System

Abstract

A system is provided.SOLUTION: A system, comprising: means for performing congestion prediction based on traffic information; means for obtaining weather information and calendar information; means for calculating a route based on past congestion information; means for generating an optimal route and a detour route based on predicted congestion data; means for sending the generated route information to a terminal; and means for obtaining real-time congestion information and updating the route.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Due to the effects of the COVID-19 pandemic, people are going out more frequently, resulting in an increasing number of cases of people getting caught in traffic jams. This situation causes time loss, reduced fuel efficiency, and has a negative impact on the environment. Furthermore, the lack of optimal routes that properly reflect real-time traffic congestion information reduces convenience for drivers. The objective of this invention is to provide optimal routes that avoid traffic jams, thereby achieving efficient use of time and consideration for the environment. [Means for solving the problem]

[0005] The present invention is a system including: a means for predicting congestion based on traffic information; a means for acquiring weather information and calendar information; a means for calculating a route based on past congestion information; a means for generating an optimal route and a detour route based on the predicted congestion data; a means for transmitting the generated route information to a terminal; and a means for acquiring real-time congestion information and updating the route. The system further includes a means for calculating the probability of congestion occurring for each time period based on the predicted congestion data and weather information, and a means for recalculating the required time for the detour route based on the real-time congestion information. This allows the user to reach their destination via the optimal route while avoiding congestion.

[0006] "Traffic information" refers to real-time or past data on road traffic, such as road congestion, road closures, and accident information.

[0007] "Traffic congestion forecasting" is the prediction of road congestion conditions at specific times based on data such as past traffic information, weather forecasts, and event information.

[0008] "Weather information" refers to weather information relating to the date and time of departure and destination, including precipitation, temperature, wind speed, and the like.

[0009] "Calendar information" is information such as national holidays, elementary and junior high school holidays, and seasonal events at specific dates and times.

[0010] "Past congestion information" is data relating to the past state of road traffic congestion at a specific date, time and location.

[0011] "Route calculation" refers to calculating the route from a starting point to a destination, including the required time and distance.

[0012] The "optimal route" refers to the route from the departure point to the destination that takes the shortest time.

[0013] A "detour route" is an alternative route that deviates from the normal route to avoid obstacles such as traffic jams or accidents.

[0014] "Generation" means creating new data or results based on given information.

[0015] A "terminal" is a computing device used by a user, such as a smartphone or a car navigation system.

[0016] "Real-time information" is the latest information that is being collected and provided in the present tense.

[0017] "Travel time" is the time required to travel from the departure point to the destination.

[0018] "Calculation" means deriving results through calculation or analysis. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a system that enables users to reach their destination while avoiding traffic jams, and is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"). This system has the function of calculating the optimal route based on traffic information, weather information, calendar information, and past traffic jam information, and updating the route to reflect real-time information.

[0041] Program processing overview

[0042] Server-side processing

[0043] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route.

[0044] 1. Data Collection

[0045] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[0046] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0047] The server searches and retrieves information on traffic congestion that occurred under similar conditions in the past from a traffic database.

[0048] 2. Traffic congestion prediction

[0049] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion for a specified date and time.

[0050] 3. Route Generation

[0051] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[0052] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[0053] 4. Sending the results

[0054] The server transmits the generated route information to the terminal.

[0055] Terminal side processing

[0056] The device displays the route information received from the server, provides navigation, and updates the route based on real-time traffic congestion information.

[0057] 1. Data Entry

[0058] The user inputs the departure point, destination, and departure date and time into the terminal.

[0059] The terminal transmits the input data to the server.

[0060] 2. Route display

[0061] The terminal displays the optimum route and detour route received from the server on a map screen.

[0062] It also displays the travel times for the main route and alternate routes, giving users a choice.

[0063] 3. Real-time updates

[0064] The device obtains real-time congestion information from traffic information services while driving.

[0065] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[0066] The terminal displays the updated route information and notifies the user.

[0067] User operation

[0068] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination along the new route.

[0069] Specific examples

[0070] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0071] 1. Data Collection

[0072] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0073] The terminal transmits this data to the server.

[0074] 2. Traffic congestion prediction and route generation

[0075] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[0076] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[0077] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[0078] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[0079] 3. Sending and displaying results

[0080] The server transmits the generated route information to the terminal.

[0081] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0082] 4. Real-time updates and navigation

[0083] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0084] The terminal acquires traffic congestion information in real time and sends it to the server.

[0085] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0086] The device notifies the user of the updated route information and displays it on the map screen.

[0087] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[0088] The processing flow will be explained below.

[0089] Step 1: Data entry (user, device)

[0090] The user inputs the departure point, destination, and departure date and time into the terminal.

[0091] The terminal formats the entered data and sends it to the server.

[0092] Step 2: Collecting weather and calendar information (server)

[0093] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[0094] The server uses a calendar API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[0095] Step 3: Obtaining past traffic congestion data (server)

[0096] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0097] Step 4: Traffic congestion prediction (server)

[0098] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[0099] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[0100] Step 5: Generate Route Information (Server)

[0101] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[0102] Step 6: Sending Route Information (Server)

[0103] The server transmits the generated route information to the terminal.

[0104] Step 7: View Route Information (Device)

[0105] The terminal draws the optimum route and detour route received from the server on a map screen.

[0106] The travel times for the main route and the detour route are also displayed to the user.

[0107] Step 8: Navigation Start (User, Device)

[0108] The user starts the navigation function of the terminal and drives the car.

[0109] The terminal guides the user along the optimum route.

[0110] Step 9: Obtaining real-time traffic congestion information (device)

[0111] The device obtains traffic congestion information from the traffic information service in real time while driving.

[0112] Step 10: Route recalculation (terminal, server)

[0113] The terminal transmits the acquired real-time traffic congestion information to the server.

[0114] The server recalculates route information based on real-time data.

[0115] Step 11: Sending and displaying updated routes (server, terminal)

[0116] The server sends the new route information to the terminal.

[0117] The terminal notifies the user of the updated route information and reflects it on the map screen.

[0118] Step 12: User arrives at destination

[0119] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[0120] This series of steps allows users to avoid traffic jams and reach their destination efficiently.

[0121] Example 1

[0122] 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."

[0123] Conventional navigation systems have difficulty using real-time traffic congestion information to provide users with the optimal route. They also lack the functionality to predict traffic conditions using weather and calendar information and provide users with the optimal route. Furthermore, they are unable to provide detailed traffic congestion predictions using past traffic congestion information based on the departure point, destination, and departure date and time.

[0124] 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.

[0125] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and date and time information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted traffic data, means for transmitting the generated route information to a device, means for acquiring real-time traffic information and updating the route, means for a user to input a departure point, a destination point, and a departure time, means for displaying the optimal route and the detour route on a map screen, means for acquiring real-time traffic information from a traffic information service while driving and updating the route, and means for displaying the route information generated in real time on a map screen and notifying the user. This makes it possible to provide the user with an optimal route and a detour route based on real-time information.

[0126] "Traffic information" refers to data relating to traffic conditions, including congestion, accidents, road construction, traffic volume, and the like.

[0127] "Traffic congestion forecast" is the process of predicting road congestion at a specified date and time based on various data.

[0128] "Weather information" refers to data related to weather forecasts, including temperature, precipitation, wind speed, and the like.

[0129] "Date and time information" is data related to a calendar, and includes information about a specific date and time.

[0130] "Historical traffic information" refers to previously recorded traffic data, including traffic performance under specific conditions and dates.

[0131] A "route" is a route from a starting point to a destination, and includes a main route and a detour route.

[0132] An "optimal route" is the best route calculated taking into account various conditions (travel time, distance, congestion, etc.).

[0133] A "detour route" is an alternative route that is chosen when the normal route is difficult due to traffic congestion or other reasons.

[0134] "Device" refers to the terminal used by the user, including smartphones, car navigation systems, etc.

