-learn.

[0175] 4. Optimization: Based on the prediction results, the optimal departure and arrival times are calculated to avoid peak times. The results are sent to the autonomous vehicle's onboard system.

[0176] 5. Real-time notification and route optimization: Based on the information received, the onboard system will optimize routes and departure times in real time and display the results on the vehicle's display, allowing passengers to adjust their travel plans accordingly.

[0177] Specific examples

[0178] For example, if a user wishes to travel from "City Departure Point A" to "City Destination Point B," they enter the date and time as "December 24, 2023, 9:00 AM." The user's device sends this information to the server, which collects past data, current traffic conditions, and weather forecasts and uses an AI model to make a prediction. If the prediction indicates that "the route will be particularly congested between 10:00 AM and 12:00 PM," the server calculates that "it is recommended to depart at 8:00 AM or after 1:00 PM." This information is sent to the autonomous vehicle's onboard system, which notifies and optimizes the route in real time.

[0179] Prompt Sentence Examples

[0180] Use traffic data to suggest optimal departure times from the following origins to destinations:

[0181] Starting point: City starting point A

[0182] Arrival point: City arrival point B

[0183] Scheduled departure time: December 24, 2023, 9:00 AM

[0184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0185] Step 1:

[0186] The user inputs the departure point, arrival point, desired date and time. This information is entered into an application on the device (smartphone, tablet, PC, etc.) used by the user. This input information includes the specific planned departure location, planned arrival location, and desired departure date and time. Once the input is complete, the user clicks the "Submit" button, which causes the device to send this information to the server.

[0187] Step 2:

[0188] The server receives the user's input information sent from the device and stores the information of the departure point, arrival point, desired date and time in a database.The server then sends an API request to collect traffic data, weather forecasts, and event information from predetermined data sources.This data is obtained from different data sources (traffic information API, weather information API, event information API, etc.).

[0189] Step 3:

[0190] The server preprocesses the collected traffic data, weather forecasts, and event information. Specifically, it performs data cleaning (noise removal and missing value imputation), normalization (standardizing the scale of the data), and feature engineering (generating new useful features). After preprocessing, the data is converted into a format suitable for artificial intelligence models. The input of this step is the collected raw data, and the output is preprocessed data.

[0191] Step 4:

[0192] The server uses the preprocessed data to predict traffic volume using an artificial intelligence model with machine learning libraries such as scikit-learn. In this prediction process, past traffic data, weather data, and event information are given as input, and future traffic volume is estimated based on this. The model is pre-trained and capable of making predictions in real time. The input for this step is the preprocessed data, and the output is the congestion prediction results.

[0193] Step 5:

[0194] Based on the congestion prediction results, the server identifies which time periods are expected to be congested. It then calculates the optimal departure and arrival times to avoid congestion. For example, if congestion is predicted for a specific time period, it will suggest departure and arrival times that avoid that time period. The input to this step is the congestion prediction results, and the output is recommended departure and arrival times.

[0195] Step 6:

[0196] The server sends the calculated results to the user's device and the autonomous vehicle's onboard system. The user's device displays the recommended departure and arrival times, and the autonomous vehicle's onboard system optimizes the route in real time. The input of this step is the recommended departure and arrival times and route information, and the output is notifications to the user's device and the onboard system.

[0197] Step 7:

[0198] The onboard system of the autonomous vehicle optimizes the route in real time based on the recommended time and route information received from the server. Passengers can check the optimized route information on the vehicle display and depart at the optimal time. The input of this step is the notification from the server, and the output is the optimized route information.

[0199] 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.

[0200] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[0201] User Actions

[0202] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input information and clicks the registration button, the terminal sends this data to the server.

[0203] Server Processing

[0204] The server receives the user's input information sent from the device, stores this information in a database, and then collects traffic data, weather forecasts, and event information from designated data sources, thereby obtaining the data necessary for prediction.

[0205] The server preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. It cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0206] The server then uses the preprocessed data to run an artificial intelligence model to predict traffic volume. The AI ​​model calculates how much traffic is likely to occur at a particular time based on past and current data. The resulting prediction is a forecast of congestion during that time.

[0207] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[0208] Emotion engine processing

[0209] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[0210] Terminal display

[0211] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[0212] Specific examples

[0213] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make predictions.

[0214] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[0215] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The user launches the application and enters their starting point, highway entrance, arrival point, highway exit, desired date and time. If the emotion engine is enabled, voice input and a facial recognition camera capture the user's state.

[0219] Step 2:

[0220] The user confirms the input and clicks the registration button. The device temporarily stores the input data and prepares to send it to the server. Emotion data is also collected at the same time.

[0221] Step 3:

[0222] The device transmits the data and emotion data entered by the user to the server, including the departure point, arrival point, desired date and time, voice data, and image data.

[0223] Step 4:

[0224] The server receives the user's input information sent from the device and stores it in a database. Voice data and image data are also stored for analysis by the emotion engine.

[0225] Step 5:

[0226] The emotion engine analyzes the stored voice and image data to estimate the user's emotions. For example, it can use voice analysis to determine the user's stress level and emotional state.

[0227] Step 6:

[0228] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0229] Step 7:

[0230] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0231] Step 8:

[0232] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic is likely to occur at a particular time.

[0233] Step 9:

[0234] The server analyzes the predictions of the AI ​​model to identify peak times. It also calculates optimal departure and arrival times, taking into account the user's emotional state. For example, it suggests recommended times for a stressed user to relax.

[0235] Step 10:

[0236] The server generates the calculated recommended departure and arrival times and sends them to the user's device, along with advice based on additional emotion data.

[0237] Step 11:

[0238] The device displays prediction results and emotion-based advice received from the server to the user, including peak times, recommended departure and arrival times, and emotion-based relaxation techniques.

[0239] Step 12:

[0240] The user can then adjust their departure plans based on the displayed information, decide whether to leave earlier or later based on recommended times to avoid crowds, and, if necessary, take emotional advice and incorporate relaxation techniques.

[0241] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user, as well as personalized advice based on their emotions.

[0242] Example 2

[0243] 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."

[0244] Current traffic prediction systems can suggest optimal departure and arrival times based on the user's input of departure and arrival locations and desired date and time, but they lack the ability to provide personalized advice that takes into account the user's individual feelings. Furthermore, data collection and pre-processing to improve traffic volume prediction accuracy are often insufficient. This can lead to lower user satisfaction and negatively impact the actual travel experience.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0246] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather information, and event information from a predetermined data source, means for predicting traffic volume using a generative AI model based on the acquired data, means for calculating busy periods based on the prediction results and for calculating recommended departure and arrival times, means for analyzing the user's emotions, and means for notifying the user's terminal of the results of the analysis. This makes it possible to improve the accuracy of traffic predictions and provide personalized advice based on the user's emotions.

[0247] "User" means a person who uses the system to input the departure point, arrival point, desired date and time, and receives suggestions for optimal departure and arrival times.

[0248] The "starting point" is the location where the user begins their journey.

[0249] The "destination point" is the location where the user ends their journey.

[0250] A "desired date and time" is a specific date and time that a user desires to depart or arrive.

[0251] "Terminal" means a device used by a User to access the System and to enter and receive information.

[0252] A "server" is a computer system that receives, processes, and analyzes data sent from a user's terminal.

[0253] A "data source" is an information source that provides data such as traffic data, weather information, and event information.

[0254] "Traffic Data" means information about past and current traffic conditions.

[0255] "Weather information" refers to information about weather forecasts and weather conditions.

[0256] "Event information" is information about an event that occurs at a specific place and time.

[0257] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to predict traffic volume.

[0258] "Traffic volume forecasting" refers to predicting the degree of traffic congestion during a specific time period.

[0259] The "emotion engine" is a system that recognizes emotions by analyzing the user's voice data and facial expressions.

[0260] "Means of notification" refers to the method by which a server or terminal displays or conveys information to a user.

[0261] The "recommended departure time and recommended arrival time" are the optimal departure and arrival times suggested to avoid congestion.

[0262] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[0263] System configuration

[0264] This system consists of a terminal used by the user, a server that processes data, various data sources, and an emotion engine.

[0265] Hardware and software used

[0266] Device: A device that allows a user to input information. Examples include smartphones, tablets, and computers.

[0267] Server: A computer system that receives data and performs predictions and analysis. Example: High performance server (including cloud servers).

[0268] Database: A system for storing and managing data. Examples: MySQL®, PostgreSQL.

[0269] Generative AI model: Artificial intelligence for traffic volume prediction. Examples: Machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch.

[0270] Emotion engine: A system that analyzes a user's emotions. Examples: speech recognition engines and facial expression recognition software.

[0271] System Operation

[0272] User Action:

[0273] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input and clicks the "Register" button, the terminal sends this data to the server.

[0274] Server Action:

[0275] The server receives user input information sent from the device. It stores this information in a database. It then collects traffic data, weather information, and event information from specified data sources. This allows it to obtain the data necessary for prediction. The server preprocesses the collected data and converts it into a format that can be input into the generative AI model. It then cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0276] The server then uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model uses past and current data to calculate how much traffic is expected at a particular time. The resulting prediction is a forecast of congestion at that time.

[0277] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[0278] Emotion Engine Processing:

[0279] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[0280] Terminal display:

[0281] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[0282] Specific examples

[0283] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather information, and more, and uses the generative AI model to make predictions.

[0284] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[0285] Examples of prompt statements

[0286] Enter the following prompt into the generative AI model:

[0287] The user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" on the date and time "December 24, 2023, 9:00 AM." What is the expected traffic congestion and what are the best departure and arrival times? Also, if the user feels "very stressed," what are some appropriate ways to relax?

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] Step 1:

[0290] Users launch the application and input their departure point, highway entrance, arrival point, highway exit, and desired date and time. Examples of input information include "Tokyo IC," "Nagoya Nishi IC," and "December 24, 2023, 9:00 AM."

[0291] Input: departure point, highway entrance, arrival point, highway exit, desired date and time.

[0292] Output: Input starting point, highway entrance, arrival point, highway exit, desired date and time.

[0293] Step 2:

[0294] When the user confirms the input and clicks the "Register" button, the device sends this data to the server. The device stores the input data in its internal memory and uses an API to send the data to the server.

[0295] Input: Data entered and confirmed by the user.

[0296] Output: The data sent to the server.

[0297] Step 3:

[0298] The server receives the user's input information sent from the terminal and stores it in a database, for example, using a database system such as MySQL.

[0299] Input: User input data sent from the terminal.

[0300] Output: User data stored in the database.

[0301] Step 4:

[0302] The server collects traffic data, weather information, and event information from predetermined data sources, including historical traffic data, current traffic conditions, weather forecasts, and event information. The collection process obtains real-time data via API.

[0303] Input: User information stored in the database.

[0304] Output: Collected traffic data, weather information, and event information.

[0305] Step 5:

[0306] The server performs preprocessing on the collected data, cleaning the data, filling in missing values, normalizing the data, and creating a dataset for efficient prediction. Specifically, it uses Python libraries (Pandas, NumPy, etc.) to format and fill in the data.

[0307] Input: Collected traffic data, weather information, and event information.

[0308] Output: A preprocessed dataset.

[0309] Step 6:

[0310] The server uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model calculates the expected traffic volume for a specific time period based on past and current data. The generative AI model uses TensorFlow and PyTorch.

[0311] Input: The preprocessed dataset.

[0312] Output: Traffic volume prediction results.

[0313] Step 7:

[0314] The server analyzes the prediction results obtained by the AI ​​model to identify busy times. It also calculates the optimal departure and arrival times to avoid congestion. For example, if the prediction results indicate that "it will be particularly busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00."

[0315] Input: Traffic prediction results.

[0316] Output: Busy times, best departure and arrival times.

[0317] Step 8:

[0318] When a user expresses emotions through voice input or facial expression recognition, the emotion engine analyzes the data. The device collects the user's voice data and sends it to the server. The emotion engine in the server analyzes the voice and facial expression data to infer the user's emotional state.

[0319] Input: User's voice data and facial expression data.

[0320] Output: Parsed emotional state.

[0321] Step 9:

[0322] The server then sends the calculated recommended departure and arrival times, as well as additional advice based on the user's emotions, to the user's device, which then displays these results to the user. Specifically, the results include customized advice such as peak times, recommended departure times (e.g., "8:00 AM departure or after 1:00 PM recommended"), and even the provision of relaxation music.

[0323] Inputs: Recommended departure time, arrival time, and sentiment-based advice.

[0324] Output: The results that are displayed to the user.

[0325] (Application example 2)

[0326] 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."

[0327] Conventional traffic prediction systems only provide prediction results based on traffic volume data and are unable to make suggestions that take into account the user's emotional state. This can result in users departing or arriving at times that make them feel stressed. Furthermore, providing only recommended travel times poses the problem of not being able to address the individual circumstances of each user. To solve this problem, a system that takes into account the user's emotional data and makes more personalized suggestions is needed.

[0328] 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.

[0329] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, means for notifying the user of the results to their terminal, means for collecting and analyzing emotional data from the user using a camera or voice input device in the vehicle, and means for providing personalized recommendations based on the analyzed emotional data. This reduces stress for the user and makes it possible to suggest optimal departure and arrival times according to the individual emotional state.

[0330] The "starting point" is where the user begins their journey.

[0331] "Destination" is the location where the user intends to end their journey.

[0332] "Desired date and time" means the specific date and time at which the user wishes to begin their journey.

[0333] "Data Source" means a source of information that provides traffic data, weather forecasts, and event information.

[0334] "Traffic data" refers to information about traffic flow and congestion at a given point in time.

[0335] A "weather forecast" is information that predicts future weather conditions.

[0336] "Event information" refers to information about events held in a specific area or on a specific date.

[0337] "Artificial intelligence" refers to computer programs or systems that simulate intelligent human behavior.

[0338] A "busy hour" is a specific time range during which traffic is expected to be very heavy.

[0339] "Recommended Departure Time" means the optimal departure time suggested to the user to avoid congestion.

[0340] "Recommended Arrival Time" refers to the optimal arrival time suggested to the user to avoid congestion.

[0341] "Emotional Data" refers to information about a user's emotional state.

[0342] A "camera" is a device that captures images and records or transmits the data.

[0343] A "voice input device" is a device that has the function of acquiring and analyzing voice data.

[0344] "Means of analysis" refers to the processes and systems that evaluate acquired data and extract meaningful information.

[0345] "Personalized suggestions" are suggestions that are customized based on the user's individual situation and emotional state.

[0346] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times based on the departure point, arrival point, and desired date and time entered by the user. It also collects and analyzes user emotion data to provide personalized advice.

[0347] System Overview

[0348] The system mainly consists of a user device and a server. Users can use their smartphone or in-car information system to input their departure and arrival points, desired date and time. This data is sent to the server, which then retrieves traffic data, weather forecasts, and event information from designated data sources.

[0349] Hardware and software used

[0350] Cameras and audio input devices: Used to collect user emotional data, such as in-car cameras and microphones.

[0351] EmotionEngine (tentative library name): Software that analyzes collected emotional data.

[0352] VehicleAPI (tentative API name): Software for controlling the navigation system of self-driving vehicles.

[0353] requests library: A software library used to communicate with the traffic congestion prediction server.