[0135] "Real-time information" means instantly updated data about current traffic conditions.

[0136] "User" refers to the person who operates and uses this system.

[0137] The "map screen" is a visual interface that displays route information, current location, etc.

[0138] "Traffic Information Service" means an external information platform that provides real-time traffic conditions.

[0139] The present invention is a system that enables users to avoid traffic jams and reach their destinations based on traffic information, weather information, date and time information, and past traffic information. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals").

[0140] Hardware and Software

[0141] server:

[0142] The server uses the traffic forecast database, weather information API, date and time information API, and traffic information API to collect and analyze various data based on user requests. Specifically, it uses the OpenWeatherMap API to obtain weather information, the Google Calendar API to obtain date and time information, and the HERE Traffic API to obtain past traffic information.

[0143] Device:

[0144] The terminal provides an interface for the user to input the starting point, destination point, and departure time, and displays the route information received from the server on a map screen. The terminal also obtains traffic data in real time from a traffic information service (e.g., Google Maps API) and sends it to the server.

[0145] User:

[0146] The user inputs the departure point, destination, and departure date and time into the device and refers to the information on the optimal route and detour route sent from the server. If the device notifies the user of route updates in real time while driving, the user follows the instructions and heads to the destination on the new route.

[0147] Data processing and calculation

[0148] Data collection:

[0149] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[0150] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0151] The server searches and retrieves traffic information that occurred under similar conditions in the past from a traffic database.

[0152] Traffic congestion forecast:

[0153] Based on the collected weather information, calendar information, and past traffic information, the server uses a traffic prediction algorithm to predict congestion at a specified date and time.

[0154] Route Generation:

[0155] The server generates the optimal route from the departure point to the destination and a detour route based on the predicted traffic data, taking into account various conditions (travel time, distance, road type, etc.) to avoid traffic congestion.

[0156] Specific examples

[0157] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0158] Data collection:

[0159] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0160] The terminal transmits this data to the server.

[0161] Weather information, calendar information, traffic forecast:

[0162] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from a weather forecast API (e.g., OpenWeatherMap API).

[0163] Obtain information from the Google Calendar API about holidays on the relevant dates and events that may attract a large number of tourists.

[0164] The HERE Traffic API is used to obtain traffic data from similar conditions in the past and predict the occurrence of traffic congestion.

[0165] Route Generation:

[0166] Based on the predicted data, the server generates the main route (e.g., expressway) and detour route (e.g., general road) and calculates the required travel time.

[0167] Send and view results:

[0168] The server transmits the generated route information to the terminal.

[0169] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0170] Real-time updates:

[0171] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0172] While driving, the device obtains real-time traffic information from the traffic information service and transmits it to the server.

[0173] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0174] The device notifies the user of the updated route information and displays it on the map screen.

[0175] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[0176] Example prompts to input to the generative AI model

[0177] "I want to create a system that calculates the optimal driving route to avoid traffic jams. I'm thinking of using weather forecast APIs, calendar information APIs, and traffic information APIs, but I'd like to know how to collect the data, calculate the route, and notify the user."

[0178] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0179] Step 1: Data entry

[0180] The user inputs the departure point, destination, and departure date and time into the terminal. For example, the user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time. This input data is sent from the terminal to the server.

[0181] Input: Departure point, destination, departure date and time

[0182] Output: The input data sent to the server

[0183] Step 2: Get weather information

[0184] The server uses weather information APIs to obtain weather information for the specified date and time near the departure and destination locations. For example, the server uses the OpenWeatherMap API to obtain detailed weather forecasts (temperature, precipitation, wind speed, etc.) for Tokyo and Hakone.

[0185] Input: Departure point, destination, departure date and time

[0186] Output: Weather information (temperature, precipitation, wind speed, etc.)

[0187] Step 3: Get Calendar Information

[0188] The server uses a date and time information API to obtain information about holidays and events for the specified date. For example, it uses the Google Calendar API to obtain information about holidays, school holidays, and other events for the specified date.

[0189] Input: Departure date and time

[0190] Output: Calendar information (holidays, school vacations, event information, etc.)

[0191] Step 4: Obtaining historical traffic information

[0192] The server uses a traffic information API to obtain traffic data that occurred under similar conditions in the past. For example, it uses the HERE Traffic API to obtain data on traffic conditions similar to the specified date and time and conditions.

[0193] Input: Departure point, destination, departure date and time

[0194] Output: Past traffic information

[0195] Step 5: Traffic congestion prediction

[0196] The server uses a traffic prediction algorithm to predict congestion for a specified date and time based on collected weather, calendar, and past traffic information, and generates forecast data on which roads will be congested at which times.

[0197] Input: Weather information, calendar information, past traffic information

[0198] Output: Traffic congestion forecast data (predicted congestion situation)

[0199] Step 6: Route Generation

[0200] The server generates the optimal route and detour routes from the departure point to the destination based on the predicted congestion data. Specifically, it calculates the main route and detour routes, taking into account factors such as travel time, distance, and road type.

[0201] Input: Traffic congestion forecast data

[0202] Output: Optimal route, detour route

[0203] Step 7: Sending the results

[0204] The server sends the generated route information to the device, including information on the optimal route, detours, travel time, and distance. The server sends this data in JSON format to the device.

[0205] Input: Optimal route, detour route

[0206] Output: Route information sent to the device

[0207] Step 8: View Route

[0208] The device receives route information from the server and displays it on a map screen, along with the travel times for the main route and detour routes, allowing the user to select the optimal route.

[0209] Input: Route information sent from the server

[0210] Output: Optimal route and detour route displayed on the map screen

[0211] Step 9: Real-time information acquisition

[0212] While driving, the device receives real-time traffic information from traffic information services (e.g., Google Maps API), allowing the user to understand current traffic congestion and accident information.

[0213] Input: Real-time information from traffic information services

[0214] Output: Real-time traffic information

[0215] Step 10: Route Update

[0216] The device sends real-time traffic information to the server and requests the server to calculate a new route. The server then calculates a new detour route based on this information and sends it to the device.

[0217] Input: Real-time traffic information

[0218] Output: New route information

[0219] Step 11: View the updated route

[0220] The device will notify the user of the updated route information and display the new route information on the map screen, allowing the user to adjust their driving route based on the latest information.

[0221] Input: New route information sent from the server

[0222] Output: Updated route information displayed on the map screen

[0223] (Application example 1)

[0224] 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."

[0225] Conventional navigation systems only predict congestion based on traffic information and present optimal routes, which can lead to problems with real-time performance and accuracy in autonomous vehicles. Furthermore, congestion conditions can vary significantly depending on city conditions and weather, and these systems are unable to adequately address these issues. Therefore, there is a need for a system that improves usability and provides a comfortable travel experience in autonomous vehicles.

[0226] 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.

[0227] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time congestion information and updating the route, means for operating the autonomous driving function, and means for displaying the route information on the in-vehicle display. This makes it possible to respond to changes in traffic conditions due to weather or events, and to simultaneously realize real-time route optimization and operation of the autonomous driving function.

[0228] "Traffic information" refers to information on the flow of traffic on roads, congestion, accidents, road construction, etc.

[0229] "Means for predicting traffic congestion" refers to algorithms that analyze existing and past traffic data and predict the occurrence of future traffic congestion.

[0230] "Weather information" is data relating to weather conditions in a specified area and at a specified date and time.

[0231] "Calendar information" is information about schedules such as holidays, school holidays, and events on specific dates.

[0232] "Method of calculating routes based on past congestion information" refers to an algorithm that uses past traffic data to calculate the optimal travel route.

[0233] The "means for generating optimal routes and detour routes based on predicted congestion data" refers to algorithms and mechanisms that calculate primary and alternative routes taking into account future traffic conditions.

[0234] The "means for transmitting generated route information to the terminal" is a function for transmitting the calculated travel route to the user's device via a communication means.