[0354] Details of data processing and calculation

[0355] The server receives the user's input of departure and arrival points, desired date and time, and based on this data, retrieves traffic data, weather forecasts, and event information from predetermined data sources.

[0356] The server then analyzes the data and uses an artificial intelligence model to predict traffic volume. Specifically, it predicts traffic volume for specific time periods based on past and current data. It then calculates peak times and calculates optimal departure and arrival times. This information is then sent to the user's device.

[0357] Use of user sentiment data

[0358] Additionally, the system uses in-car cameras and audio input devices to analyze the user's emotional state. EmotionEngine analyzes the user's voice and facial expressions to extract emotional data. Based on the results, recommended departure and arrival times are personalized. For example, if the user indicates high stress, the system will suggest playing relaxing music in the car.

[0359] Examples and prompts

[0360] As a specific example, suppose a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" and enters the date and time as "December 24, 2023, 9:00 AM." When the user registers this information, the server collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and other information, and uses the AI ​​model to make a prediction. If the prediction indicates that traffic will be particularly congested between 10:00 AM and 12:00 PM, the system calculates that "departing at 8:00 AM or after 1:00 PM is recommended." This information is notified and displayed on the user's device. Furthermore, if the user verbally says, "I'm feeling very stressed," the emotion engine determines that the user is feeling stressed and suggests relaxing background music and stress management measures.

[0361] An example prompt for a generative AI model might look something like this:

[0362] The user wishes to depart from Tokyo IC to Nagoya Nishi IC at 09:00 on December 24, 2023. Based on this information, calculate the optimal departure and arrival times using historical traffic data, current traffic conditions, and weather forecasts. Also, if the user's voice and facial expression data indicate that they are feeling stressed, suggest playing relaxing music.

[0363] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[0364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0365] Step 1:

[0366] The user inputs the departure point, arrival point, desired date and time into the terminal. The terminal collects this data and sends it to the server when the registration button is pressed.

[0367] Input: Departure point, arrival point, desired date and time

[0368] Output: Sends input data to the server

[0369] How it works: The user uses a smartphone or in-car infotainment system to enter, confirm, and submit data.

[0370] Step 2:

[0371] The server receives user data sent from the terminal and makes requests to obtain traffic data, weather forecasts, and event information from predetermined data sources.

[0372] Input: User-entered departure point, arrival point, desired date and time

[0373] Output: Traffic data, weather forecast, event information

[0374] How it works: The server calls the appropriate APIs to collect the necessary traffic data, weather forecasts and event information.

[0375] Step 3:

[0376] The server preprocesses the acquired data and converts it into a format that can be input into an artificial intelligence model. It cleans the traffic data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0377] Input: traffic data, weather forecast, event information

[0378] Output: Preprocessed dataset

[0379] What it does: Performs preprocessing steps such as cleaning data, imputing missing values, and normalizing it to convert it into a data format suitable for artificial intelligence models.

[0380] Step 4:

[0381] The server uses the preprocessed data to run an artificial intelligence model to predict traffic volume, calculating traffic volume for a specific time period based on past and current data.

[0382] Input: Preprocessed dataset

[0383] Output: Traffic volume prediction results

[0384] How it works: Traffic volume predictions are made using artificial intelligence models (machine learning algorithms).

[0385] Step 5:

[0386] Based on the traffic volume prediction results, the server identifies busy times and calculates recommended departure and arrival times, thereby generating an optimal travel plan.

[0387] Input: Traffic volume prediction results

[0388] Output: Recommended departure and arrival times

[0389] Operation: Analyzes busy times and calculates recommended times.

[0390] Step 6:

[0391] To collect user emotion data, the system uses cameras and voice input devices inside the car. The emotion engine analyzes the user's emotions and sends the data to a server.

[0392] Input: User's voice data, video data

[0393] Output: Emotion data as the analysis result

[0394] How it works: The device uses a camera and microphone to collect audio and video and perform sentiment analysis.

[0395] Step 7:

[0396] The server analyzes the emotional data and personalizes recommendations based on the user's emotional state, for example, suggesting playing relaxing music in the car if the user indicates high stress.

[0397] Input: Emotion data, recommended departure time and recommended arrival time

[0398] Output: Personalized recommendations

[0399] Behavior: Customize recommended time and additional advice based on sentiment data.

[0400] Step 8:

[0401] The server then sends the final recommendations and personalized advice to the user's device for display, allowing them to see suggestions tailored to their emotional state.

[0402] Input: Personalized recommendations

[0403] Output: Displayed recommended departure and arrival times, sentiment-based advice

[0404] Operation: The terminal displays and notifies the user of the results received from the server.

[0405] In this way, the present invention not only allows the user to know the best departure and arrival times, but also provides personalized advice according to their emotional state.

[0406] 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.

[0407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] 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.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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."

[0422] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times by having the user input their departure point, destination, and desired date and time. This system is built around the user's terminal and a server.

[0423] User Actions

[0424] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0425] Server Processing

[0426] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0427] This system preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. After processes such as data cleaning, missing value completion, and normalization, a dataset for efficient prediction is created.

[0428] The server then uses the preprocessed data to run an artificial intelligence model, which uses machine learning algorithms to accurately predict traffic volume based on past and current data. The resulting prediction is how much traffic there will be for a given date and time, including the hours when it is expected to be particularly busy.

[0429] The server then calculates the optimal departure and arrival times based on the peak times. This calculation aims to select the most efficient time for the user to avoid the crowds. For example, if a particular time period is predicted to be peak, the server will recommend departure and arrival times that avoid that time period.

[0430] Terminal display

[0431] The calculated results are sent from the server to the user's device, where the device displays the congestion forecast results to the user. The results include peak times and recommended departure and arrival times to avoid the crowds. The user can use this information to adjust their schedule and choose the optimal departure time.

[0432] Specific examples

[0433] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make a prediction.

[0434] For example, if the prediction result is "It will be particularly busy between 10:00 and 12:00," the server calculates that "Departure at 08:00 or after 13:00 is recommended." This is sent to the terminal and displayed to the user, who can then make plans based on the recommended time displayed.

[0435] The above is a specific embodiment of the present invention, in which the elements of the user, terminal, and server work together to provide a means for efficiently avoiding congestion.

[0436] The processing flow will be explained below.

[0437] Step 1:

[0438] The user launches the application and enters the departure point, highway entrance, arrival point, highway exit, desired date and time.

[0439] Step 2:

[0440] The user checks the input information and clicks the registration button. The terminal temporarily stores the input data within the device and prepares to send it to the server.

[0441] Step 3:

[0442] The terminal transmits the data entered by the user to the server, including the departure point, arrival point, desired date and time.

[0443] Step 4:

[0444] The server receives the user's input information sent from the terminal, and the received data is first stored in a database.

[0445] Step 5:

[0446] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0447] Step 6:

[0448] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0449] Step 7:

[0450] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic volume can be expected at a particular time of day.

[0451] Step 8:

[0452] The server analyzes the predictions of the AI ​​model to identify peak times of day, and calculates optimal departure and arrival times based on the identified peak times of day.

[0453] Step 9:

[0454] The server generates the calculated recommended departure time and recommended arrival time results and transmits them to the user's terminal.

[0455] Step 10:

[0456] The terminal displays the prediction results received from the server to the user, including the busiest times and recommended departure and arrival times to avoid those times.

[0457] Step 11:

[0458] Based on the displayed information, the user can adjust his / her departure schedule, for example, by deciding to depart during a less busy time or to arrive later.

[0459] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user.

[0460] Example 1

[0461] 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."

[0462] Congestion on expressways is a major source of stress for road users, making it difficult to plan trips. Choosing an appropriate departure time to avoid congestion is particularly important when planning travel or business. However, conventional systems have had difficulty comprehensively predicting traffic conditions, weather, and event information, and suggesting optimal departure and arrival times. Therefore, an efficient system that can predict highway congestion and suggest optimal departure and arrival times to users is needed.

[0463] 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.

[0464] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for preprocessing the acquired data and inputting it into an artificial intelligence model, means for predicting traffic volume using artificial intelligence, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, and means for transmitting the calculated results to the user's terminal and displaying them, thereby enabling the user to receive accurate congestion forecast information and select optimal departure and arrival times.

[0465] "Means for receiving the user's input of departure point, arrival point, desired date and time" refers to the interface that receives the information the user inputs through an application or website.

[0466] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to programs or functions for collecting the necessary data from external data providers or APIs.

[0467] "Means for preprocessing acquired data and inputting it into an artificial intelligence model" refers to a system that converts collected information into a format that can be used by an AI model through processes such as cleaning, missing value completion, and normalization.

[0468] "Means for predicting traffic volume using artificial intelligence" refers to a function that uses machine learning algorithms or other AI models to predict future traffic volume.

[0469] "Means for calculating congested time periods based on the prediction results and calculating recommended departure and arrival times" refers to a program or algorithm that analyzes the traffic volume prediction results from AI and derives optimal departure and arrival times to avoid congestion.

[0470] "Means for sending the calculated results to the user's device and displaying the results" refers to an interface that transfers the prediction results from the server to the user's device (smartphone or computer) and displays them in a format that the user can check.

[0471] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times when a user inputs their departure point, arrival point, and desired date and time. This system is built around the user's terminal and a server.

[0472] User Actions

[0473] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0474] Server Processing

[0475] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0476] Specifically, we collect information from the following data sources:

[0477] 1. Traffic Data: Specific traffic APIs (e.g., Google Maps API)

[0478] 2. Weather forecast: Japan Meteorological Agency API

[0479] 3. Event Information: Regional Event Calendar API

[0480] Data Preprocessing

[0481] The collected data undergoes the following preprocessing:

[0482] Cleaning: Removing invalid data and noise.

[0483] Imputing missing values: Filling incomplete data with guesses or other data.

[0484] Normalization: Aligning the scale of the data.

[0485] This puts the data in a format suitable for AI models.

[0486] Prediction using artificial intelligence models

[0487] The server then runs an AI model based on the preprocessed data, which uses a machine learning algorithm (e.g., a regression model) created using Python's Scikit-learn library, to predict future traffic volumes.

[0488] Calculating optimal departure and arrival times

[0489] The server calculates the optimal departure and arrival times based on the predictions made by the AI ​​model. For example, if the AI ​​model predicts that congestion will occur between 10:00 and 12:00, the server will recommend departing at 08:00 or after 13:00 to avoid that time period.

[0490] Terminal display

[0491] The calculation results are sent from the server to the user's device, which then displays the results received from the server to the user. These results include peak times and recommended departure and arrival times to avoid these peak times. The user can use this information to adjust their schedule.

[0492] Specific examples

[0493] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, and weather forecasts, and uses the AI ​​model to make a prediction.

[0494] If the prediction result is "particularly busy between 10:00 and 12:00," the server will recommend "departing at 08:00 or after 13:00." The calculated information is sent to the terminal, and the user is shown a message saying "recommended departure at 08:00 or after 13:00." The user can use this information to choose the optimal departure time.

[0495] Prompt Sentence Examples

[0496] Specific prompt examples:

[0497] "A user wants to travel from Tokyo IC to Nagoya Nishi IC. The departure date and time is December 24, 2023 at 9:00 AM. Please suggest the best departure time for this user based on past Christmas Eve traffic data, current traffic conditions, and weather forecasts."

[0498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0499] Step 1:

[0500] The user launches the application on the terminal and inputs the departure point, arrival point, desired date and time.

[0501] Input: User-entered departure point, arrival point, desired date and time information

[0502] Output: Trip data entered into the terminal

[0503] Step 2:

[0504] Once the user confirms the information and clicks the registration button, the terminal sends the input data to the server.

[0505] Input: Travel data entered into the terminal

[0506] Output: Trip data transferred to the server

[0507] Step 3:

[0508] The server receives the user's input information sent from the terminal and stores it in a database.

[0509] Input: Travel data sent from the device

[0510] Output: Trip data stored in a database

[0511] Step 4:

[0512] The server then collects traffic data, weather forecasts, and event information from predetermined data sources.

[0513] Input: API request for a given data source

[0514] Output: Traffic data, weather forecast, and event information collected on the server

[0515] Step 5:

[0516] The server performs preprocessing on the collected data, which includes cleaning the data, imputing missing values, and normalizing it.

[0517] Input: Raw data collected

[0518] Output: Preprocessed data

[0519] Step 6:

[0520] The server runs an artificial intelligence model based on the preprocessed data to predict traffic volume.

[0521] Input: Preprocessed data

[0522] Output: Predicted traffic volume data

[0523] Step 7:

[0524] The server calculates the busy times based on the prediction results and calculates the recommended departure time and recommended arrival time.

[0525] Input: Predicted traffic data

[0526] Output: Calculated optimal departure and arrival times

[0527] Step 8:

[0528] The server sends the calculation results to the user's terminal.

[0529] Input: Calculated optimal departure and arrival times

[0530] Output: The calculation result sent to the user's device

[0531] Step 9:

[0532] The terminal displays the results received from the server to the user, who can then view the forecast information and suggested departure and arrival times.

[0533] Input: Calculation result sent from the server

[0534] Output: Displayed forecast information and recommended departure and arrival times

[0535] (Application example 1)

[0536] 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."

[0537] Conventional congestion prediction systems mainly rely on users checking traffic information before departure and planning departure times. However, autonomous vehicles are required to optimize routes in real time and avoid congestion. Furthermore, they must respond quickly to abnormal weather or sudden events. There is a problem with the lack of real-time congestion prediction and route suggestions for optimizing operations in autonomous vehicles.

[0538] 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.

[0539] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure times and recommended arrival times, and means for notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time, thereby enabling the autonomous vehicle to plan the optimal departure time and route that reflects traffic conditions in real time and reach the destination quickly and efficiently.

[0540] "User-entered departure point, arrival point, desired date and time" refers to the information in the system about the user's departure point, arrival point, and the specific date and time of desired travel.

[0541] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to the functionality of a system, device, or software for obtaining current and historical traffic conditions, weather forecasts, and information about major events in a specific area from different data sources.

[0542] "Means for predicting traffic volume using artificial intelligence based on acquired data" refers to the function of analyzing collected traffic data, weather forecasts, and event information, and using artificial intelligence and machine learning algorithms to predict future traffic volume with high accuracy.

[0543] "Means for calculating busy times based on the prediction results and calculating recommended departure and arrival times" refers to a function that identifies which times are expected to be crowded based on prediction data generated by an artificial intelligence model and determines the optimal departure and arrival times.

[0544] "Means of notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time" refers to the function of sending the calculated congestion forecast results and recommended departure and arrival times to the onboard system of the autonomous vehicle and using that information to optimize the route in real time.

[0545] The present invention is a highway congestion prediction system for an autonomous vehicle. The system includes: means for receiving a departure point, arrival point, and desired date and time input by a user; means for acquiring traffic data, weather forecasts, and event information from predetermined data sources; means for predicting traffic volume based on the acquired data using artificial intelligence; means for calculating congestion times based on the prediction results and calculating recommended departure and arrival times; and means for notifying the autonomous vehicle's onboard system of the results and optimizing the route in real time.

[0546] System Program

[0547] The main components of the system are the user terminal, the server, and the autonomous vehicle's onboard system. The user terminal provides an interface for inputting the departure point, arrival point, desired date and time. The server collects and processes traffic data, weather forecasts, and event information, and uses an artificial intelligence model to predict congestion. The autonomous vehicle's onboard system receives the results sent from the server and optimizes the route in real time.

[0548] Processing Details

[0549] Hardware and Software

[0550] Hardware: On-board computer systems for autonomous vehicles

[0551] Software: Python, requests library, scikit-learn (to run machine learning models)

[0552] Data processing and calculation

[0553] 1. Data collection: The server receives the departure point, arrival point, desired date and time from the user's device, and then obtains traffic data, weather forecasts, and event information through API requests.

[0554] 2. Data preprocessing: Cleaning the collected data and converting it into a format suitable for artificial intelligence models, including imputing missing values ​​and normalizing the data.

[0555] 3. Artificial intelligence prediction: Based on preprocessed data, congestion predictions are made using a machine learning model using scikit-learn.

[0556] 4. Optimization: Based on the prediction results, the optimal departure and arrival times are calculated to avoid peak times. The results are sent to the autonomous vehicle's onboard system.

[0557] 5. Real-time notification and route optimization: Based on the information received, the onboard system will optimize routes and departure times in real time and display the results on the vehicle's display, allowing passengers to adjust their travel plans accordingly.

[0558] Specific examples

[0559] For example, if a user wishes to travel from "City Departure Point A" to "City Destination Point B," they enter the date and time as "December 24, 2023, 9:00 AM." The user's device sends this information to the server, which collects past data, current traffic conditions, and weather forecasts and uses an AI model to make a prediction. If the prediction indicates that "the route will be particularly congested between 10:00 AM and 12:00 PM," the server calculates that "it is recommended to depart at 8:00 AM or after 1:00 PM." This information is sent to the autonomous vehicle's onboard system, which notifies and optimizes the route in real time.

[0560] Prompt Sentence Examples

[0561] Use traffic data to suggest optimal departure times from the following origins to destinations:

[0562] Starting point: City starting point A

[0563] Arrival point: City arrival point B

[0564] Scheduled departure time: December 24, 2023, 9:00 AM

[0565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0566] Step 1:

[0567] The user inputs the departure point, arrival point, desired date and time. This information is entered into an application on the device (smartphone, tablet, PC, etc.) used by the user. This input information includes the specific planned departure location, planned arrival location, and desired departure date and time. Once the input is complete, the user clicks the "Submit" button, which causes the device to send this information to the server.

[0568] Step 2:

[0569] The server receives the user's input information sent from the device and stores the information of the departure point, arrival point, desired date and time in a database.The server then sends an API request to collect traffic data, weather forecasts, and event information from predetermined data sources.This data is obtained from different data sources (traffic information API, weather information API, event information API, etc.).

[0570] Step 3:

[0571] The server preprocesses the collected traffic data, weather forecasts, and event information. Specifically, it performs data cleaning (noise removal and missing value imputation), normalization (standardizing the scale of the data), and feature engineering (generating new useful features). After preprocessing, the data is converted into a format suitable for artificial intelligence models. The input of this step is the collected raw data, and the output is preprocessed data.

[0572] Step 4:

[0573] The server uses the preprocessed data to predict traffic volume using an artificial intelligence model with machine learning libraries such as scikit-learn. In this prediction process, past traffic data, weather data, and event information are given as input, and future traffic volume is estimated based on this. The model is pre-trained and capable of making predictions in real time. The input for this step is the preprocessed data, and the output is the congestion prediction results.

[0574] Step 5:

[0575] Based on the congestion prediction results, the server identifies which time periods are expected to be congested. It then calculates the optimal departure and arrival times to avoid congestion. For example, if congestion is predicted for a specific time period, it will suggest departure and arrival times that avoid that time period. The input to this step is the congestion prediction results, and the output is recommended departure and arrival times.

[0576] Step 6:

[0577] The server sends the calculated results to the user's device and the autonomous vehicle's onboard system. The user's device displays the recommended departure and arrival times, and the autonomous vehicle's onboard system optimizes the route in real time. The input of this step is the recommended departure and arrival times and route information, and the output is notifications to the user's device and the onboard system.

[0578] Step 7:

[0579] The onboard system of the autonomous vehicle optimizes the route in real time based on the recommended time and route information received from the server. Passengers can check the optimized route information on the vehicle display and depart at the optimal time. The input of this step is the notification from the server, and the output is the optimized route information.

[0580] 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.

[0581] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[0582] User Actions

[0583] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input information and clicks the registration button, the terminal sends this data to the server.

[0584] Server Processing

[0585] The server receives the user's input information sent from the device, stores this information in a database, and then collects traffic data, weather forecasts, and event information from designated data sources, thereby obtaining the data necessary for prediction.

[0586] The server preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. It cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0587] The server then uses the preprocessed data to run an artificial intelligence model to predict traffic volume. The AI ​​model calculates how much traffic is likely to occur at a particular time based on past and current data. The resulting prediction is a forecast of congestion during that time.

[0588] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[0589] Emotion engine processing

[0590] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[0591] Terminal display

[0592] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[0593] Specific examples

[0594] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make predictions.

[0595] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[0596] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[0597] The processing flow will be explained below.

[0598] Step 1:

[0599] The user launches the application and enters their starting point, highway entrance, arrival point, highway exit, desired date and time. If the emotion engine is enabled, voice input and a facial recognition camera capture the user's state.

[0600] Step 2:

[0601] The user confirms the input and clicks the registration button. The device temporarily stores the input data and prepares to send it to the server. Emotion data is also collected at the same time.

[0602] Step 3:

[0603] The device transmits the data and emotion data entered by the user to the server, including the departure point, arrival point, desired date and time, voice data, and image data.

[0604] Step 4:

[0605] The server receives the user's input information sent from the device and stores it in a database. Voice data and image data are also stored for analysis by the emotion engine.

[0606] Step 5:

[0607] The emotion engine analyzes the stored voice and image data to estimate the user's emotions. For example, it can use voice analysis to determine the user's stress level and emotional state.

[0608] Step 6:

[0609] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0610] Step 7:

[0611] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0612] Step 8:

[0613] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic is likely to occur at a particular time.

[0614] Step 9:

[0615] The server analyzes the predictions of the AI ​​model to identify peak times. It also calculates optimal departure and arrival times, taking into account the user's emotional state. For example, it suggests recommended times for a stressed user to relax.

[0616] Step 10:

[0617] The server generates the calculated recommended departure and arrival times and sends them to the user's device, along with advice based on additional emotion data.

[0618] Step 11:

[0619] The device displays prediction results and emotion-based advice received from the server to the user, including peak times, recommended departure and arrival times, and emotion-based relaxation techniques.

[0620] Step 12:

[0621] The user can then adjust their departure plans based on the displayed information, decide whether to leave earlier or later based on recommended times to avoid crowds, and, if necessary, take emotional advice and incorporate relaxation techniques.

[0622] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user, as well as personalized advice based on their emotions.

[0623] Example 2

[0624] 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."

[0625] Current traffic prediction systems can suggest optimal departure and arrival times based on the user's input of departure and arrival locations and desired date and time, but they lack the ability to provide personalized advice that takes into account the user's individual feelings. Furthermore, data collection and pre-processing to improve traffic volume prediction accuracy are often insufficient. This can lead to lower user satisfaction and negatively impact the actual travel experience.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0627] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather information, and event information from a predetermined data source, means for predicting traffic volume using a generative AI model based on the acquired data, means for calculating busy periods based on the prediction results and for calculating recommended departure and arrival times, means for analyzing the user's emotions, and means for notifying the user's terminal of the results of the analysis. This makes it possible to improve the accuracy of traffic predictions and provide personalized advice based on the user's emotions.

[0628] "User" means a person who uses the system to input the departure point, arrival point, desired date and time, and receives suggestions for optimal departure and arrival times.

[0629] The "starting point" is the location where the user begins their journey.

[0630] The "destination point" is the location where the user ends their journey.

[0631] A "desired date and time" is a specific date and time that a user desires to depart or arrive.

[0632] "Terminal" means a device used by a User to access the System and to enter and receive information.

[0633] A "server" is a computer system that receives, processes, and analyzes data sent from a user's terminal.

[0634] A "data source" is an information source that provides data such as traffic data, weather information, and event information.

[0635] "Traffic Data" means information about past and current traffic conditions.

[0636] "Weather information" refers to information about weather forecasts and weather conditions.

[0637] "Event information" is information about an event that occurs at a specific place and time.

[0638] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to predict traffic volume.

[0639] "Traffic volume forecasting" refers to predicting the degree of traffic congestion during a specific time period.

[0640] The "emotion engine" is a system that recognizes emotions by analyzing the user's voice data and facial expressions.

[0641] "Means of notification" refers to the method by which a server or terminal displays or conveys information to a user.

[0642] The "recommended departure time and recommended arrival time" are the optimal departure and arrival times suggested to avoid congestion.

[0643] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[0644] System configuration

[0645] This system consists of a terminal used by the user, a server that processes data, various data sources, and an emotion engine.

[0646] Hardware and software used

[0647] Device: A device that allows a user to input information. Examples include smartphones, tablets, and computers.

[0648] Server: A computer system that receives data and performs predictions and analysis. Example: High performance server (including cloud servers).

[0649] Database: A system for storing and managing data. Examples: MySQL, PostgreSQL.

[0650] Generative AI model: Artificial intelligence for traffic prediction. Example: Machine learning frameworks such as TensorFlow, PyTorch, etc.

[0651] Emotion engine: A system that analyzes a user's emotions. Examples: speech recognition engines and facial expression recognition software.

[0652] System Operation

[0653] User Action:

[0654] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input and clicks the "Register" button, the terminal sends this data to the server.

[0655] Server Action:

[0656] The server receives user input information sent from the device. It stores this information in a database. It then collects traffic data, weather information, and event information from specified data sources. This allows it to obtain the data necessary for prediction. The server preprocesses the collected data and converts it into a format that can be input into the generative AI model. It then cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0657] The server then uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model uses past and current data to calculate how much traffic is expected at a particular time. The resulting prediction is a forecast of congestion at that time.

[0658] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[0659] Emotion Engine Processing:

[0660] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[0661] Terminal display:

[0662] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[0663] Specific examples

[0664] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather information, and more, and uses the generative AI model to make predictions.

[0665] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[0666] Examples of prompt statements

[0667] Enter the following prompt into the generative AI model:

[0668] The user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" on the date and time "December 24, 2023, 9:00 AM." What is the expected traffic congestion and what are the best departure and arrival times? Also, if the user feels "very stressed," what are some appropriate ways to relax?

[0669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] Users launch the application and input their departure point, highway entrance, arrival point, highway exit, and desired date and time. Examples of input information include "Tokyo IC," "Nagoya Nishi IC," and "December 24, 2023, 9:00 AM."

[0672] Input: departure point, highway entrance, arrival point, highway exit, desired date and time.

[0673] Output: Input starting point, highway entrance, arrival point, highway exit, desired date and time.

[0674] Step 2:

[0675] When the user confirms the input and clicks the "Register" button, the device sends this data to the server. The device stores the input data in its internal memory and uses an API to send the data to the server.

[0676] Input: Data entered and confirmed by the user.

[0677] Output: The data sent to the server.

[0678] Step 3:

[0679] The server receives the user's input information sent from the terminal and stores it in a database, for example, using a database system such as MySQL.

[0680] Input: User input data sent from the terminal.

[0681] Output: User data stored in the database.

[0682] Step 4:

[0683] The server collects traffic data, weather information, and event information from predetermined data sources, including historical traffic data, current traffic conditions, weather forecasts, and event information. The collection process obtains real-time data via API.

[0684] Input: User information stored in the database.

[0685] Output: Collected traffic data, weather information, and event information.

[0686] Step 5:

[0687] The server performs preprocessing on the collected data, cleaning the data, filling in missing values, normalizing the data, and creating a dataset for efficient prediction. Specifically, it uses Python libraries (Pandas, NumPy, etc.) to format and fill in the data.

[0688] Input: Collected traffic data, weather information, and event information.

[0689] Output: A preprocessed dataset.

[0690] Step 6:

[0691] The server uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model calculates the expected traffic volume for a specific time period based on past and current data. The generative AI model uses TensorFlow and PyTorch.

[0692] Input: The preprocessed dataset.

[0693] Output: Traffic volume prediction results.

[0694] Step 7:

[0695] The server analyzes the prediction results obtained by the AI ​​model to identify busy times. It also calculates the optimal departure and arrival times to avoid congestion. For example, if the prediction results indicate that "it will be particularly busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00."

[0696] Input: Traffic prediction results.

[0697] Output: Busy times, best departure and arrival times.

[0698] Step 8:

[0699] When a user expresses emotions through voice input or facial expression recognition, the emotion engine analyzes the data. The device collects the user's voice data and sends it to the server. The emotion engine in the server analyzes the voice and facial expression data to infer the user's emotional state.

[0700] Input: User's voice data and facial expression data.

[0701] Output: Parsed emotional state.

[0702] Step 9:

[0703] The server then sends the calculated recommended departure and arrival times, as well as additional advice based on the user's emotions, to the user's device, which then displays these results to the user. Specifically, the results include customized advice such as peak times, recommended departure times (e.g., "8:00 AM departure or after 1:00 PM recommended"), and even the provision of relaxation music.

[0704] Inputs: Recommended departure time, arrival time, and sentiment-based advice.

[0705] Output: The results that are displayed to the user.

[0706] (Application example 2)

[0707] 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."

[0708] Conventional traffic prediction systems only provide prediction results based on traffic volume data and are unable to make suggestions that take into account the user's emotional state. This can result in users departing or arriving at times that make them feel stressed. Furthermore, providing only recommended travel times poses the problem of not being able to address the individual circumstances of each user. To solve this problem, a system that takes into account the user's emotional data and makes more personalized suggestions is needed.

[0709] 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.

[0710] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, means for notifying the user of the results to their terminal, means for collecting and analyzing emotional data from the user using a camera or voice input device in the vehicle, and means for providing personalized recommendations based on the analyzed emotional data. This reduces stress for the user and makes it possible to suggest optimal departure and arrival times according to the individual emotional state.

[0711] The "starting point" is where the user begins their journey.

[0712] "Destination" is the location where the user intends to end their journey.

[0713] "Desired date and time" means the specific date and time at which the user wishes to begin their journey.

[0714] "Data Source" means a source of information that provides traffic data, weather forecasts, and event information.

[0715] "Traffic data" refers to information about traffic flow and congestion at a given point in time.

[0716] A "weather forecast" is information that predicts future weather conditions.

[0717] "Event information" refers to information about events held in a specific area or on a specific date.

[0718] "Artificial intelligence" refers to computer programs or systems that simulate intelligent human behavior.

[0719] A "busy hour" is a specific time range during which traffic is expected to be very heavy.

[0720] "Recommended Departure Time" means the optimal departure time suggested to the user to avoid congestion.

[0721] "Recommended Arrival Time" refers to the optimal arrival time suggested to the user to avoid congestion.

[0722] "Emotional Data" refers to information about a user's emotional state.

[0723] A "camera" is a device that captures images and records or transmits the data.

[0724] A "voice input device" is a device that has the function of acquiring and analyzing voice data.