[0235] "Means for obtaining real-time traffic congestion information and updating routes" refers to a function that obtains the latest traffic conditions in real time while traveling and recalculates and updates the route if necessary.

[0236] "Means for operating autonomous driving functions" refers to a function that issues instructions to the autonomous driving system of an autonomous vehicle and controls the driving of the vehicle.

[0237] "Means for displaying route information on an in-vehicle display" refers to a function for visually displaying the calculated travel route and related information on a display device inside the vehicle.

[0238] This invention provides a system that allows users to use autonomous vehicles to select the optimal route based on traffic information, weather information, calendar information, and past congestion information, and reach their destination safely and comfortably. This system works in conjunction with the navigation software and on-board display of the autonomous vehicle.

[0239] System configuration

[0240] The system mainly consists of three components: a server, a terminal, and a user. The functions of each component are as follows:

[0241] server

[0242] The server collects and analyzes traffic information, weather information, calendar information, and past congestion information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route. Specifically, information is collected using a weather information API, calendar information API, and traffic database. The collected data is analyzed using a traffic prediction algorithm, and congestion predictions are made for the specified date and time. The optimal route and detour route are then generated and sent to the user's device.

[0243] Terminal

[0244] The terminal is installed in an autonomous vehicle and includes an on-board display and autonomous driving system. The terminal displays route information received from the server and provides navigation. It also has the function of updating routes based on traffic congestion information obtained in real time. The terminal obtains real-time traffic information through a GPS module and an internet connection, and sends the information to the server to calculate a new route as needed.

[0245] User

[0246] The user inputs the departure point, destination, and departure date and time into the device. Following the device's guidance, the user begins driving to the destination along the optimal route. If the device notifies the user of real-time route updates during autonomous driving, the user follows those instructions.

[0247] Specific examples

[0248] As a specific example, consider the case of heading from Tokyo to a tourist destination at 9:00 AM on a holiday. The user enters "Tokyo" as the departure point, "tourist destination" as the destination, and "9:00 AM on a holiday" as the departure date and time into the device. The device sends this data to the server. The server obtains weather information for Tokyo and the tourist destination from a weather forecast API and information that the relevant day is a holiday or that there may be a large number of tourists from a calendar information API. It obtains traffic congestion information under similar conditions from past traffic congestion data and predicts the occurrence of traffic congestion. The server generates a main route and a detour route based on the predicted data and calculates the required travel time. The server sends the generated route information to the device. The device displays the received route information on the in-vehicle display, along with the required travel time for the main route and the detour route. The user follows the device's guidance and begins driving toward the destination tourist destination via the optimal route. The device obtains traffic congestion information in real time and sends it to the server. If traffic congestion occurs, the server calculates a new detour route and sends it to the device. The device notifies the user of the updated route information and displays it on the in-vehicle display. In this way, the system can provide the user with an optimal driving plan that avoids traffic jams.

[0249] Example prompt sentences to use

[0250] Example prompts that may be used include:

[0251] "Calculate the best route from Tokyo to a tourist spot."

[0252] "Please tell me the route taking into account today's weather and traffic information."

[0253] Please suggest an alternative route to avoid the traffic jam.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] The user inputs the departure point, destination, and departure date and time into the terminal. The input data includes the departure point, destination, departure date and time, and is transmitted to the server by the terminal.

[0257] Step 2:

[0258] The server uses the weather forecast API to obtain weather information for the specified date and time near the departure point and destination. The input is the departure point and destination information, and based on that, it sends a request to the API to obtain weather information data. The output is the weather information for the specified date and time.

[0259] Step 3:

[0260] The server uses the calendar information API to obtain information about holidays, elementary and junior high school holidays, and other event information for the relevant day. The input is the departure date and time information, and based on that, it sends a request to the calendar API to obtain event information data. The output is calendar information.

[0261] Step 4:

[0262] The server searches and retrieves traffic congestion information that occurred under similar conditions in the past from a traffic database. The inputs include the departure point, destination, departure date and time, and a database query is executed based on these to retrieve traffic congestion information. The output is past traffic congestion information.

[0263] Step 5:

[0264] The server runs a traffic prediction algorithm based on the collected weather information, calendar information, and past traffic congestion information to predict traffic congestion for a specified date and time. The inputs are the acquired weather information, calendar information, and past traffic congestion information, which are combined and analyzed by the algorithm. The output is traffic congestion prediction data.

[0265] Step 6:

[0266] The server generates the optimal route and detour route from the departure point to the destination point based on the predicted congestion data. The inputs are the congestion prediction data, the departure point, and the destination, and the route generation algorithm is executed based on this information. The output is the optimal route and detour route information.

[0267] Step 7:

[0268] The server sends the generated route information to the terminal. The input is the generated route information, and the server executes a communication operation to send it to the terminal. The output is the route information that has been sent to the terminal.

[0269] Step 8:

[0270] The terminal displays the optimal route and detour route received from the server on the in-vehicle display. The input is the route information received from the server, and the terminal performs operations to display it on the display screen. The output is a visual display of the route information.

[0271] Step 9:

[0272] The device obtains real-time traffic information from the traffic information service while driving. The input is the current GPS location and a real-time traffic information API request, and the latest traffic information is obtained from the API. The output is real-time traffic information.

[0273] Step 10:

[0274] The terminal sends the acquired real-time traffic congestion information to the server and requests the calculation of a new route. The input is the real-time traffic congestion information, and the terminal performs a communication operation to send this information to the server. The output is the traffic congestion information that has been sent to the server.

[0275] Step 11:

[0276] The terminal receives the new detour route from the server and displays it on the in-vehicle display. The input is the updated route information, and the terminal performs an operation to display it again on the display. The output is the updated route information being displayed.

[0277] This series of steps allows users to always use the latest and most appropriate navigation information, enabling them to reach their destination smoothly.

[0278] 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.

[0279] This invention is a system that helps users avoid traffic jams and reach their destination, and combines an emotion engine that recognizes the user's emotions. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"), and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0280] Program processing overview

[0281] Server-side processing

[0282] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour routes.The server also analyzes the user's emotional data and provides routes that correspond to the user's emotional state.

[0283] 1. Data Collection

[0284] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[0285] The server uses the calendar information API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[0286] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0287] 2. Traffic congestion prediction

[0288] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion at a specified date and time.

[0289] 3. Route Generation

[0290] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[0291] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[0292] 4. Emotion Data Analysis

[0293] The server uses an emotion engine to analyze the user's emotion data, which includes analyzing the user's voice and facial expressions.

[0294] 5. Route adjustment based on user emotions

[0295] If the user is feeling stressed, the server generates a route to alleviate that stress and adds it to the regular route suggestions.

[0296] 6. Submitting the results

[0297] The server transmits the generated route information to the terminal.

[0298] Terminal side processing

[0299] The device displays the route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary.

[0300] 1. Data Entry

[0301] The user inputs the departure point, destination, and departure date and time into the terminal.

[0302] The terminal transmits the input data to the server.

[0303] 2. Route display

[0304] The terminal displays the optimum route and detour route received from the server on a map screen.

[0305] It also displays the travel times for the main route and alternate routes, giving users a choice.

[0306] 3. Emotion Monitoring

[0307] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[0308] If stress or irritation is detected, the information is sent to the server.

[0309] 4. Real-time updates

[0310] The device obtains real-time congestion information from traffic information services while driving.

[0311] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[0312] The device notifies the user of the updated route information and displays it on the map screen.

[0313] User operation

[0314] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of real-time route updates while driving, the user follows the instructions and heads to the destination on a new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route.

[0315] Specific examples

[0316] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0317] 1. Data Collection

[0318] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0319] The terminal transmits this data to the server.

[0320] 2. Traffic congestion prediction and route generation

[0321] The server obtains weather information (e.g. sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[0322] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[0323] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[0324] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[0325] 3. Emotional Data Analysis and User Feedback

[0326] If the user becomes frustrated while driving, the emotion engine will detect this and suggest a quieter route that will reduce stress.