[0725] "Means of analysis" refers to the processes and systems that evaluate acquired data and extract meaningful information.

[0726] "Personalized suggestions" are suggestions that are customized based on the user's individual situation and emotional state.

[0727] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times based on the departure point, arrival point, and desired date and time entered by the user. It also collects and analyzes user emotion data to provide personalized advice.

[0728] System Overview

[0729] The system mainly consists of a user device and a server. Users can use their smartphone or in-car information system to input their departure and arrival points, desired date and time. This data is sent to the server, which then retrieves traffic data, weather forecasts, and event information from designated data sources.

[0730] Hardware and software used

[0731] Cameras and audio input devices: Used to collect user emotional data, such as in-car cameras and microphones.

[0732] EmotionEngine (tentative library name): Software that analyzes collected emotional data.

[0733] VehicleAPI (tentative API name): Software for controlling the navigation system of self-driving vehicles.

[0734] requests library: A software library used to communicate with the traffic congestion prediction server.

[0735] Details of data processing and calculation

[0736] The server receives the user's input of departure and arrival points, desired date and time, and based on this data, retrieves traffic data, weather forecasts, and event information from predetermined data sources.

[0737] The server then analyzes the data and uses an artificial intelligence model to predict traffic volume. Specifically, it predicts traffic volume for specific time periods based on past and current data. It then calculates peak times and calculates optimal departure and arrival times. This information is then sent to the user's device.

[0738] Use of user sentiment data

[0739] Additionally, the system uses in-car cameras and audio input devices to analyze the user's emotional state. EmotionEngine analyzes the user's voice and facial expressions to extract emotional data. Based on the results, recommended departure and arrival times are personalized. For example, if the user indicates high stress, the system will suggest playing relaxing music in the car.

[0740] Examples and prompts

[0741] As a specific example, suppose a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" and enters the date and time as "December 24, 2023, 9:00 AM." When the user registers this information, the server collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and other information, and uses the AI ​​model to make a prediction. If the prediction indicates that traffic will be particularly congested between 10:00 AM and 12:00 PM, the system calculates that "departing at 8:00 AM or after 1:00 PM is recommended." This information is notified and displayed on the user's device. Furthermore, if the user verbally says, "I'm feeling very stressed," the emotion engine determines that the user is feeling stressed and suggests relaxing background music and stress management measures.

[0742] An example prompt for a generative AI model might look something like this:

[0743] The user wishes to depart from Tokyo IC to Nagoya Nishi IC at 09:00 on December 24, 2023. Based on this information, calculate the optimal departure and arrival times using historical traffic data, current traffic conditions, and weather forecasts. Also, if the user's voice and facial expression data indicate that they are feeling stressed, suggest playing relaxing music.

[0744] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[0745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0746] Step 1:

[0747] The user inputs the departure point, arrival point, desired date and time into the terminal. The terminal collects this data and sends it to the server when the registration button is pressed.

[0748] Input: Departure point, arrival point, desired date and time

[0749] Output: Sends input data to the server

[0750] How it works: The user uses a smartphone or in-car infotainment system to enter, confirm, and submit data.

[0751] Step 2:

[0752] The server receives user data sent from the terminal and makes requests to obtain traffic data, weather forecasts, and event information from predetermined data sources.

[0753] Input: User-entered departure point, arrival point, desired date and time

[0754] Output: Traffic data, weather forecast, event information

[0755] How it works: The server calls the appropriate APIs to collect the necessary traffic data, weather forecasts and event information.

[0756] Step 3:

[0757] The server preprocesses the acquired data and converts it into a format that can be input into an artificial intelligence model. It cleans the traffic data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0758] Input: traffic data, weather forecast, event information

[0759] Output: Preprocessed dataset

[0760] What it does: Performs preprocessing steps such as cleaning data, imputing missing values, and normalizing it to convert it into a data format suitable for artificial intelligence models.

[0761] Step 4:

[0762] The server uses the preprocessed data to run an artificial intelligence model to predict traffic volume, calculating traffic volume for a specific time period based on past and current data.

[0763] Input: Preprocessed dataset

[0764] Output: Traffic volume prediction results

[0765] How it works: Traffic volume predictions are made using artificial intelligence models (machine learning algorithms).

[0766] Step 5:

[0767] Based on the traffic volume prediction results, the server identifies busy times and calculates recommended departure and arrival times, thereby generating an optimal travel plan.

[0768] Input: Traffic volume prediction results

[0769] Output: Recommended departure and arrival times

[0770] Operation: Analyzes busy times and calculates recommended times.

[0771] Step 6:

[0772] To collect user emotion data, the system uses cameras and voice input devices inside the car. The emotion engine analyzes the user's emotions and sends the data to a server.

[0773] Input: User's voice data, video data

[0774] Output: Emotion data as the analysis result

[0775] How it works: The device uses a camera and microphone to collect audio and video and perform sentiment analysis.

[0776] Step 7:

[0777] The server analyzes the emotional data and personalizes recommendations based on the user's emotional state, for example, suggesting playing relaxing music in the car if the user indicates high stress.

[0778] Input: Emotion data, recommended departure time and recommended arrival time

[0779] Output: Personalized recommendations

[0780] Behavior: Customize recommended time and additional advice based on sentiment data.

[0781] Step 8:

[0782] The server then sends the final recommendations and personalized advice to the user's device for display, allowing them to see suggestions tailored to their emotional state.

[0783] Input: Personalized recommendations

[0784] Output: Displayed recommended departure and arrival times, sentiment-based advice

[0785] Operation: The terminal displays and notifies the user of the results received from the server.

[0786] In this way, the present invention not only allows the user to know the best departure and arrival times, but also provides personalized advice according to their emotional state.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] [Third embodiment]

[0791] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0792] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0793] 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).

[0794] 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.

[0795] 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.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times by having the user input their departure point, destination, and desired date and time. This system is built around the user's terminal and a server.

[0804] User Actions

[0805] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0806] Server Processing

[0807] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0808] This system preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. After processes such as data cleaning, missing value completion, and normalization, a dataset for efficient prediction is created.

[0809] The server then uses the preprocessed data to run an artificial intelligence model, which uses machine learning algorithms to accurately predict traffic volume based on past and current data. The resulting prediction is how much traffic there will be for a given date and time, including the hours when it is expected to be particularly busy.

[0810] The server then calculates the optimal departure and arrival times based on the peak times. This calculation aims to select the most efficient time for the user to avoid the crowds. For example, if a particular time period is predicted to be peak, the server will recommend departure and arrival times that avoid that time period.

[0811] Terminal display

[0812] The calculated results are sent from the server to the user's device, where the device displays the congestion forecast results to the user. The results include peak times and recommended departure and arrival times to avoid the crowds. The user can use this information to adjust their schedule and choose the optimal departure time.

[0813] Specific examples

[0814] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make a prediction.

[0815] For example, if the prediction result is "It will be particularly busy between 10:00 and 12:00," the server calculates that "Departure at 08:00 or after 13:00 is recommended." This is sent to the terminal and displayed to the user, who can then make plans based on the recommended time displayed.

[0816] The above is a specific embodiment of the present invention, in which the elements of the user, terminal, and server work together to provide a means for efficiently avoiding congestion.

[0817] The processing flow will be explained below.

[0818] Step 1:

[0819] The user launches the application and enters the departure point, highway entrance, arrival point, highway exit, desired date and time.

[0820] Step 2:

[0821] The user checks the input information and clicks the registration button. The terminal temporarily stores the input data within the device and prepares to send it to the server.

[0822] Step 3:

[0823] The terminal transmits the data entered by the user to the server, including the departure point, arrival point, desired date and time.

[0824] Step 4:

[0825] The server receives the user's input information sent from the terminal, and the received data is first stored in a database.

[0826] Step 5:

[0827] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0828] Step 6:

[0829] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0830] Step 7:

[0831] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic volume can be expected at a particular time of day.

[0832] Step 8:

[0833] The server analyzes the predictions of the AI ​​model to identify peak times of day, and calculates optimal departure and arrival times based on the identified peak times of day.

[0834] Step 9:

[0835] The server generates the calculated recommended departure time and recommended arrival time results and transmits them to the user's terminal.

[0836] Step 10:

[0837] The terminal displays the prediction results received from the server to the user, including the busiest times and recommended departure and arrival times to avoid those times.

[0838] Step 11:

[0839] Based on the displayed information, the user can adjust his / her departure schedule, for example, by deciding to depart during a less busy time or to arrive later.

[0840] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user.

[0841] Example 1

[0842] 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."

[0843] Congestion on expressways is a major source of stress for road users, making it difficult to plan trips. Choosing an appropriate departure time to avoid congestion is particularly important when planning travel or business. However, conventional systems have had difficulty comprehensively predicting traffic conditions, weather, and event information, and suggesting optimal departure and arrival times. Therefore, an efficient system that can predict highway congestion and suggest optimal departure and arrival times to users is needed.

[0844] 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.

[0845] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for preprocessing the acquired data and inputting it into an artificial intelligence model, means for predicting traffic volume using artificial intelligence, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, and means for transmitting the calculated results to the user's terminal and displaying them, thereby enabling the user to receive accurate congestion forecast information and select optimal departure and arrival times.

[0846] "Means for receiving the user's input of departure point, arrival point, desired date and time" refers to the interface that receives the information the user inputs through an application or website.

[0847] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to programs or functions for collecting the necessary data from external data providers or APIs.

[0848] "Means for preprocessing acquired data and inputting it into an artificial intelligence model" refers to a system that converts collected information into a format that can be used by an AI model through processes such as cleaning, missing value completion, and normalization.

[0849] "Means for predicting traffic volume using artificial intelligence" refers to a function that uses machine learning algorithms or other AI models to predict future traffic volume.

[0850] "Means for calculating congested time periods based on the prediction results and calculating recommended departure and arrival times" refers to a program or algorithm that analyzes the traffic volume prediction results from AI and derives optimal departure and arrival times to avoid congestion.

[0851] "Means for sending the calculated results to the user's device and displaying the results" refers to an interface that transfers the prediction results from the server to the user's device (smartphone or computer) and displays them in a format that the user can check.

[0852] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times when a user inputs their departure point, arrival point, and desired date and time. This system is built around the user's terminal and a server.

[0853] User Actions

[0854] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[0855] Server Processing

[0856] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[0857] Specifically, we collect information from the following data sources:

[0858] 1. Traffic Data: Specific traffic APIs (e.g., Google Maps API)

[0859] 2. Weather forecast: Japan Meteorological Agency API

[0860] 3. Event Information: Regional Event Calendar API

[0861] Data Preprocessing

[0862] The collected data undergoes the following preprocessing:

[0863] Cleaning: Removing invalid data and noise.

[0864] Imputing missing values: Filling incomplete data with guesses or other data.

[0865] Normalization: Aligning the scale of the data.

[0866] This puts the data in a format suitable for AI models.

[0867] Prediction using artificial intelligence models

[0868] The server then runs an AI model based on the preprocessed data, which uses a machine learning algorithm (e.g., a regression model) created using Python's Scikit-learn library, to predict future traffic volumes.

[0869] Calculating optimal departure and arrival times

[0870] The server calculates the optimal departure and arrival times based on the predictions made by the AI ​​model. For example, if the AI ​​model predicts that congestion will occur between 10:00 and 12:00, the server will recommend departing at 08:00 or after 13:00 to avoid that time period.

[0871] Terminal display

[0872] The calculation results are sent from the server to the user's device, which then displays the results received from the server to the user. These results include peak times and recommended departure and arrival times to avoid these peak times. The user can use this information to adjust their schedule.

[0873] Specific examples

[0874] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, and weather forecasts, and uses the AI ​​model to make a prediction.

[0875] If the prediction result is "particularly busy between 10:00 and 12:00," the server will recommend "departing at 08:00 or after 13:00." The calculated information is sent to the terminal, and the user is shown a message saying "recommended departure at 08:00 or after 13:00." The user can use this information to choose the optimal departure time.

[0876] Prompt Sentence Examples

[0877] Specific prompt examples:

[0878] "A user wants to travel from Tokyo IC to Nagoya Nishi IC. The departure date and time is December 24, 2023 at 9:00 AM. Please suggest the best departure time for this user based on past Christmas Eve traffic data, current traffic conditions, and weather forecasts."

[0879] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0880] Step 1:

[0881] The user launches the application on the terminal and inputs the departure point, arrival point, desired date and time.

[0882] Input: User-entered departure point, arrival point, desired date and time information

[0883] Output: Trip data entered into the terminal

[0884] Step 2:

[0885] Once the user confirms the information and clicks the registration button, the terminal sends the input data to the server.

[0886] Input: Travel data entered into the terminal

[0887] Output: Trip data transferred to the server

[0888] Step 3:

[0889] The server receives the user's input information sent from the terminal and stores it in a database.

[0890] Input: Travel data sent from the device

[0891] Output: Trip data stored in a database

[0892] Step 4:

[0893] The server then collects traffic data, weather forecasts, and event information from predetermined data sources.

[0894] Input: API request for a given data source

[0895] Output: Traffic data, weather forecast, and event information collected on the server

[0896] Step 5:

[0897] The server performs preprocessing on the collected data, which includes cleaning the data, imputing missing values, and normalizing it.

[0898] Input: Raw data collected

[0899] Output: Preprocessed data

[0900] Step 6:

[0901] The server runs an artificial intelligence model based on the preprocessed data to predict traffic volume.

[0902] Input: Preprocessed data

[0903] Output: Predicted traffic volume data

[0904] Step 7:

[0905] The server calculates the busy times based on the prediction results and calculates the recommended departure time and recommended arrival time.

[0906] Input: Predicted traffic data

[0907] Output: Calculated optimal departure and arrival times

[0908] Step 8:

[0909] The server sends the calculation results to the user's terminal.

[0910] Input: Calculated optimal departure and arrival times

[0911] Output: The calculation result sent to the user's device

[0912] Step 9:

[0913] The terminal displays the results received from the server to the user, who can then view the forecast information and suggested departure and arrival times.

[0914] Input: Calculation result sent from the server

[0915] Output: Displayed forecast information and recommended departure and arrival times

[0916] (Application example 1)

[0917] 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."

[0918] Conventional congestion prediction systems mainly rely on users checking traffic information before departure and planning departure times. However, autonomous vehicles are required to optimize routes in real time and avoid congestion. Furthermore, they must respond quickly to abnormal weather or sudden events. There is a problem with the lack of real-time congestion prediction and route suggestions for optimizing operations in autonomous vehicles.

[0919] 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.

[0920] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure times and recommended arrival times, and means for notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time, thereby enabling the autonomous vehicle to plan the optimal departure time and route that reflects traffic conditions in real time and reach the destination quickly and efficiently.

[0921] "User-entered departure point, arrival point, desired date and time" refers to the information in the system about the user's departure point, arrival point, and the specific date and time of desired travel.

[0922] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to the functionality of a system, device, or software for obtaining current and historical traffic conditions, weather forecasts, and information about major events in a specific area from different data sources.

[0923] "Means for predicting traffic volume using artificial intelligence based on acquired data" refers to the function of analyzing collected traffic data, weather forecasts, and event information, and using artificial intelligence and machine learning algorithms to predict future traffic volume with high accuracy.

[0924] "Means for calculating busy times based on the prediction results and calculating recommended departure and arrival times" refers to a function that identifies which times are expected to be crowded based on prediction data generated by an artificial intelligence model and determines the optimal departure and arrival times.

[0925] "Means of notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time" refers to the function of sending the calculated congestion forecast results and recommended departure and arrival times to the onboard system of the autonomous vehicle and using that information to optimize the route in real time.

[0926] The present invention is a highway congestion prediction system for an autonomous vehicle. The system includes: means for receiving a departure point, arrival point, and desired date and time input by a user; means for acquiring traffic data, weather forecasts, and event information from predetermined data sources; means for predicting traffic volume based on the acquired data using artificial intelligence; means for calculating congestion times based on the prediction results and calculating recommended departure and arrival times; and means for notifying the autonomous vehicle's onboard system of the results and optimizing the route in real time.

[0927] System Program

[0928] The main components of the system are the user terminal, the server, and the autonomous vehicle's onboard system. The user terminal provides an interface for inputting the departure point, arrival point, desired date and time. The server collects and processes traffic data, weather forecasts, and event information, and uses an artificial intelligence model to predict congestion. The autonomous vehicle's onboard system receives the results sent from the server and optimizes the route in real time.

[0929] Processing Details

[0930] Hardware and Software

[0931] Hardware: On-board computer systems for autonomous vehicles

[0932] Software: Python, requests library, scikit-learn (to run machine learning models)

[0933] Data processing and calculation

[0934] 1. Data collection: The server receives the departure point, arrival point, desired date and time from the user's device, and then obtains traffic data, weather forecasts, and event information through API requests.

[0935] 2. Data preprocessing: Cleaning the collected data and converting it into a format suitable for artificial intelligence models, including imputing missing values ​​and normalizing the data.

[0936] 3. Artificial intelligence prediction: Based on preprocessed data, congestion predictions are made using a machine learning model using scikit-learn.

[0937] 4. Optimization: Based on the prediction results, the optimal departure and arrival times are calculated to avoid peak times. The results are sent to the autonomous vehicle's onboard system.

[0938] 5. Real-time notification and route optimization: Based on the information received, the onboard system will optimize routes and departure times in real time and display the results on the vehicle's display, allowing passengers to adjust their travel plans accordingly.

[0939] Specific examples

[0940] For example, if a user wishes to travel from "City Departure Point A" to "City Destination Point B," they enter the date and time as "December 24, 2023, 9:00 AM." The user's device sends this information to the server, which collects past data, current traffic conditions, and weather forecasts and uses an AI model to make a prediction. If the prediction indicates that "the route will be particularly congested between 10:00 AM and 12:00 PM," the server calculates that "it is recommended to depart at 8:00 AM or after 1:00 PM." This information is sent to the autonomous vehicle's onboard system, which notifies and optimizes the route in real time.

[0941] Prompt Sentence Examples

[0942] Use traffic data to suggest optimal departure times from the following origins to destinations:

[0943] Starting point: City starting point A

[0944] Arrival point: City arrival point B

[0945] Scheduled departure time: December 24, 2023, 9:00 AM

[0946] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0947] Step 1:

[0948] The user inputs the departure point, arrival point, desired date and time. This information is entered into an application on the device (smartphone, tablet, PC, etc.) used by the user. This input information includes the specific planned departure location, planned arrival location, and desired departure date and time. Once the input is complete, the user clicks the "Submit" button, which causes the device to send this information to the server.

[0949] Step 2:

[0950] The server receives the user's input information sent from the device and stores the information of the departure point, arrival point, desired date and time in a database.The server then sends an API request to collect traffic data, weather forecasts, and event information from predetermined data sources.This data is obtained from different data sources (traffic information API, weather information API, event information API, etc.).

[0951] Step 3:

[0952] The server preprocesses the collected traffic data, weather forecasts, and event information. Specifically, it performs data cleaning (noise removal and missing value imputation), normalization (standardizing the scale of the data), and feature engineering (generating new useful features). After preprocessing, the data is converted into a format suitable for artificial intelligence models. The input of this step is the collected raw data, and the output is preprocessed data.

[0953] Step 4:

[0954] The server uses the preprocessed data to predict traffic volume using an artificial intelligence model with machine learning libraries such as scikit-learn. In this prediction process, past traffic data, weather data, and event information are given as input, and future traffic volume is estimated based on this. The model is pre-trained and capable of making predictions in real time. The input for this step is the preprocessed data, and the output is the congestion prediction results.

[0955] Step 5:

[0956] Based on the congestion prediction results, the server identifies which time periods are expected to be congested. It then calculates the optimal departure and arrival times to avoid congestion. For example, if congestion is predicted for a specific time period, it will suggest departure and arrival times that avoid that time period. The input to this step is the congestion prediction results, and the output is recommended departure and arrival times.

[0957] Step 6:

[0958] The server sends the calculated results to the user's device and the autonomous vehicle's onboard system. The user's device displays the recommended departure and arrival times, and the autonomous vehicle's onboard system optimizes the route in real time. The input of this step is the recommended departure and arrival times and route information, and the output is notifications to the user's device and the onboard system.

[0959] Step 7:

[0960] The onboard system of the autonomous vehicle optimizes the route in real time based on the recommended time and route information received from the server. Passengers can check the optimized route information on the vehicle display and depart at the optimal time. The input of this step is the notification from the server, and the output is the optimized route information.

[0961] 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.

[0962] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[0963] User Actions

[0964] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input information and clicks the registration button, the terminal sends this data to the server.

[0965] Server Processing

[0966] The server receives the user's input information sent from the device, stores this information in a database, and then collects traffic data, weather forecasts, and event information from designated data sources, thereby obtaining the data necessary for prediction.

[0967] The server preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. It cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[0968] The server then uses the preprocessed data to run an artificial intelligence model to predict traffic volume. The AI ​​model calculates how much traffic is likely to occur at a particular time based on past and current data. The resulting prediction is a forecast of congestion during that time.

[0969] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[0970] Emotion engine processing

[0971] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[0972] Terminal display

[0973] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[0974] Specific examples

[0975] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make predictions.

[0976] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[0977] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[0978] The processing flow will be explained below.

[0979] Step 1:

[0980] The user launches the application and enters their starting point, highway entrance, arrival point, highway exit, desired date and time. If the emotion engine is enabled, voice input and a facial recognition camera capture the user's state.

[0981] Step 2:

[0982] The user confirms the input and clicks the registration button. The device temporarily stores the input data and prepares to send it to the server. Emotion data is also collected at the same time.

[0983] Step 3:

[0984] The device transmits the data and emotion data entered by the user to the server, including the departure point, arrival point, desired date and time, voice data, and image data.

[0985] Step 4:

[0986] The server receives the user's input information sent from the device and stores it in a database. Voice data and image data are also stored for analysis by the emotion engine.

[0987] Step 5:

[0988] The emotion engine analyzes the stored voice and image data to estimate the user's emotions. For example, it can use voice analysis to determine the user's stress level and emotional state.

[0989] Step 6:

[0990] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[0991] Step 7:

[0992] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[0993] Step 8:

[0994] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic is likely to occur at a particular time.

[0995] Step 9:

[0996] The server analyzes the predictions of the AI ​​model to identify peak times. It also calculates optimal departure and arrival times, taking into account the user's emotional state. For example, it suggests recommended times for a stressed user to relax.

[0997] Step 10:

[0998] The server generates the calculated recommended departure and arrival times and sends them to the user's device, along with advice based on additional emotion data.

[0999] Step 11:

[1000] The device displays prediction results and emotion-based advice received from the server to the user, including peak times, recommended departure and arrival times, and emotion-based relaxation techniques.

[1001] Step 12:

[1002] The user can then adjust their departure plans based on the displayed information, decide whether to leave earlier or later based on recommended times to avoid crowds, and, if necessary, take emotional advice and incorporate relaxation techniques.

[1003] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user, as well as personalized advice based on their emotions.

[1004] Example 2

[1005] 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."

[1006] Current traffic prediction systems can suggest optimal departure and arrival times based on the user's input of departure and arrival locations and desired date and time, but they lack the ability to provide personalized advice that takes into account the user's individual feelings. Furthermore, data collection and pre-processing to improve traffic volume prediction accuracy are often insufficient. This can lead to lower user satisfaction and negatively impact the actual travel experience.

[1007] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1008] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather information, and event information from a predetermined data source, means for predicting traffic volume using a generative AI model based on the acquired data, means for calculating busy periods based on the prediction results and for calculating recommended departure and arrival times, means for analyzing the user's emotions, and means for notifying the user's terminal of the results of the analysis. This makes it possible to improve the accuracy of traffic predictions and provide personalized advice based on the user's emotions.

[1009] "User" means a person who uses the system to input the departure point, arrival point, desired date and time, and receives suggestions for optimal departure and arrival times.

[1010] The "starting point" is the location where the user begins their journey.

[1011] The "destination point" is the location where the user ends their journey.

[1012] A "desired date and time" is a specific date and time that a user desires to depart or arrive.

[1013] "Terminal" means a device used by a User to access the System and to enter and receive information.

[1014] A "server" is a computer system that receives, processes, and analyzes data sent from a user's terminal.

[1015] A "data source" is an information source that provides data such as traffic data, weather information, and event information.

[1016] "Traffic Data" means information about past and current traffic conditions.

[1017] "Weather information" refers to information about weather forecasts and weather conditions.

[1018] "Event information" is information about an event that occurs at a specific place and time.

[1019] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to predict traffic volume.

[1020] "Traffic volume forecasting" refers to predicting the degree of traffic congestion during a specific time period.

[1021] The "emotion engine" is a system that recognizes emotions by analyzing the user's voice data and facial expressions.

[1022] "Means of notification" refers to the method by which a server or terminal displays or conveys information to a user.

[1023] The "recommended departure time and recommended arrival time" are the optimal departure and arrival times suggested to avoid congestion.

[1024] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[1025] System configuration

[1026] This system consists of a terminal used by the user, a server that processes data, various data sources, and an emotion engine.

[1027] Hardware and software used

[1028] Device: A device that allows a user to input information. Examples include smartphones, tablets, and computers.

[1029] Server: A computer system that receives data and performs predictions and analysis. Example: High performance server (including cloud servers).

[1030] Database: A system for storing and managing data. Examples: MySQL, PostgreSQL.

[1031] Generative AI model: Artificial intelligence for traffic prediction. Example: Machine learning frameworks such as TensorFlow, PyTorch, etc.

[1032] Emotion engine: A system that analyzes a user's emotions. Examples: speech recognition engines and facial expression recognition software.

[1033] System Operation

[1034] User Action:

[1035] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input and clicks the "Register" button, the terminal sends this data to the server.

[1036] Server Action:

[1037] The server receives user input information sent from the device. It stores this information in a database. It then collects traffic data, weather information, and event information from specified data sources. This allows it to obtain the data necessary for prediction. The server preprocesses the collected data and converts it into a format that can be input into the generative AI model. It then cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[1038] The server then uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model uses past and current data to calculate how much traffic is expected at a particular time. The resulting prediction is a forecast of congestion at that time.

[1039] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[1040] Emotion Engine Processing:

[1041] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[1042] Terminal display:

[1043] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[1044] Specific examples

[1045] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather information, and more, and uses the generative AI model to make predictions.

[1046] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[1047] Examples of prompt statements

[1048] Enter the following prompt into the generative AI model:

[1049] The user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" on the date and time "December 24, 2023, 9:00 AM." What is the expected traffic congestion and what are the best departure and arrival times? Also, if the user feels "very stressed," what are some appropriate ways to relax?

[1050] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1051] Step 1:

[1052] Users launch the application and input their departure point, highway entrance, arrival point, highway exit, and desired date and time. Examples of input information include "Tokyo IC," "Nagoya Nishi IC," and "December 24, 2023, 9:00 AM."

[1053] Input: departure point, highway entrance, arrival point, highway exit, desired date and time.

[1054] Output: Input starting point, highway entrance, arrival point, highway exit, desired date and time.

[1055] Step 2:

[1056] When the user confirms the input and clicks the "Register" button, the device sends this data to the server. The device stores the input data in its internal memory and uses an API to send the data to the server.

[1057] Input: Data entered and confirmed by the user.

[1058] Output: The data sent to the server.

[1059] Step 3:

[1060] The server receives the user's input information sent from the terminal and stores it in a database, for example, using a database system such as MySQL.

[1061] Input: User input data sent from the terminal.

[1062] Output: User data stored in the database.

[1063] Step 4:

[1064] The server collects traffic data, weather information, and event information from predetermined data sources, including historical traffic data, current traffic conditions, weather forecasts, and event information. The collection process obtains real-time data via API.

[1065] Input: User information stored in the database.

[1066] Output: Collected traffic data, weather information, and event information.

[1067] Step 5:

[1068] The server performs preprocessing on the collected data, cleaning the data, filling in missing values, normalizing the data, and creating a dataset for efficient prediction. Specifically, it uses Python libraries (Pandas, NumPy, etc.) to format and fill in the data.

[1069] Input: Collected traffic data, weather information, and event information.

[1070] Output: A preprocessed dataset.

[1071] Step 6:

[1072] The server uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model calculates the expected traffic volume for a specific time period based on past and current data. The generative AI model uses TensorFlow and PyTorch.

[1073] Input: The preprocessed dataset.

[1074] Output: Traffic volume prediction results.

[1075] Step 7:

[1076] The server analyzes the prediction results obtained by the AI ​​model to identify busy times. It also calculates the optimal departure and arrival times to avoid congestion. For example, if the prediction results indicate that "it will be particularly busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00."

[1077] Input: Traffic prediction results.

[1078] Output: Busy times, best departure and arrival times.

[1079] Step 8:

[1080] When a user expresses emotions through voice input or facial expression recognition, the emotion engine analyzes the data. The device collects the user's voice data and sends it to the server. The emotion engine in the server analyzes the voice and facial expression data to infer the user's emotional state.

[1081] Input: User's voice data and facial expression data.

[1082] Output: Parsed emotional state.

[1083] Step 9:

[1084] The server then sends the calculated recommended departure and arrival times, as well as additional advice based on the user's emotions, to the user's device, which then displays these results to the user. Specifically, the results include customized advice such as peak times, recommended departure times (e.g., "8:00 AM departure or after 1:00 PM recommended"), and even the provision of relaxation music.

[1085] Inputs: Recommended departure time, arrival time, and sentiment-based advice.

[1086] Output: The results that are displayed to the user.

[1087] (Application example 2)

[1088] 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."

[1089] Conventional traffic prediction systems only provide prediction results based on traffic volume data and are unable to make suggestions that take into account the user's emotional state. This can result in users departing or arriving at times that make them feel stressed. Furthermore, providing only recommended travel times poses the problem of not being able to address the individual circumstances of each user. To solve this problem, a system that takes into account the user's emotional data and makes more personalized suggestions is needed.

[1090] 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.

[1091] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, means for notifying the user of the results to their terminal, means for collecting and analyzing emotional data from the user using a camera or voice input device in the vehicle, and means for providing personalized recommendations based on the analyzed emotional data. This reduces stress for the user and makes it possible to suggest optimal departure and arrival times according to the individual emotional state.

[1092] The "starting point" is where the user begins their journey.