[0327] 4. Sending and displaying results

[0328] The server transmits the generated route information to the terminal.

[0329] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0330] 5. Real-time updates and navigation

[0331] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0332] The terminal acquires traffic congestion information in real time and sends it to the server.

[0333] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0334] The device notifies the user of the updated route information and displays it on the map screen.

[0335] In this way, the system provides the user with an efficient driving plan that avoids traffic jams, and also suggests routes that reflect the user's emotions.

[0336] The processing flow will be explained below.

[0337] Step 1: Data entry (user, device)

[0338] The user inputs the departure point (e.g., Tokyo), destination (e.g., Hakone), and departure date and time (e.g., 9:00 AM on a holiday) into the terminal.

[0339] The terminal formats the entered data and sends it to the server.

[0340] Step 2: Collecting weather and calendar information (server)

[0341] The server calls the weather forecast API and obtains weather information for the departure and destination locations for the specified date and time.

[0342] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0343] Step 3: Obtaining past traffic congestion data (server)

[0344] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0345] Step 4: Traffic congestion prediction (server)

[0346] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[0347] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[0348] Step 5: Generate Route Information (Server)

[0349] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[0350] Step 6: Emotion data analysis (server)

[0351] The server uses an emotion engine to analyze the user's emotion data (voice and facial expressions).

[0352] To detect a state in which a user is feeling stressed.

[0353] Step 7: Route adjustment according to user sentiment (server)

[0354] The server generates routes to reduce the user's stress and adds them to the regular route suggestions.

[0355] Step 8: Sending Route Information (Server)

[0356] The server transmits the generated route information to the terminal.

[0357] Step 9: View Route Information (Device)

[0358] The terminal draws the optimum route and detour route received from the server on a map screen.

[0359] The travel times for the main route and the detour route are also displayed to the user.

[0360] Step 10: Emotion Monitoring (Device)

[0361] The device acquires the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[0362] If the user is feeling stressed, the information is sent to the server.

[0363] Step 11: Navigation Start (User, Device)

[0364] The user starts the navigation function of the terminal and drives the car.

[0365] The terminal guides the user along the optimum route.

[0366] Step 12: Obtaining real-time traffic congestion information (device)

[0367] The device obtains traffic congestion information from the traffic information service in real time while driving.

[0368] Step 13: Recalculating the route (terminal, server)

[0369] The terminal transmits the acquired real-time traffic congestion information to the server.

[0370] The server recalculates route information based on real-time data.

[0371] Step 14: Sending and displaying updated routes (server, terminal)

[0372] The server sends the new route information to the terminal.

[0373] The terminal notifies the user of the updated route information and reflects it on the map screen.

[0374] Step 15: User arrives at destination

[0375] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[0376] This series of steps allows users to avoid traffic jams, reach their destination efficiently, and even suggests routes based on the user's emotions.

[0377] Example 2

[0378] 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."

[0379] Conventional navigation systems can predict traffic congestion and provide optimal routes using traffic and weather information, but they do not take the user's emotional state into account when proposing routes. As a result, even if the user is feeling stressed, only efficient routes are provided, which can potentially reduce the user's comfort. Furthermore, systems must be able to respond quickly to real-time traffic conditions, and a system that can reliably do so was needed.

[0380] 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.

[0381] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for analyzing user emotion data, means for adjusting the route based on the analyzed emotion data, and means for acquiring real-time congestion information and updating the route, thereby enabling efficient and real-time optimal route suggestions while taking user comfort into consideration.

[0382] "Traffic information" refers to information about road congestion, traffic regulations, accidents, etc.

[0383] "Weather information" refers to meteorological data and forecasts that indicate weather conditions at a particular time or location.

[0384] "Calendar information" is information about events, holidays, vacations, etc. associated with a specific date.

[0385] "Traffic congestion forecasting" is the prediction of future road congestion conditions based on collected traffic information and related data.

[0386] A "route" is a path from a starting point to a destination.

[0387] An "optimal route" is a route that is determined to be the most efficient, taking into account factors such as travel time, distance, and traffic conditions.

[0388] A "detour route" is an alternative route set aside from the main route to meet specific conditions (e.g., avoiding congestion, reducing stress).

[0389] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's voice, facial expression, etc.

[0390] "Emotion analysis" refers to identifying a user's emotional state based on collected emotional data.

[0391] "Real-time information" refers to information provided based on ongoing situations and the most recent data.

[0392] This invention is a system that helps users avoid traffic jams and reach their destinations, and combines an emotion engine that recognizes the user's emotions. This system is installed in devices such as smartphones and car navigation systems, and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0393] Specifically, this system is broadly divided into server-side and terminal-side processing.

[0394] Server-side processing

[0395] Data collection

[0396] The server first collects traffic, weather, calendar, and historical congestion information using the following APIs and databases:

[0397] Weather forecast APIs (e.g., OpenWeatherMap API)

[0398] Calendar information API (e.g. Google Calendar API)

[0399] Traffic database: Stores historical traffic information and retrieves data by issuing SQL queries

[0400] Traffic congestion forecast

[0401] Based on the collected data, the server uses traffic prediction algorithms to predict congestion. Using the integrated dataset, machine learning models are applied to predict future congestion information.

[0402] Route Generation

[0403] The server generates main routes and detour routes based on predicted traffic congestion data. It optimizes routes using Google Maps API and Microsoft Bing Maps API. It also generates special routes, such as stress-reducing routes, based on the analysis of user emotion data.

[0404] Emotional Data Analysis

[0405] The server uses an emotion engine to analyze emotion data from the user's voice and facial expressions, utilizing IBM Watson Speech to Text API, IBM Watson Tone Analyzer, and Microsoft Azure Face API.

[0406] Sending the results

[0407] The generated route information and sentiment analysis results are sent to the device via a RESTful API via an HTTPS request.

[0408] Terminal side processing

[0409] Data input from the user

[0410] The user inputs the departure point, destination, and departure date and time into the terminal, and the input data is sent to the server.

[0411] View route information

[0412] The device displays the route information received from the server on a map screen, along with the travel times for the main route and detour routes, providing the user with a choice of routes.

[0413] Emotion Monitoring

[0414] The device captures the user's voice and facial expressions in real time and analyzes them with an emotion engine. If stress or irritation is detected, the information is sent to the server.

[0415] Real-time updates

[0416] The device obtains real-time traffic congestion information from a traffic information service (e.g., Google Traffic API) and sends it to the server. If new route information is available, it is displayed on the map screen and notified to the user.

[0417] Specific examples

[0418] Consider a case where a user goes for a drive from Tokyo to Hakone at 9:00 a.m. on a holiday.

[0419] 1. Data Collection

[0420] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal, and the terminal transmits this data to the server.

[0421] 2. Traffic congestion prediction and route generation

[0422] The server retrieves weather information for Tokyo and Hakone from the weather forecast API (e.g. sunny).

[0423] Obtain information from the calendar information API that the relevant date is a holiday.

[0424] Information on days with similar conditions is obtained from past traffic congestion data to predict traffic congestion.

[0425] The server generates the main route (e.g., Tomei Expressway) and the detour route (e.g., general roads) and calculates the required travel time.

[0426] 3. Emotional Data Analysis and User Feedback

[0427] If the user becomes irritated while driving, the emotion engine will detect this and suggest a quieter route to reduce stress.

[0428] 4. Sending and displaying results

[0429] The server sends the generated route information to the device, which then displays the route information on a map screen, along with the travel times for the main route and detour routes.

[0430] 5. Real-time updates and navigation

[0431] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0432] The terminal acquires traffic congestion information in real time and sends it to the server.

[0433] When traffic congestion occurs, the server calculates a new detour route and sends it to the device, which then displays the updated route information on the map screen and notifies the user.

[0434] This system provides users with efficient driving plans that avoid traffic jams, and can also suggest routes based on the user's emotions.In addition, by utilizing real-time information, route guidance is always based on the latest conditions.