[1093] "Destination" is the location where the user intends to end their journey.

[1094] "Desired date and time" means the specific date and time at which the user wishes to begin their journey.

[1095] "Data Source" means a source of information that provides traffic data, weather forecasts, and event information.

[1096] "Traffic data" refers to information about traffic flow and congestion at a given point in time.

[1097] A "weather forecast" is information that predicts future weather conditions.

[1098] "Event information" refers to information about events held in a specific area or on a specific date.

[1099] "Artificial intelligence" refers to computer programs or systems that simulate intelligent human behavior.

[1100] A "busy hour" is a specific time range during which traffic is expected to be very heavy.

[1101] "Recommended Departure Time" means the optimal departure time suggested to the user to avoid congestion.

[1102] "Recommended Arrival Time" refers to the optimal arrival time suggested to the user to avoid congestion.

[1103] "Emotional Data" refers to information about a user's emotional state.

[1104] A "camera" is a device that captures images and records or transmits the data.

[1105] A "voice input device" is a device that has the function of acquiring and analyzing voice data.

[1106] "Means of analysis" refers to the processes and systems that evaluate acquired data and extract meaningful information.

[1107] "Personalized suggestions" are suggestions that are customized based on the user's individual situation and emotional state.

[1108] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times based on the departure point, arrival point, and desired date and time entered by the user. It also collects and analyzes user emotion data to provide personalized advice.

[1109] System Overview

[1110] The system mainly consists of a user device and a server. Users can use their smartphone or in-car information system to input their departure and arrival points, desired date and time. This data is sent to the server, which then retrieves traffic data, weather forecasts, and event information from designated data sources.

[1111] Hardware and software used

[1112] Cameras and audio input devices: Used to collect user emotional data, such as in-car cameras and microphones.

[1113] EmotionEngine (tentative library name): Software that analyzes collected emotional data.

[1114] VehicleAPI (tentative API name): Software for controlling the navigation system of self-driving vehicles.

[1115] requests library: A software library used to communicate with the traffic congestion prediction server.

[1116] Details of data processing and calculation

[1117] The server receives the user's input of departure and arrival points, desired date and time, and based on this data, retrieves traffic data, weather forecasts, and event information from predetermined data sources.

[1118] The server then analyzes the data and uses an artificial intelligence model to predict traffic volume. Specifically, it predicts traffic volume for specific time periods based on past and current data. It then calculates peak times and calculates optimal departure and arrival times. This information is then sent to the user's device.

[1119] Use of user sentiment data

[1120] Additionally, the system uses in-car cameras and audio input devices to analyze the user's emotional state. EmotionEngine analyzes the user's voice and facial expressions to extract emotional data. Based on the results, recommended departure and arrival times are personalized. For example, if the user indicates high stress, the system will suggest playing relaxing music in the car.

[1121] Examples and prompts

[1122] As a specific example, suppose a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" and enters the date and time as "December 24, 2023, 9:00 AM." When the user registers this information, the server collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and other information, and uses the AI ​​model to make a prediction. If the prediction indicates that traffic will be particularly congested between 10:00 AM and 12:00 PM, the system calculates that "departing at 8:00 AM or after 1:00 PM is recommended." This information is notified and displayed on the user's device. Furthermore, if the user verbally says, "I'm feeling very stressed," the emotion engine determines that the user is feeling stressed and suggests relaxing background music and stress management measures.

[1123] An example prompt for a generative AI model might look something like this:

[1124] The user wishes to depart from Tokyo IC to Nagoya Nishi IC at 09:00 on December 24, 2023. Based on this information, calculate the optimal departure and arrival times using historical traffic data, current traffic conditions, and weather forecasts. Also, if the user's voice and facial expression data indicate that they are feeling stressed, suggest playing relaxing music.

[1125] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[1126] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1127] Step 1:

[1128] The user inputs the departure point, arrival point, desired date and time into the terminal. The terminal collects this data and sends it to the server when the registration button is pressed.

[1129] Input: Departure point, arrival point, desired date and time

[1130] Output: Sends input data to the server

[1131] How it works: The user uses a smartphone or in-car infotainment system to enter, confirm, and submit data.

[1132] Step 2:

[1133] The server receives user data sent from the terminal and makes requests to obtain traffic data, weather forecasts, and event information from predetermined data sources.

[1134] Input: User-entered departure point, arrival point, desired date and time

[1135] Output: Traffic data, weather forecast, event information

[1136] How it works: The server calls the appropriate APIs to collect the necessary traffic data, weather forecasts and event information.

[1137] Step 3:

[1138] The server preprocesses the acquired data and converts it into a format that can be input into an artificial intelligence model. It cleans the traffic data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[1139] Input: traffic data, weather forecast, event information

[1140] Output: Preprocessed dataset

[1141] What it does: Performs preprocessing steps such as cleaning data, imputing missing values, and normalizing it to convert it into a data format suitable for artificial intelligence models.

[1142] Step 4:

[1143] The server uses the preprocessed data to run an artificial intelligence model to predict traffic volume, calculating traffic volume for a specific time period based on past and current data.

[1144] Input: Preprocessed dataset

[1145] Output: Traffic volume prediction results

[1146] How it works: Traffic volume predictions are made using artificial intelligence models (machine learning algorithms).

[1147] Step 5:

[1148] Based on the traffic volume prediction results, the server identifies busy times and calculates recommended departure and arrival times, thereby generating an optimal travel plan.

[1149] Input: Traffic volume prediction results

[1150] Output: Recommended departure and arrival times

[1151] Operation: Analyzes busy times and calculates recommended times.

[1152] Step 6:

[1153] To collect user emotion data, the system uses cameras and voice input devices inside the car. The emotion engine analyzes the user's emotions and sends the data to a server.

[1154] Input: User's voice data, video data

[1155] Output: Emotion data as the analysis result

[1156] How it works: The device uses a camera and microphone to collect audio and video and perform sentiment analysis.

[1157] Step 7:

[1158] The server analyzes the emotional data and personalizes recommendations based on the user's emotional state, for example, suggesting playing relaxing music in the car if the user indicates high stress.

[1159] Input: Emotion data, recommended departure time and recommended arrival time

[1160] Output: Personalized recommendations

[1161] Behavior: Customize recommended time and additional advice based on sentiment data.

[1162] Step 8:

[1163] The server then sends the final recommendations and personalized advice to the user's device for display, allowing them to see suggestions tailored to their emotional state.

[1164] Input: Personalized recommendations

[1165] Output: Displayed recommended departure and arrival times, sentiment-based advice

[1166] Operation: The terminal displays and notifies the user of the results received from the server.

[1167] In this way, the present invention not only allows the user to know the best departure and arrival times, but also provides personalized advice according to their emotional state.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] [Fourth embodiment]

[1172] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1173] 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.

[1174] 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).

[1175] 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.

[1176] 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.

[1177] 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).

[1178] 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.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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."

[1185] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times by having the user input their departure point, destination, and desired date and time. This system is built around the user's terminal and a server.

[1186] User Actions

[1187] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[1188] Server Processing

[1189] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[1190] This system preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. After processes such as data cleaning, missing value completion, and normalization, a dataset for efficient prediction is created.

[1191] The server then uses the preprocessed data to run an artificial intelligence model, which uses machine learning algorithms to accurately predict traffic volume based on past and current data. The resulting prediction is how much traffic there will be for a given date and time, including the hours when it is expected to be particularly busy.

[1192] The server then calculates the optimal departure and arrival times based on the peak times. This calculation aims to select the most efficient time for the user to avoid the crowds. For example, if a particular time period is predicted to be peak, the server will recommend departure and arrival times that avoid that time period.

[1193] Terminal display

[1194] The calculated results are sent from the server to the user's device, where the device displays the congestion forecast results to the user. The results include peak times and recommended departure and arrival times to avoid the crowds. The user can use this information to adjust their schedule and choose the optimal departure time.

[1195] Specific examples

[1196] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make a prediction.

[1197] For example, if the prediction result is "It will be particularly busy between 10:00 and 12:00," the server calculates that "Departure at 08:00 or after 13:00 is recommended." This is sent to the terminal and displayed to the user, who can then make plans based on the recommended time displayed.

[1198] The above is a specific embodiment of the present invention, in which the elements of the user, terminal, and server work together to provide a means for efficiently avoiding congestion.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user launches the application and enters the departure point, highway entrance, arrival point, highway exit, desired date and time.

[1202] Step 2:

[1203] The user checks the input information and clicks the registration button. The terminal temporarily stores the input data within the device and prepares to send it to the server.

[1204] Step 3:

[1205] The terminal transmits the data entered by the user to the server, including the departure point, arrival point, desired date and time.

[1206] Step 4:

[1207] The server receives the user's input information sent from the terminal, and the received data is first stored in a database.

[1208] Step 5:

[1209] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[1210] Step 6:

[1211] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[1212] Step 7:

[1213] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic volume can be expected at a particular time of day.

[1214] Step 8:

[1215] The server analyzes the predictions of the AI ​​model to identify peak times of day, and calculates optimal departure and arrival times based on the identified peak times of day.

[1216] Step 9:

[1217] The server generates the calculated recommended departure time and recommended arrival time results and transmits them to the user's terminal.

[1218] Step 10:

[1219] The terminal displays the prediction results received from the server to the user, including the busiest times and recommended departure and arrival times to avoid those times.

[1220] Step 11:

[1221] Based on the displayed information, the user can adjust his / her departure schedule, for example, by deciding to depart during a less busy time or to arrive later.

[1222] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user.

[1223] Example 1

[1224] 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."

[1225] Congestion on expressways is a major source of stress for road users, making it difficult to plan trips. Choosing an appropriate departure time to avoid congestion is particularly important when planning travel or business. However, conventional systems have had difficulty comprehensively predicting traffic conditions, weather, and event information, and suggesting optimal departure and arrival times. Therefore, an efficient system that can predict highway congestion and suggest optimal departure and arrival times to users is needed.

[1226] 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.

[1227] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for preprocessing the acquired data and inputting it into an artificial intelligence model, means for predicting traffic volume using artificial intelligence, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, and means for transmitting the calculated results to the user's terminal and displaying them, thereby enabling the user to receive accurate congestion forecast information and select optimal departure and arrival times.

[1228] "Means for receiving the user's input of departure point, arrival point, desired date and time" refers to the interface that receives the information the user inputs through an application or website.

[1229] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to programs or functions for collecting the necessary data from external data providers or APIs.

[1230] "Means for preprocessing acquired data and inputting it into an artificial intelligence model" refers to a system that converts collected information into a format that can be used by an AI model through processes such as cleaning, missing value completion, and normalization.

[1231] "Means for predicting traffic volume using artificial intelligence" refers to a function that uses machine learning algorithms or other AI models to predict future traffic volume.

[1232] "Means for calculating congested time periods based on the prediction results and calculating recommended departure and arrival times" refers to a program or algorithm that analyzes the traffic volume prediction results from AI and derives optimal departure and arrival times to avoid congestion.

[1233] "Means for sending the calculated results to the user's device and displaying the results" refers to an interface that transfers the prediction results from the server to the user's device (smartphone or computer) and displays them in a format that the user can check.

[1234] This invention relates to a highway congestion prediction system, which predicts congestion and suggests optimal departure and arrival times when a user inputs their departure point, arrival point, and desired date and time. This system is built around the user's terminal and a server.

[1235] User Actions

[1236] The user first launches the application and inputs the departure and arrival points, as well as the desired date and time. This input information includes the planned highway entrance and exit, as well as the departure date and time. Once the user confirms the information and clicks the registration button, the device sends the input data to the server.

[1237] Server Processing

[1238] The server receives user input information sent from the device and stores it in a database. The server then collects traffic data, weather forecasts, and event information from designated data sources. This data is obtained by sending API requests.

[1239] Specifically, we collect information from the following data sources:

[1240] 1. Traffic Data: Specific traffic APIs (e.g., Google Maps API)

[1241] 2. Weather forecast: Japan Meteorological Agency API

[1242] 3. Event Information: Regional Event Calendar API

[1243] Data Preprocessing

[1244] The collected data undergoes the following preprocessing:

[1245] Cleaning: Removing invalid data and noise.

[1246] Imputing missing values: Filling incomplete data with guesses or other data.

[1247] Normalization: Aligning the scale of the data.

[1248] This puts the data in a format suitable for AI models.

[1249] Prediction using artificial intelligence models

[1250] The server then runs an AI model based on the preprocessed data, which uses a machine learning algorithm (e.g., a regression model) created using Python's Scikit-learn library, to predict future traffic volumes.

[1251] Calculating optimal departure and arrival times

[1252] The server calculates the optimal departure and arrival times based on the predictions made by the AI ​​model. For example, if the AI ​​model predicts that congestion will occur between 10:00 and 12:00, the server will recommend departing at 08:00 or after 13:00 to avoid that time period.

[1253] Terminal display

[1254] The calculation results are sent from the server to the user's device, which then displays the results received from the server to the user. These results include peak times and recommended departure and arrival times to avoid these peak times. The user can use this information to adjust their schedule.

[1255] Specific examples

[1256] For example, if a user wishes to travel from Tokyo IC to Nagoya Nishi IC, they can enter the date and time as "December 24, 2023, 9:00 AM." The device then sends this information to the server, which then collects traffic data from past Christmas Eves, current traffic conditions, and weather forecasts, and uses the AI ​​model to make a prediction.

[1257] If the prediction result is "particularly busy between 10:00 and 12:00," the server will recommend "departing at 08:00 or after 13:00." The calculated information is sent to the terminal, and the user is shown a message saying "recommended departure at 08:00 or after 13:00." The user can use this information to choose the optimal departure time.

[1258] Prompt Sentence Examples

[1259] Specific prompt examples:

[1260] "A user wants to travel from Tokyo IC to Nagoya Nishi IC. The departure date and time is December 24, 2023 at 9:00 AM. Please suggest the best departure time for this user based on past Christmas Eve traffic data, current traffic conditions, and weather forecasts."

[1261] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1262] Step 1:

[1263] The user launches the application on the terminal and inputs the departure point, arrival point, desired date and time.

[1264] Input: User-entered departure point, arrival point, desired date and time information

[1265] Output: Trip data entered into the terminal

[1266] Step 2:

[1267] Once the user confirms the information and clicks the registration button, the terminal sends the input data to the server.

[1268] Input: Travel data entered into the terminal

[1269] Output: Trip data transferred to the server

[1270] Step 3:

[1271] The server receives the user's input information sent from the terminal and stores it in a database.

[1272] Input: Travel data sent from the device

[1273] Output: Trip data stored in a database

[1274] Step 4:

[1275] The server then collects traffic data, weather forecasts, and event information from predetermined data sources.

[1276] Input: API request for a given data source

[1277] Output: Traffic data, weather forecast, and event information collected on the server

[1278] Step 5:

[1279] The server performs preprocessing on the collected data, which includes cleaning the data, imputing missing values, and normalizing it.

[1280] Input: Raw data collected

[1281] Output: Preprocessed data

[1282] Step 6:

[1283] The server runs an artificial intelligence model based on the preprocessed data to predict traffic volume.

[1284] Input: Preprocessed data

[1285] Output: Predicted traffic volume data

[1286] Step 7:

[1287] The server calculates the busy times based on the prediction results and calculates the recommended departure time and recommended arrival time.