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Step 1: Enter user data

[0437] The user inputs the origin, destination, and departure date and time into the device. The device retrieves this data and sends it to the server in a structured format (e.g., JSON). The server then starts optimizing the route based on the origin, destination, and departure date and time.

[0438] Step 2: Get weather information

[0439] The server sends a request to a weather forecast API (e.g., OpenWeatherMap API) to get current and forecast weather information for the origin and destination. As input, the API request is sent, and as output, weather information (e.g., temperature, chance of precipitation, and weather conditions) is returned in JSON format.

[0440] Step 3: Get Calendar Information

[0441] The server retrieves event information (e.g., public holidays, elementary and junior high school holidays) related to the specified departure date and time from a calendar information API (e.g., Google Calendar API). An API query is sent as input, and calendar event information is retrieved as output.

[0442] Step 4: Obtaining historical traffic congestion information

[0443] The server issues a query to the traffic database to retrieve historical traffic data for days with similar conditions to the specified date and time. The input is an SQL query, and the output is a dataset containing historical traffic information.

[0444] Step 5: Data integration and congestion prediction

[0445] The server integrates the collected weather information, calendar information, and past traffic congestion information and inputs it into a machine learning model. The input is the integrated dataset, and the output is the traffic congestion prediction result. Specifically, the model takes into account information such as weather and event dates to predict the probability of traffic congestion occurring.

[0446] Step 6: Generate main and detour routes

[0447] The server uses the Google Maps API and Microsoft Bing Maps API to generate optimal routes and detour routes based on predicted congestion data. The inputs are the departure point, destination, and congestion forecast data, and the output is route information and travel time for each route.

[0448] Step 7: Analyze the sentiment data

[0449] The device captures the user's voice and facial expression data in real time and sends it to the server, which then analyzes the data using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stress level).

[0450] Step 8: Adjust the route information

[0451] The server analyzes the user's emotional state and adjusts the route as necessary. If the user is feeling stressed, it will suggest a quieter route or a more scenic route. The input is the emotion analysis result, and the output is the adjusted route information.

[0452] Step 9: Submit route information

[0453] The server sends the generated and adjusted route information to the terminal. The route information is the input and the transmission to the terminal is the output.

[0454] Step 10: View Route Information

[0455] The terminal displays the route information received from the server on a map screen. Route information is input, and the route is displayed to the user as output. The required times for the main route and detour routes are also displayed, helping the user select the optimal route.

[0456] Step 11: Real-time updates

[0457] While driving, the device obtains real-time traffic congestion information from the traffic information service and sends it to the server. The server recalculates the route based on the new information and sends the optimal route and detour route to the device. The real-time traffic congestion information is the input, and the updated route information is the output.

[0458] Through these steps, the present invention provides comfortable and efficient navigation for the user.

[0459] (Application example 2)

[0460] 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."

[0461] Conventional navigation systems provide optimal routes based on traffic and weather information, but do not take into account the user's emotional state. As a result, users are likely to feel stressed due to traffic congestion and other traffic conditions. Even in cases where real-time updates are provided regarding traffic congestion avoidance and route changes, route selection is not yet possible, taking the user's mental state into account. To solve this problem, it is necessary to propose optimal routes that take the user's emotions into account.

[0462] 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.

[0463] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time traffic information and updating the route, means for recognizing the user's emotions, and means for providing a route according to the user's emotional state, thereby making it possible to provide a stress-reducing route that matches the user's emotional state.

[0464] "Traffic information" refers to data on road usage, such as road congestion, traffic congestion information, and whether or not road construction is occurring.

[0465] "Traffic congestion prediction" is the prediction of future road congestion based on past traffic data, current traffic conditions, and other related information.

[0466] "Weather information" is data on weather conditions such as temperature, probability of precipitation, and wind speed in a specified area.

[0467] "Calendar information" is schedule data such as holidays, school holidays, special events, etc., that correspond to specific dates and times.

[0468] "Past congestion information" is recorded data relating to road congestion conditions at specific past dates and times.

[0469] The "means for calculating a route" is a means for calculating the optimal route from a departure point to a destination using traffic information, weather information, calendar information, and past congestion information.

[0470] The "optimal route" is the optimal route calculated based on predicted congestion data, taking into account the required time, distance, traffic conditions, etc.

[0471] A "detour route" is an alternative route to avoid congestion on a designated main route.

[0472] "Generated route information" refers to data relating to the calculated and generated optimum route and detour route.

[0473] A "terminal" is an electronic device that displays information and exchanges data between a user and the Internet.

[0474] "Real-time traffic congestion information" is data that provides current road traffic conditions in real time.

[0475] The "means for updating a route" is a means for recalculating an existing route based on new traffic information and providing the latest route information.

[0476] "User emotions" refers to a person's psychological state or mood, and includes emotions such as stress, frustration, and relief.

[0477] "Means for recognizing emotions" refers to means for identifying the emotional state of a user through voice analysis, facial expression analysis, etc.

[0478] A "route according to emotional state" is a route selected to reduce stress, taking into consideration the user's current emotions.

[0479] This invention relates to a system that predicts congestion based on traffic information, recognizes the user's emotions, and provides the optimal route. This system is realized through the interaction between a server, a terminal, and a user.

[0480] The server includes a means for predicting congestion based on traffic information, a means for acquiring weather and calendar information, a means for calculating routes based on past congestion information, a means for generating optimal routes and detour routes based on predicted congestion data, a means for transmitting the generated route information to the terminal, a means for acquiring real-time congestion information and updating routes, a means for recognizing user emotions, and a means for providing routes according to the user's emotional state. This server generally operates in a cloud computing environment connected to the Internet. Specifically, it is implemented using a backend service using Python or JavaScript (e.g., Django, Node.js).

[0481] The device displays route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary. This device is typically a smartphone or car navigation system, and is implemented as an Android or iOS application. Specifically, map services such as Google Maps API are used to obtain and display real-time data, and libraries such as EmotionEngine are used for emotion analysis.

[0482] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination on the new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route. Emotion recognition uses voice input and facial expression analysis via a camera.

[0483] Specifically, the server obtains weather information from a weather information API and event information for a specific day from a calendar information API. It then obtains past traffic congestion information from a traffic database and predicts traffic congestion based on this information. In this way, an optimal route and detour route are generated and sent to the terminal. The terminal then displays this route information on a map screen and provides it to the user.

[0484] While driving, the user's voice and facial expression data are collected by the device and analyzed by the emotion engine. If the user feels stressed, this information is sent to the server, which then calculates a new detour route that corresponds to the user's emotions. This allows the user to reach their destination via a more comfortable route.

[0485] As a concrete example, consider a user heading from Tokyo to Hakone at 9 a.m. on a holiday. The system generates a main route and a detour route based on weather forecasts, holiday information, and past traffic congestion data. If the user's emotion is detected as irritation while driving, the emotion engine recognizes this and suggests changing the route to a quieter route on the server side.

[0486] An example prompt is, "If the user feels stressed while driving, the emotion engine should detect this and suggest a quieter route in real time. Also, notify the user of the new route along with the estimated travel time and distance."

[0487] In this way, the present invention can provide the user with a comfortable driving plan that is less stressful.

[0488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0489] Step 1: User Input

[0490] The user inputs the departure point, destination, and departure date and time into the terminal.

[0491] Input data: origin, destination, departure date and time.

[0492] Output data: The input data is sent to the server.

[0493] Step 2: Get weather information

[0494] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[0495] Input data: origin, destination.

[0496] Data processing: Obtain weather information using API.

[0497] Output data: Weather information.

[0498] Step 3: Get Calendar Information

[0499] The server uses the calendar information API to obtain information about holidays and special events for the relevant day.

[0500] Input data: Departure date and time.

[0501] Data processing: Calendar information is obtained using API.

[0502] Output data: Calendar information.

[0503] Step 4: Obtaining historical traffic congestion information

[0504] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0505] Input data: origin, destination, departure date and time.

[0506] Data processing: Obtain past traffic congestion information using database queries.

[0507] Output data: Past traffic congestion information.

[0508] Step 5: Traffic congestion prediction

[0509] The server uses a traffic prediction algorithm to predict congestion at a specified date and time based on collected weather information, calendar information, and past congestion information.

[0510] Input data: Weather information, calendar information, past traffic congestion information.

[0511] Data calculation: Traffic prediction algorithms are applied to predict congestion.

[0512] Output data: Predicted congestion data.

[0513] Step 6: Route Generation

[0514] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[0515] Input data: predicted traffic congestion data, origin, destination.

[0516] Data calculation: Route calculation algorithm is applied to generate a route.

[0517] Output data: Optimal route, detour route.

[0518] Step 7: Submit the generated route information

[0519] The server transmits the generated route information to the terminal.

[0520] Input data: Generated route information.

[0521] Output data: Route information is sent to the terminal.

[0522] Step 8: View Route Information

[0523] The terminal displays the optimum route and detour route received from the server on a map screen to provide navigation.

[0524] Input data: Route information.

[0525] Output data: Route information is displayed on the map screen.

[0526] Step 9: Collect emotion data

[0527] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[0528] Input data: User's voice data and facial expression data.

[0529] Data Computation: Analyze emotional state using emotion engine.

[0530] Output data: emotion data.

[0531] Step 10: Sending Emotion Data

[0532] The device transmits the emotion data to the server.

[0533] Input data: Emotion data.

[0534] Output data: Emotion data is sent to the server.

[0535] Step 11: Recalculate the route

[0536] The server recalculates the travel time of the detour route based on the emotion data and real-time traffic congestion information.

[0537] Input data: Emotion data, real-time traffic congestion information.

[0538] Data calculation: Route recalculation based on emotion data and traffic congestion information.

[0539] Output data: Updated route information.

[0540] Step 12: Submit updated route information

[0541] The server sends the updated route information to the terminal.

[0542] Input data: Updated route information.

[0543] Output data: Updated route information is sent to the device.

[0544] Step 13: View the updated route

[0545] The terminal displays the updated route information on the map screen and notifies the user.

[0546] Input data: Updated route information.

[0547] Output data: Updated route information is displayed on the map screen.

[0548] Step 14: Navigation Update

[0549] The user follows the instructions on the device and heads to the destination via a new route.

[0550] Input data: Updated route information.

[0551] Output data: User's driving route corrections.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Second embodiment]

[0556] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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."

[0568] The present invention is a system that enables users to reach their destination while avoiding traffic jams, and is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"). This system has the function of calculating the optimal route based on traffic information, weather information, calendar information, and past traffic jam information, and updating the route to reflect real-time information.

[0569] Program processing overview

[0570] Server-side processing

[0571] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route.

[0572] 1. Data Collection

[0573] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[0574] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0575] The server searches and retrieves information on traffic congestion that occurred under similar conditions in the past from a traffic database.

[0576] 2. Traffic congestion prediction

[0577] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion for a specified date and time.

[0578] 3. Route Generation

[0579] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[0580] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[0581] 4. Sending the results

[0582] The server transmits the generated route information to the terminal.

[0583] Terminal side processing

[0584] The device displays the route information received from the server, provides navigation, and updates the route based on real-time traffic congestion information.

[0585] 1. Data Entry

[0586] The user inputs the departure point, destination, and departure date and time into the terminal.

[0587] The terminal transmits the input data to the server.

[0588] 2. Route display

[0589] The terminal displays the optimum route and detour route received from the server on a map screen.

[0590] It also displays the travel times for the main route and alternate routes, giving users a choice.

[0591] 3. Real-time updates

[0592] The device obtains real-time congestion information from traffic information services while driving.

[0593] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[0594] The terminal displays the updated route information and notifies the user.

[0595] User operation

[0596] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination along the new route.

[0597] Specific examples

[0598] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0599] 1. Data Collection

[0600] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0601] The terminal transmits this data to the server.

[0602] 2. Traffic congestion prediction and route generation

[0603] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[0604] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[0605] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[0606] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[0607] 3. Sending and displaying results

[0608] The server transmits the generated route information to the terminal.

[0609] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0610] 4. Real-time updates and navigation

[0611] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0612] The terminal acquires traffic congestion information in real time and sends it to the server.

[0613] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0614] The device notifies the user of the updated route information and displays it on the map screen.

[0615] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[0616] The processing flow will be explained below.

[0617] Step 1: Data entry (user, device)

[0618] The user inputs the departure point, destination, and departure date and time into the terminal.

[0619] The terminal formats the entered data and sends it to the server.

[0620] Step 2: Collecting weather and calendar information (server)

[0621] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[0622] The server uses a calendar API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[0623] Step 3: Obtaining past traffic congestion data (server)

[0624] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0625] Step 4: Traffic congestion prediction (server)

[0626] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[0627] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[0628] Step 5: Generate Route Information (Server)

[0629] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[0630] Step 6: Sending Route Information (Server)

[0631] The server transmits the generated route information to the terminal.

[0632] Step 7: View Route Information (Device)

[0633] The terminal draws the optimum route and detour route received from the server on a map screen.

[0634] The travel times for the main route and the detour route are also displayed to the user.

[0635] Step 8: Navigation Start (User, Device)

[0636] The user starts the navigation function of the terminal and drives the car.

[0637] The terminal guides the user along the optimum route.

[0638] Step 9: Obtaining real-time traffic congestion information (device)

[0639] The device obtains traffic congestion information from the traffic information service in real time while driving.

[0640] Step 10: Route recalculation (terminal, server)

[0641] The terminal transmits the acquired real-time traffic congestion information to the server.

[0642] The server recalculates route information based on real-time data.

[0643] Step 11: Sending and displaying updated routes (server, terminal)

[0644] The server sends the new route information to the terminal.

[0645] The terminal notifies the user of the updated route information and reflects it on the map screen.

[0646] Step 12: User arrives at destination

[0647] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[0648] This series of steps allows users to avoid traffic jams and reach their destination efficiently.

[0649] Example 1

[0650] 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."

[0651] Conventional navigation systems have difficulty using real-time traffic congestion information to provide users with the optimal route. They also lack the functionality to predict traffic conditions using weather and calendar information and provide users with the optimal route. Furthermore, they are unable to provide detailed traffic congestion predictions using past traffic congestion information based on the departure point, destination, and departure date and time.

[0652] 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.

[0653] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and date and time information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted traffic data, means for transmitting the generated route information to a device, means for acquiring real-time traffic information and updating the route, means for a user to input a departure point, a destination point, and a departure time, means for displaying the optimal route and the detour route on a map screen, means for acquiring real-time traffic information from a traffic information service while driving and updating the route, and means for displaying the route information generated in real time on a map screen and notifying the user. This makes it possible to provide the user with an optimal route and a detour route based on real-time information.

[0654] "Traffic information" refers to data relating to traffic conditions, including congestion, accidents, road construction, traffic volume, and the like.

[0655] "Traffic congestion forecast" is the process of predicting road congestion at a specified date and time based on various data.

[0656] "Weather information" is data related to weather forecasts, including temperature, precipitation, wind speed, and the like.

[0657] "Date and time information" is data related to a calendar, and includes information about a specific date and time.

[0658] "Historical traffic information" refers to previously recorded traffic data, including traffic performance under specific conditions and dates.

[0659] A "route" is a route from a starting point to a destination, including a main route and a detour route.

[0660] An "optimal route" is the best route calculated taking into account various conditions (travel time, distance, congestion, etc.).

[0661] A "detour route" is an alternative route that is chosen when the normal route is difficult due to traffic congestion or other reasons.

[0662] "Device" refers to the terminal used by the user, including smartphones, car navigation systems, etc.

[0663] "Real-time information" means instantly updated data about current traffic conditions.

[0664] "User" refers to a person who operates and uses this system.