[1288] Input: Predicted traffic data

[1289] Output: Calculated optimal departure and arrival times

[1290] Step 8:

[1291] The server sends the calculation results to the user's terminal.

[1292] Input: Calculated optimal departure and arrival times

[1293] Output: The calculation result sent to the user's device

[1294] Step 9:

[1295] The terminal displays the results received from the server to the user, who can then view the forecast information and suggested departure and arrival times.

[1296] Input: Calculation result sent from the server

[1297] Output: Displayed forecast information and recommended departure and arrival times

[1298] (Application example 1)

[1299] 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."

[1300] Conventional congestion prediction systems mainly rely on users checking traffic information before departure and planning departure times. However, autonomous vehicles are required to optimize routes in real time and avoid congestion. Furthermore, they must respond quickly to abnormal weather or sudden events. There is a problem with the lack of real-time congestion prediction and route suggestions for optimizing operations in autonomous vehicles.

[1301] 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.

[1302] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure times and recommended arrival times, and means for notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time, thereby enabling the autonomous vehicle to plan the optimal departure time and route that reflects traffic conditions in real time and reach the destination quickly and efficiently.

[1303] "User-entered departure point, arrival point, desired date and time" refers to the information in the system about the user's departure point, arrival point, and the specific date and time of desired travel.

[1304] "Means for obtaining traffic data, weather forecasts, and event information from predetermined data sources" refers to the functionality of a system, device, or software for obtaining current and historical traffic conditions, weather forecasts, and information about major events in a specific area from different data sources.

[1305] "Means for predicting traffic volume using artificial intelligence based on acquired data" refers to the function of analyzing collected traffic data, weather forecasts, and event information, and using artificial intelligence and machine learning algorithms to predict future traffic volume with high accuracy.

[1306] "Means for calculating busy times based on the prediction results and calculating recommended departure and arrival times" refers to a function that identifies which times are expected to be crowded based on prediction data generated by an artificial intelligence model and determines the optimal departure and arrival times.

[1307] "Means of notifying the results to the onboard system of the autonomous vehicle and optimizing the route in real time" refers to the function of sending the calculated congestion forecast results and recommended departure and arrival times to the onboard system of the autonomous vehicle and using that information to optimize the route in real time.

[1308] The present invention is a highway congestion prediction system for an autonomous vehicle. The system includes: means for receiving a departure point, arrival point, and desired date and time input by a user; means for acquiring traffic data, weather forecasts, and event information from predetermined data sources; means for predicting traffic volume based on the acquired data using artificial intelligence; means for calculating congestion times based on the prediction results and calculating recommended departure and arrival times; and means for notifying the autonomous vehicle's onboard system of the results and optimizing the route in real time.

[1309] System Program

[1310] The main components of the system are the user terminal, the server, and the autonomous vehicle's onboard system. The user terminal provides an interface for inputting the departure point, arrival point, desired date and time. The server collects and processes traffic data, weather forecasts, and event information, and uses an artificial intelligence model to predict congestion. The autonomous vehicle's onboard system receives the results sent from the server and optimizes the route in real time.

[1311] Processing Details

[1312] Hardware and Software

[1313] Hardware: On-board computer systems for autonomous vehicles

[1314] Software: Python, requests library, scikit-learn (to run machine learning models)

[1315] Data processing and calculation

[1316] 1. Data collection: The server receives the departure point, arrival point, desired date and time from the user's device, and then obtains traffic data, weather forecasts, and event information through API requests.

[1317] 2. Data preprocessing: Cleaning the collected data and converting it into a format suitable for artificial intelligence models, including imputing missing values ​​and normalizing the data.

[1318] 3. Artificial intelligence prediction: Based on preprocessed data, congestion predictions are made using a machine learning model using scikit-learn.

[1319] 4. Optimization: Based on the prediction results, the optimal departure and arrival times are calculated to avoid peak times. The results are sent to the autonomous vehicle's onboard system.

[1320] 5. Real-time notification and route optimization: Based on the information received, the onboard system will optimize routes and departure times in real time and display the results on the vehicle's display, allowing passengers to adjust their travel plans accordingly.

[1321] Specific examples

[1322] For example, if a user wishes to travel from "City Departure Point A" to "City Destination Point B," they enter the date and time as "December 24, 2023, 9:00 AM." The user's device sends this information to the server, which collects past data, current traffic conditions, and weather forecasts and uses an AI model to make a prediction. If the prediction indicates that "the route will be particularly congested between 10:00 AM and 12:00 PM," the server calculates that "it is recommended to depart at 8:00 AM or after 1:00 PM." This information is sent to the autonomous vehicle's onboard system, which notifies and optimizes the route in real time.

[1323] Prompt Sentence Examples

[1324] Use traffic data to suggest optimal departure times from the following origins to destinations:

[1325] Starting point: City starting point A

[1326] Arrival point: City arrival point B

[1327] Scheduled departure time: December 24, 2023, 9:00 AM

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] The user inputs the departure point, arrival point, desired date and time. This information is entered into an application on the device (smartphone, tablet, PC, etc.) used by the user. This input information includes the specific planned departure location, planned arrival location, and desired departure date and time. Once the input is complete, the user clicks the "Submit" button, which causes the device to send this information to the server.

[1331] Step 2:

[1332] The server receives the user's input information sent from the device and stores the information of the departure point, arrival point, desired date and time in a database.The server then sends an API request to collect traffic data, weather forecasts, and event information from predetermined data sources.This data is obtained from different data sources (traffic information API, weather information API, event information API, etc.).

[1333] Step 3:

[1334] The server preprocesses the collected traffic data, weather forecasts, and event information. Specifically, it performs data cleaning (noise removal and missing value imputation), normalization (standardizing the scale of the data), and feature engineering (generating new useful features). After preprocessing, the data is converted into a format suitable for artificial intelligence models. The input of this step is the collected raw data, and the output is preprocessed data.

[1335] Step 4:

[1336] The server uses the preprocessed data to predict traffic volume using an artificial intelligence model with machine learning libraries such as scikit-learn. In this prediction process, past traffic data, weather data, and event information are given as input, and future traffic volume is estimated based on this. The model is pre-trained and capable of making predictions in real time. The input for this step is the preprocessed data, and the output is the congestion prediction results.

[1337] Step 5:

[1338] Based on the congestion prediction results, the server identifies which time periods are expected to be congested. It then calculates the optimal departure and arrival times to avoid congestion. For example, if congestion is predicted for a specific time period, it will suggest departure and arrival times that avoid that time period. The input to this step is the congestion prediction results, and the output is recommended departure and arrival times.

[1339] Step 6:

[1340] The server sends the calculated results to the user's device and the autonomous vehicle's onboard system. The user's device displays the recommended departure and arrival times, and the autonomous vehicle's onboard system optimizes the route in real time. The input of this step is the recommended departure and arrival times and route information, and the output is notifications to the user's device and the onboard system.

[1341] Step 7:

[1342] The onboard system of the autonomous vehicle optimizes the route in real time based on the recommended time and route information received from the server. Passengers can check the optimized route information on the vehicle display and depart at the optimal time. The input of this step is the notification from the server, and the output is the optimized route information.

[1343] 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.

[1344] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[1345] User Actions

[1346] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input information and clicks the registration button, the terminal sends this data to the server.

[1347] Server Processing

[1348] The server receives the user's input information sent from the device, stores this information in a database, and then collects traffic data, weather forecasts, and event information from designated data sources, thereby obtaining the data necessary for prediction.

[1349] The server preprocesses the collected data and converts it into a format that can be input into an artificial intelligence model. It cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[1350] The server then uses the preprocessed data to run an artificial intelligence model to predict traffic volume. The AI ​​model calculates how much traffic is likely to occur at a particular time based on past and current data. The resulting prediction is a forecast of congestion during that time.

[1351] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[1352] Emotion engine processing

[1353] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[1354] Terminal display

[1355] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[1356] Specific examples

[1357] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and more, and uses the AI ​​model to make predictions.

[1358] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[1359] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[1360] The processing flow will be explained below.

[1361] Step 1:

[1362] The user launches the application and enters their starting point, highway entrance, arrival point, highway exit, desired date and time. If the emotion engine is enabled, voice input and a facial recognition camera capture the user's state.

[1363] Step 2:

[1364] The user confirms the input and clicks the registration button. The device temporarily stores the input data and prepares to send it to the server. Emotion data is also collected at the same time.

[1365] Step 3:

[1366] The device transmits the data and emotion data entered by the user to the server, including the departure point, arrival point, desired date and time, voice data, and image data.

[1367] Step 4:

[1368] The server receives the user's input information sent from the device and stores it in a database. Voice data and image data are also stored for analysis by the emotion engine.

[1369] Step 5:

[1370] The emotion engine analyzes the stored voice and image data to estimate the user's emotions. For example, it can use voice analysis to determine the user's stress level and emotional state.

[1371] Step 6:

[1372] The server collects the required data from predetermined data sources such as traffic data, weather forecasts, event information, etc. This collection is done through API requests.

[1373] Step 7:

[1374] The server preprocesses the collected data, which includes cleaning the data, imputing missing values, and normalizing the data.

[1375] Step 8:

[1376] The server then inputs the pre-processed data into an artificial intelligence model, which then predicts traffic volume. The AI ​​model uses past and current data to calculate how much traffic is likely to occur at a particular time.

[1377] Step 9:

[1378] The server analyzes the predictions of the AI ​​model to identify peak times. It also calculates optimal departure and arrival times, taking into account the user's emotional state. For example, it suggests recommended times for a stressed user to relax.

[1379] Step 10:

[1380] The server generates the calculated recommended departure and arrival times and sends them to the user's device, along with advice based on additional emotion data.

[1381] Step 11:

[1382] The device displays prediction results and emotion-based advice received from the server to the user, including peak times, recommended departure and arrival times, and emotion-based relaxation techniques.

[1383] Step 12:

[1384] The user can then adjust their departure plans based on the displayed information, decide whether to leave earlier or later based on recommended times to avoid crowds, and, if necessary, take emotional advice and incorporate relaxation techniques.

[1385] These are the specific processing steps of this system. Each step works together to provide the optimal departure and arrival times for the user, as well as personalized advice based on their emotions.

[1386] Example 2

[1387] 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."

[1388] Current traffic prediction systems can suggest optimal departure and arrival times based on the user's input of departure and arrival locations and desired date and time, but they lack the ability to provide personalized advice that takes into account the user's individual feelings. Furthermore, data collection and pre-processing to improve traffic volume prediction accuracy are often insufficient. This can lead to lower user satisfaction and negatively impact the actual travel experience.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1390] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather information, and event information from a predetermined data source, means for predicting traffic volume using a generative AI model based on the acquired data, means for calculating busy periods based on the prediction results and for calculating recommended departure and arrival times, means for analyzing the user's emotions, and means for notifying the user's terminal of the results of the analysis. This makes it possible to improve the accuracy of traffic predictions and provide personalized advice based on the user's emotions.

[1391] "User" means a person who uses the system to input the departure point, arrival point, desired date and time, and receives suggestions for optimal departure and arrival times.

[1392] The "starting point" is the location where the user begins their journey.

[1393] The "destination point" is the location where the user ends their journey.

[1394] A "desired date and time" is a specific date and time that a user desires to depart or arrive.

[1395] "Terminal" means a device used by a User to access the System and to enter and receive information.

[1396] A "server" is a computer system that receives, processes, and analyzes data sent from a user's terminal.

[1397] A "data source" is an information source that provides data such as traffic data, weather information, and event information.

[1398] "Traffic Data" means information about past and current traffic conditions.

[1399] "Weather information" refers to information about weather forecasts and weather conditions.

[1400] "Event information" is information about an event that occurs at a specific place and time.

[1401] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to predict traffic volume.

[1402] "Traffic volume forecasting" refers to predicting the degree of traffic congestion during a specific time period.

[1403] The "emotion engine" is a system that recognizes emotions by analyzing the user's voice data and facial expressions.

[1404] "Means of notification" refers to the method by which a server or terminal displays or conveys information to a user.

[1405] The "recommended departure time and recommended arrival time" are the optimal departure and arrival times suggested to avoid congestion.

[1406] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times by inputting the departure point, arrival point, desired date and time. Furthermore, it can provide more personalized advice by combining it with an emotion engine that recognizes the user's emotions. This system is built around the user's terminal and a server.

[1407] System configuration

[1408] This system consists of a terminal used by the user, a server that processes data, various data sources, and an emotion engine.

[1409] Hardware and software used

[1410] Device: A device that allows a user to input information. Examples include smartphones, tablets, and computers.

[1411] Server: A computer system that receives data and performs predictions and analysis. Example: High performance server (including cloud servers).

[1412] Database: A system for storing and managing data. Examples: MySQL, PostgreSQL.

[1413] Generative AI model: Artificial intelligence for traffic prediction. Example: Machine learning frameworks such as TensorFlow, PyTorch, etc.

[1414] Emotion engine: A system that analyzes a user's emotions. Examples: speech recognition engines and facial expression recognition software.

[1415] System Operation

[1416] User Action:

[1417] The user starts the application and inputs the departure point, highway entrance, arrival point, highway exit, desired date and time. This input information includes the planned departure date and time, highway entrance and exit. When the user confirms the input and clicks the "Register" button, the terminal sends this data to the server.

[1418] Server Action:

[1419] The server receives user input information sent from the device. It stores this information in a database. It then collects traffic data, weather information, and event information from specified data sources. This allows it to obtain the data necessary for prediction. The server preprocesses the collected data and converts it into a format that can be input into the generative AI model. It then cleans the data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[1420] The server then uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model uses past and current data to calculate how much traffic is expected at a particular time. The resulting prediction is a forecast of congestion at that time.

[1421] The server analyzes the prediction results, identifies busy times, and calculates optimal departure and arrival times to avoid congestion, allowing it to provide optimal advice to users.

[1422] Emotion Engine Processing:

[1423] Furthermore, this system is equipped with an emotion engine, which recognizes emotions by analyzing the user's voice input and facial expressions. For example, if a user inputs a comment expressing their emotions into the app, the device collects the voice data and the emotion engine analyzes it. This allows the system to determine whether the user is feeling nervous or stressed.

[1424] Terminal display:

[1425] The server then sends the calculated recommended departure and arrival times, along with additional advice based on the user's emotions, to the user's device, which then displays these results to the user. These include information on busy times, recommended departure and arrival times to avoid crowds, and customized advice based on the user's emotions.

[1426] Specific examples

[1427] For example, if a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC," they can enter the date and time as "December 24, 2023, 9:00 AM." Once the user registers this information, the device sends the data to the server. The server then collects traffic data from past Christmas Eves, current traffic conditions, weather information, and more, and uses the generative AI model to make predictions.

[1428] For example, if the prediction result is "It will be especially busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00." This is sent to the user's device and displayed. Also, if the user says aloud, "I'm so stressed," the emotion engine will determine that the user is feeling stressed and will suggest relaxing background music and other stress management measures.

[1429] Examples of prompt statements

[1430] Enter the following prompt into the generative AI model:

[1431] The user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" on the date and time "December 24, 2023, 9:00 AM." What is the expected traffic congestion and what are the best departure and arrival times? Also, if the user feels "very stressed," what are some appropriate ways to relax?