[0665] The "map screen" is a visual interface that displays route information, current location, etc.

[0666] "Traffic Information Service" means an external information platform that provides real-time traffic conditions.

[0667] The present invention is a system that enables users to avoid traffic jams and reach their destinations based on traffic information, weather information, date and time information, and past traffic information. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals").

[0668] Hardware and Software

[0669] server:

[0670] The server uses the traffic forecast database, weather information API, date and time information API, and traffic information API to collect and analyze various data based on user requests. Specifically, it uses the OpenWeatherMap API to obtain weather information, the Google Calendar API to obtain date and time information, and the HERE Traffic API to obtain past traffic information.

[0671] Device:

[0672] The terminal provides an interface for the user to input the starting point, destination point, and departure time, and displays the route information received from the server on a map screen. The terminal also obtains traffic data in real time from a traffic information service (e.g., Google Maps API) and sends it to the server.

[0673] User:

[0674] The user inputs the departure point, destination, and departure date and time into the device and refers to the information on the optimal route and detour route sent from the server. If the device notifies the user of route updates in real time while driving, the user follows the instructions and heads to the destination on the new route.

[0675] Data processing and calculation

[0676] Data collection:

[0677] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[0678] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0679] The server searches and retrieves traffic information that occurred under similar conditions in the past from a traffic database.

[0680] Traffic congestion forecast:

[0681] Based on the collected weather information, calendar information, and past traffic information, the server uses a traffic prediction algorithm to predict congestion at a specified date and time.

[0682] Route Generation:

[0683] The server generates the optimal route from the departure point to the destination and a detour route based on the predicted traffic data, taking into account various conditions (travel time, distance, road type, etc.) to avoid traffic congestion.

[0684] Specific examples

[0685] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0686] Data collection:

[0687] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0688] The terminal transmits this data to the server.

[0689] Weather information, calendar information, traffic forecast:

[0690] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from a weather forecast API (e.g., OpenWeatherMap API).

[0691] Obtain information from the Google Calendar API about holidays on the relevant dates and events that may attract a large number of tourists.

[0692] The HERE Traffic API is used to obtain traffic data from similar conditions in the past and predict the occurrence of traffic congestion.

[0693] Route Generation:

[0694] Based on the predicted data, the server generates the main route (e.g., expressway) and detour route (e.g., general road) and calculates the required travel time.

[0695] Send and view results:

[0696] The server transmits the generated route information to the terminal.

[0697] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0698] Real-time updates:

[0699] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0700] While driving, the device obtains real-time traffic information from the traffic information service and transmits it to the server.

[0701] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0702] The device notifies the user of the updated route information and displays it on the map screen.

[0703] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[0704] Example prompts to input to the generative AI model

[0705] "I want to create a system that calculates the optimal driving route to avoid traffic jams. I'm thinking of using weather forecast APIs, calendar information APIs, and traffic information APIs, but I'd like to know how to collect the data, calculate the route, and notify the user."

[0706] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0707] Step 1: Data entry

[0708] The user inputs the departure point, destination, and departure date and time into the terminal. For example, the user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time. This input data is sent from the terminal to the server.

[0709] Input: Departure point, destination, departure date and time

[0710] Output: The input data sent to the server

[0711] Step 2: Get weather information

[0712] The server uses weather information APIs to obtain weather information for the specified date and time near the departure and destination locations. For example, the server uses the OpenWeatherMap API to obtain detailed weather forecasts (temperature, precipitation, wind speed, etc.) for Tokyo and Hakone.

[0713] Input: Departure point, destination, departure date and time

[0714] Output: Weather information (temperature, precipitation, wind speed, etc.)

[0715] Step 3: Get Calendar Information

[0716] The server uses a date and time information API to obtain information about holidays and events for the specified date. For example, it uses the Google Calendar API to obtain information about holidays, school holidays, and other events for the specified date.

[0717] Input: Departure date and time

[0718] Output: Calendar information (holidays, school vacations, event information, etc.)

[0719] Step 4: Obtaining historical traffic information

[0720] The server uses a traffic information API to obtain traffic data that occurred under similar conditions in the past. For example, it uses the HERE Traffic API to obtain data on traffic conditions similar to the specified date and time and conditions.

[0721] Input: Departure point, destination, departure date and time

[0722] Output: Past traffic information

[0723] Step 5: Traffic congestion prediction

[0724] The server uses a traffic prediction algorithm to predict congestion for a specified date and time based on collected weather, calendar, and past traffic information, and generates forecast data on which roads will be congested at which times.

[0725] Input: Weather information, calendar information, past traffic information

[0726] Output: Traffic congestion forecast data (predicted congestion situation)

[0727] Step 6: Route Generation

[0728] The server generates the optimal route and detour routes from the departure point to the destination based on the predicted congestion data. Specifically, it calculates the main route and detour routes, taking into account factors such as travel time, distance, and road type.

[0729] Input: Traffic congestion forecast data

[0730] Output: Optimal route, detour route

[0731] Step 7: Sending the results

[0732] The server sends the generated route information to the device, including information on the optimal route, detours, travel time, and distance. The server sends this data in JSON format to the device.

[0733] Input: Optimal route, detour route

[0734] Output: Route information sent to the device

[0735] Step 8: View Route

[0736] The device receives route information from the server and displays it on a map screen, along with the travel times for the main route and detour routes, allowing the user to select the optimal route.

[0737] Input: Route information sent from the server

[0738] Output: Optimal route and detour route displayed on the map screen

[0739] Step 9: Real-time information acquisition

[0740] While driving, the device receives real-time traffic information from traffic information services (e.g., Google Maps API), allowing the user to understand current traffic congestion and accident information.

[0741] Input: Real-time information from traffic information services

[0742] Output: Real-time traffic information

[0743] Step 10: Route Update

[0744] The device sends real-time traffic information to the server and requests the server to calculate a new route. The server then calculates a new detour route based on this information and sends it to the device.

[0745] Input: Real-time traffic information

[0746] Output: New route information

[0747] Step 11: View the updated route

[0748] The device will notify the user of the updated route information and display the new route information on the map screen, allowing the user to adjust their driving route based on the latest information.

[0749] Input: New route information sent from the server

[0750] Output: Updated route information displayed on the map screen

[0751] (Application example 1)

[0752] 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."

[0753] Conventional navigation systems only predict congestion based on traffic information and present optimal routes, which can lead to problems with real-time performance and accuracy in autonomous vehicles. Furthermore, congestion conditions can vary significantly depending on city conditions and weather, and these systems are unable to adequately address these issues. Therefore, there is a need for a system that improves usability and provides a comfortable travel experience in autonomous vehicles.

[0754] 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.

[0755] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time congestion information and updating the route, means for operating the autonomous driving function, and means for displaying the route information on the in-vehicle display. This makes it possible to respond to changes in traffic conditions due to weather or events, and to simultaneously realize real-time route optimization and operation of the autonomous driving function.

[0756] "Traffic information" refers to information on the flow of traffic on roads, congestion, accidents, road construction, etc.

[0757] "Means for predicting traffic congestion" refers to algorithms that analyze existing and past traffic data and predict the occurrence of future traffic congestion.

[0758] "Weather information" is data relating to weather conditions in a specified area and at a specified date and time.

[0759] "Calendar information" is information about schedules such as holidays, school holidays, and events on specific dates.

[0760] "Method of calculating routes based on past congestion information" refers to an algorithm that uses past traffic data to calculate the optimal travel route.

[0761] The "means for generating optimal routes and detour routes based on predicted congestion data" refers to algorithms and mechanisms that calculate primary and alternative routes taking into account future traffic conditions.

[0762] The "means for transmitting generated route information to the terminal" is a function for transmitting the calculated travel route to the user's device via a communication means.

[0763] "Means for obtaining real-time traffic congestion information and updating routes" refers to a function that obtains the latest traffic conditions in real time while traveling and recalculates and updates the route if necessary.