[1432] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1433] Step 1:

[1434] Users launch the application and input their departure point, highway entrance, arrival point, highway exit, and desired date and time. Examples of input information include "Tokyo IC," "Nagoya Nishi IC," and "December 24, 2023, 9:00 AM."

[1435] Input: departure point, highway entrance, arrival point, highway exit, desired date and time.

[1436] Output: Input starting point, highway entrance, arrival point, highway exit, desired date and time.

[1437] Step 2:

[1438] When the user confirms the input and clicks the "Register" button, the device sends this data to the server. The device stores the input data in its internal memory and uses an API to send the data to the server.

[1439] Input: Data entered and confirmed by the user.

[1440] Output: The data sent to the server.

[1441] Step 3:

[1442] The server receives the user's input information sent from the terminal and stores it in a database, for example, using a database system such as MySQL.

[1443] Input: User input data sent from the terminal.

[1444] Output: User data stored in the database.

[1445] Step 4:

[1446] The server collects traffic data, weather information, and event information from predetermined data sources, including historical traffic data, current traffic conditions, weather forecasts, and event information. The collection process obtains real-time data via API.

[1447] Input: User information stored in the database.

[1448] Output: Collected traffic data, weather information, and event information.

[1449] Step 5:

[1450] The server performs preprocessing on the collected data, cleaning the data, filling in missing values, normalizing the data, and creating a dataset for efficient prediction. Specifically, it uses Python libraries (Pandas, NumPy, etc.) to format and fill in the data.

[1451] Input: Collected traffic data, weather information, and event information.

[1452] Output: A preprocessed dataset.

[1453] Step 6:

[1454] The server uses the preprocessed data to run a generative AI model to predict traffic volume. The AI ​​model calculates the expected traffic volume for a specific time period based on past and current data. The generative AI model uses TensorFlow and PyTorch.

[1455] Input: The preprocessed dataset.

[1456] Output: Traffic volume prediction results.

[1457] Step 7:

[1458] The server analyzes the prediction results obtained by the AI ​​model to identify busy times. It also calculates the optimal departure and arrival times to avoid congestion. For example, if the prediction results indicate that "it will be particularly busy between 10:00 and 12:00," the server calculates that "it is recommended to depart at 08:00 or after 13:00."

[1459] Input: Traffic prediction results.

[1460] Output: Busy times, best departure and arrival times.

[1461] Step 8:

[1462] When a user expresses emotions through voice input or facial expression recognition, the emotion engine analyzes the data. The device collects the user's voice data and sends it to the server. The emotion engine in the server analyzes the voice and facial expression data to infer the user's emotional state.

[1463] Input: User's voice data and facial expression data.

[1464] Output: Parsed emotional state.

[1465] Step 9:

[1466] The server then sends the calculated recommended departure and arrival times, as well as additional advice based on the user's emotions, to the user's device, which then displays these results to the user. Specifically, the results include customized advice such as peak times, recommended departure times (e.g., "8:00 AM departure or after 1:00 PM recommended"), and even the provision of relaxation music.

[1467] Inputs: Recommended departure time, arrival time, and sentiment-based advice.

[1468] Output: The results that are displayed to the user.

[1469] (Application example 2)

[1470] 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."

[1471] Conventional traffic prediction systems only provide prediction results based on traffic volume data and are unable to make suggestions that take into account the user's emotional state. This can result in users departing or arriving at times that make them feel stressed. Furthermore, providing only recommended travel times poses the problem of not being able to address the individual circumstances of each user. To solve this problem, a system that takes into account the user's emotional data and makes more personalized suggestions is needed.

[1472] 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.

[1473] In this invention, the server includes means for receiving the departure point, arrival point, and desired date and time input by the user, means for acquiring traffic data, weather forecasts, and event information from predetermined data sources, means for predicting traffic volume using artificial intelligence based on the acquired data, means for calculating busy periods based on the prediction results and calculating recommended departure and arrival times, means for notifying the user of the results to their terminal, means for collecting and analyzing emotional data from the user using a camera or voice input device in the vehicle, and means for providing personalized recommendations based on the analyzed emotional data. This reduces stress for the user and makes it possible to suggest optimal departure and arrival times according to the individual emotional state.

[1474] The "starting point" is where the user begins their journey.

[1475] "Destination" is the location where the user intends to end their journey.

[1476] "Desired date and time" means the specific date and time at which the user wishes to begin their journey.

[1477] "Data Source" means a source of information that provides traffic data, weather forecasts, and event information.

[1478] "Traffic data" refers to information about traffic flow and congestion at a given point in time.

[1479] A "weather forecast" is information that predicts future weather conditions.

[1480] "Event information" refers to information about events held in a specific area or on a specific date.

[1481] "Artificial intelligence" refers to computer programs or systems that simulate intelligent human behavior.

[1482] A "busy hour" is a specific time range during which traffic is expected to be very heavy.

[1483] "Recommended Departure Time" means the optimal departure time suggested to the user to avoid congestion.

[1484] "Recommended Arrival Time" refers to the optimal arrival time suggested to the user to avoid congestion.

[1485] "Emotional Data" refers to information about a user's emotional state.

[1486] A "camera" is a device that captures images and records or transmits the data.

[1487] A "voice input device" is a device that has the function of acquiring and analyzing voice data.

[1488] "Means of analysis" refers to the processes and systems that evaluate acquired data and extract meaningful information.

[1489] "Personalized suggestions" are suggestions that are customized based on the user's individual situation and emotional state.

[1490] This invention relates to a system that predicts traffic congestion and suggests optimal departure and arrival times based on the departure point, arrival point, and desired date and time entered by the user. It also collects and analyzes user emotion data to provide personalized advice.

[1491] System Overview

[1492] The system mainly consists of a user device and a server. Users can use their smartphone or in-car information system to input their departure and arrival points, desired date and time. This data is sent to the server, which then retrieves traffic data, weather forecasts, and event information from designated data sources.

[1493] Hardware and software used

[1494] Cameras and audio input devices: Used to collect user emotional data, such as in-car cameras and microphones.

[1495] EmotionEngine (tentative library name): Software that analyzes collected emotional data.

[1496] VehicleAPI (tentative API name): Software for controlling the navigation system of self-driving vehicles.

[1497] requests library: A software library used to communicate with the traffic congestion prediction server.

[1498] Details of data processing and calculation

[1499] The server receives the user's input of departure and arrival points, desired date and time, and based on this data, retrieves traffic data, weather forecasts, and event information from predetermined data sources.

[1500] The server then analyzes the data and uses an artificial intelligence model to predict traffic volume. Specifically, it predicts traffic volume for specific time periods based on past and current data. It then calculates peak times and calculates optimal departure and arrival times. This information is then sent to the user's device.

[1501] Use of user sentiment data

[1502] Additionally, the system uses in-car cameras and audio input devices to analyze the user's emotional state. EmotionEngine analyzes the user's voice and facial expressions to extract emotional data. Based on the results, recommended departure and arrival times are personalized. For example, if the user indicates high stress, the system will suggest playing relaxing music in the car.

[1503] Examples and prompts

[1504] As a specific example, suppose a user wishes to travel from "Tokyo IC" to "Nagoya Nishi IC" and enters the date and time as "December 24, 2023, 9:00 AM." When the user registers this information, the server collects traffic data from past Christmas Eves, current traffic conditions, weather forecasts, and other information, and uses the AI ​​model to make a prediction. If the prediction indicates that traffic will be particularly congested between 10:00 AM and 12:00 PM, the system calculates that "departing at 8:00 AM or after 1:00 PM is recommended." This information is notified and displayed on the user's device. Furthermore, if the user verbally says, "I'm feeling very stressed," the emotion engine determines that the user is feeling stressed and suggests relaxing background music and stress management measures.

[1505] An example prompt for a generative AI model might look something like this:

[1506] The user wishes to depart from Tokyo IC to Nagoya Nishi IC at 09:00 on December 24, 2023. Based on this information, calculate the optimal departure and arrival times using historical traffic data, current traffic conditions, and weather forecasts. Also, if the user's voice and facial expression data indicate that they are feeling stressed, suggest playing relaxing music.

[1507] The above is a specific embodiment of the present invention, in which the user, terminal, server, and emotion engine work together to provide the optimal departure and arrival timing for the user and provide personalized advice based on emotions.

[1508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1509] Step 1:

[1510] The user inputs the departure point, arrival point, desired date and time into the terminal. The terminal collects this data and sends it to the server when the registration button is pressed.

[1511] Input: Departure point, arrival point, desired date and time

[1512] Output: Sends input data to the server

[1513] How it works: The user uses a smartphone or in-car infotainment system to enter, confirm, and submit data.

[1514] Step 2:

[1515] The server receives user data sent from the terminal and makes requests to obtain traffic data, weather forecasts, and event information from predetermined data sources.

[1516] Input: User-entered departure point, arrival point, desired date and time

[1517] Output: Traffic data, weather forecast, event information

[1518] How it works: The server calls the appropriate APIs to collect the necessary traffic data, weather forecasts and event information.

[1519] Step 3:

[1520] The server preprocesses the acquired data and converts it into a format that can be input into an artificial intelligence model. It cleans the traffic data, fills in missing values, normalizes the data, and creates a dataset for efficient prediction.

[1521] Input: traffic data, weather forecast, event information

[1522] Output: Preprocessed dataset

[1523] What it does: Performs preprocessing steps such as cleaning data, imputing missing values, and normalizing it to convert it into a data format suitable for artificial intelligence models.

[1524] Step 4:

[1525] The server uses the preprocessed data to run an artificial intelligence model to predict traffic volume, calculating traffic volume for a specific time period based on past and current data.

[1526] Input: Preprocessed dataset

[1527] Output: Traffic volume prediction results

[1528] How it works: Traffic volume predictions are made using artificial intelligence models (machine learning algorithms).

[1529] Step 5:

[1530] Based on the traffic volume prediction results, the server identifies busy times and calculates recommended departure and arrival times, thereby generating an optimal travel plan.

[1531] Input: Traffic volume prediction results

[1532] Output: Recommended departure and arrival times

[1533] Operation: Analyzes busy times and calculates recommended times.

[1534] Step 6:

[1535] To collect user emotion data, the system uses cameras and voice input devices inside the car. The emotion engine analyzes the user's emotions and sends the data to a server.

[1536] Input: User's voice data, video data

[1537] Output: Emotion data as the analysis result

[1538] How it works: The device uses a camera and microphone to collect audio and video and perform sentiment analysis.

[1539] Step 7:

[1540] The server analyzes the emotional data and personalizes recommendations based on the user's emotional state, for example, suggesting playing relaxing music in the car if the user indicates high stress.

[1541] Input: Emotion data, recommended departure time and recommended arrival time

[1542] Output: Personalized recommendations

[1543] Behavior: Customize recommended time and additional advice based on sentiment data.

[1544] Step 8:

[1545] The server then sends the final recommendations and personalized advice to the user's device for display, allowing them to see suggestions tailored to their emotional state.

[1546] Input: Personalized recommendations

[1547] Output: Displayed recommended departure and arrival times, sentiment-based advice

[1548] Operation: The terminal displays and notifies the user of the results received from the server.

[1549] In this way, the present invention not only allows the user to know the best departure and arrival times, but also provides personalized advice according to their emotional state.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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).

[1557] 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.

[1558] 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."

[1559] 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.

[1560] 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).

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] The following is further disclosed regarding the above embodiment.

[1572] (Claim 1)

[1573] means for receiving a user-entered departure point, arrival point, desired date and time;

[1574] means for obtaining traffic data, weather forecasts, and event information from predetermined data sources;

[1575] A means of predicting traffic volume using artificial intelligence based on the acquired data;

[1576] means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time;

[1577] means for notifying the user of the result;

[1578] A system including:

[1579] (Claim 2)

[1580] 10. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

[1581] (Claim 3)

[1582] 10. The system of claim 1, wherein the artificial intelligence model comprises a machine learning algorithm.

[1583] "Example 1"

[1584] (Claim 1)

[1585] means for receiving a user-entered departure point, arrival point, desired date and time;

[1586] means for obtaining traffic data, weather forecasts, and event information from predetermined data sources;

[1587] A means for pre-processing the acquired data and inputting it into an artificial intelligence model;

[1588] A means for predicting traffic volume using artificial intelligence;

[1589] means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time;

[1590] means for transmitting the calculated result to a user's terminal and displaying the result;

[1591] A system including:

[1592] (Claim 2)

[1593] 10. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

[1594] (Claim 3)

[1595] 10. The system of claim 1, wherein the artificial intelligence model comprises a machine learning algorithm.

[1596] "Application Example 1"

[1597] (Claim 1)

[1598] means for receiving a user-entered departure point, arrival point, desired date and time;

[1599] means for acquiring traffic data, weather forecasts, and event information from predetermined data sources;

[1600] A means of predicting traffic volume using artificial intelligence based on the acquired data;

[1601] means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time;

[1602] A means to communicate the results to the autonomous vehicle's onboard systems and optimize the route in real time; and

[1603] A system including:

[1604] (Claim 2)

[1605] 10. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

[1606] (Claim 3)

[1607] 10. The system of claim 1, wherein the artificial intelligence model comprises a machine learning algorithm.

[1608] "Example 2: Combining Emotion Engines"

[1609] (Claim 1)

[1610] means for receiving a user-entered departure point, arrival point, desired date and time;

[1611] means for acquiring traffic data, weather information, and event information from predetermined data sources;

[1612] A means of predicting traffic volume using a generative AI model based on the acquired data;

[1613] means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time;

[1614] A means of analyzing user emotions,

[1615] means for notifying the user of the results and analysis results;

[1616] A system including:

[1617] (Claim 2)

[1618] 10. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

[1619] (Claim 3)

[1620] 10. The system of claim 1, wherein the generative AI model comprises a machine learning algorithm.

[1621] "Application example 2 when combining emotion engines"

[1622] (Claim 1)

[1623] means for receiving a user-entered departure point, arrival point, desired date and time;

[1624] means for obtaining traffic data, weather forecasts, and event information from predetermined data sources;

[1625] A means of predicting traffic volume using artificial intelligence based on the acquired data;

[1626] means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time;

[1627] means for notifying the user of the result;

[1628] A means for collecting and analyzing user emotion data using a camera and a voice input device in the vehicle;

[1629] A means for providing personalized recommendations based on the analyzed sentiment data; and

[1630] A system including:

[1631] (Claim 2)

[1632] 10. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

[1633] (Claim 3)

[1634] 10. The system of claim 1, wherein the artificial intelligence model comprises a machine learning algorithm. [Explanation of symbols]

[1635] 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. means for receiving a user-entered departure point, arrival point, desired date and time; means for obtaining traffic data, weather forecasts, and event information from predetermined data sources; A means of predicting traffic volume using artificial intelligence based on the acquired data; means for calculating a time period when congestion will occur based on the prediction result, and calculating a recommended departure time and a recommended arrival time; means for notifying the user of the result; A system including:

2. 2. The system of claim 1, wherein the traffic data includes historical data, current data, and weather condition data.

3. The system of claim 1 , wherein the artificial intelligence model comprises a machine learning algorithm.

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