[0764] "Means for operating autonomous driving functions" refers to a function that issues instructions to the autonomous driving system of an autonomous vehicle and controls the driving of the vehicle.

[0765] "Means for displaying route information on an in-vehicle display" refers to a function for visually displaying the calculated travel route and related information on a display device inside the vehicle.

[0766] This invention provides a system that allows users to use autonomous vehicles to select the optimal route based on traffic information, weather information, calendar information, and past congestion information, and reach their destination safely and comfortably. This system works in conjunction with the navigation software and on-board display of the autonomous vehicle.

[0767] System configuration

[0768] The system mainly consists of three components: a server, a terminal, and a user. The functions of each component are as follows:

[0769] server

[0770] The server collects and analyzes traffic information, weather information, calendar information, and past congestion information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route. Specifically, information is collected using a weather information API, calendar information API, and traffic database. The collected data is analyzed using a traffic prediction algorithm, and congestion predictions are made for the specified date and time. The optimal route and detour route are then generated and sent to the user's device.

[0771] Terminal

[0772] The terminal is installed in an autonomous vehicle and includes an on-board display and autonomous driving system. The terminal displays route information received from the server and provides navigation. It also has the function of updating routes based on traffic congestion information obtained in real time. The terminal obtains real-time traffic information through a GPS module and an internet connection, and sends the information to the server to calculate a new route as needed.

[0773] User

[0774] The user inputs the departure point, destination, and departure date and time into the device. Following the device's guidance, the user begins driving to the destination along the optimal route. If the device notifies the user of real-time route updates during autonomous driving, the user follows those instructions.

[0775] Specific examples

[0776] As a specific example, consider the case of heading from Tokyo to a tourist destination at 9:00 AM on a holiday. The user enters "Tokyo" as the departure point, "tourist destination" as the destination, and "9:00 AM on a holiday" as the departure date and time into the device. The device sends this data to the server. The server obtains weather information for Tokyo and the tourist destination from a weather forecast API and information that the relevant day is a holiday or that there may be a large number of tourists from a calendar information API. It obtains traffic congestion information under similar conditions from past traffic congestion data and predicts the occurrence of traffic congestion. The server generates a main route and a detour route based on the predicted data and calculates the required travel time. The server sends the generated route information to the device. The device displays the received route information on the in-vehicle display, along with the required travel time for the main route and the detour route. The user follows the device's guidance and begins driving toward the destination tourist destination via the optimal route. The device obtains traffic congestion information in real time and sends it to the server. If traffic congestion occurs, the server calculates a new detour route and sends it to the device. The device notifies the user of the updated route information and displays it on the in-vehicle display. In this way, the system can provide the user with an optimal driving plan that avoids traffic jams.

[0777] Example prompt sentences to use

[0778] Example prompts that may be used include:

[0779] "Calculate the best route from Tokyo to a tourist spot."

[0780] "Please tell me the route taking into account today's weather and traffic information."

[0781] Please suggest an alternative route to avoid the traffic jam.

[0782] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0783] Step 1:

[0784] The user inputs the departure point, destination, and departure date and time into the terminal. The input data includes the departure point, destination, departure date and time, and is transmitted to the server by the terminal.

[0785] Step 2:

[0786] The server uses the weather forecast API to obtain weather information for the specified date and time near the departure point and destination. The input is the departure point and destination information, and based on that, a request is sent to the API to obtain weather information data. The output is the weather information for the specified date and time.

[0787] Step 3:

[0788] The server uses the calendar information API to obtain information about holidays, elementary and junior high school holidays, and other event information for the relevant day. The input is the departure date and time information, and based on that, a request is sent to the calendar API to obtain event information data. The output is calendar information.

[0789] Step 4:

[0790] The server searches and retrieves traffic information from a traffic database about traffic congestion that occurred under similar conditions in the past. The inputs include the departure point, destination, departure date and time, and a database query is executed based on these to retrieve traffic congestion information. The output is past traffic congestion information.

[0791] Step 5:

[0792] The server runs a traffic prediction algorithm based on the collected weather information, calendar information, and past traffic congestion information to predict traffic congestion for a specified date and time. The inputs are the acquired weather information, calendar information, and past traffic congestion information, which are combined and analyzed by the algorithm. The output is traffic congestion prediction data.

[0793] Step 6:

[0794] The server generates the optimal route and detour route from the departure point to the destination point based on the predicted congestion data. The inputs are the congestion prediction data, the departure point, and the destination, and the route generation algorithm is executed based on this information. The output is the optimal route and detour route information.

[0795] Step 7:

[0796] The server sends the generated route information to the terminal. The input is the generated route information, and the server executes a communication operation to send it to the terminal. The output is the route information that has been sent to the terminal.

[0797] Step 8:

[0798] The terminal displays the optimal route and detour route received from the server on the in-vehicle display. The input is the route information received from the server, and the terminal performs operations to display it on the display screen. The output is a visual display of the route information.

[0799] Step 9:

[0800] The device obtains real-time traffic information from the traffic information service while driving. The input is the current GPS location and a real-time traffic information API request, and the latest traffic information is obtained from the API. The output is real-time traffic information.

[0801] Step 10:

[0802] The terminal sends the acquired real-time traffic congestion information to the server and requests the calculation of a new route. The input is the real-time traffic congestion information, and the terminal performs a communication operation to send this information to the server. The output is the traffic congestion information that has been sent to the server.

[0803] Step 11:

[0804] The terminal receives the new detour route from the server and displays it on the in-vehicle display. The input is the updated route information, and the terminal performs an operation to display it again on the display. The output is the updated route information being displayed.

[0805] This series of steps allows users to always use the latest and most appropriate navigation information, enabling them to reach their destination smoothly.

[0806] 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.

[0807] This invention is a system that helps users avoid traffic jams and reach their destination, and combines an emotion engine that recognizes the user's emotions. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"), and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0808] Program processing overview

[0809] Server-side processing

[0810] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates optimal routes and detour routes.The server also analyzes the user's emotional data and provides routes that correspond to the user's emotional state.

[0811] 1. Data Collection

[0812] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[0813] The server uses the calendar information API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[0814] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0815] 2. Traffic congestion prediction

[0816] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion at a specified date and time.

[0817] 3. Route Generation

[0818] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[0819] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[0820] 4. Emotion Data Analysis

[0821] The server uses an emotion engine to analyze the user's emotion data, which includes analyzing the user's voice and facial expressions.

[0822] 5. Route adjustment based on user sentiment

[0823] If the user is feeling stressed, the server generates a route to alleviate that stress and adds it to the regular route suggestions.

[0824] 6. Submitting the results

[0825] The server transmits the generated route information to the terminal.

[0826] Terminal side processing

[0827] The device displays the route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary.

[0828] 1. Data Entry

[0829] The user inputs the departure point, destination, and departure date and time into the terminal.

[0830] The terminal transmits the input data to the server.

[0831] 2. Route display

[0832] The terminal displays the optimum route and detour route received from the server on a map screen.

[0833] It also displays the travel times for the main route and alternate routes, giving users a choice.

[0834] 3. Emotion Monitoring

[0835] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[0836] If stress or irritation is detected, the information is sent to the server.

[0837] 4. Real-time updates

[0838] The device obtains real-time congestion information from traffic information services while driving.

[0839] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[0840] The device notifies the user of the updated route information and displays it on the map screen.

[0841] User operation

[0842] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of real-time route updates while driving, the user follows the instructions and heads to the destination on a new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route.

[0843] Specific examples

[0844] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[0845] 1. Data Collection

[0846] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[0847] The terminal transmits this data to the server.

[0848] 2. Traffic congestion prediction and route generation

[0849] The server obtains weather information (e.g. sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[0850] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[0851] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[0852] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[0853] 3. Emotional Data Analysis and User Feedback

[0854] If the user becomes frustrated while driving, the emotion engine will detect this and suggest a quieter route that will reduce stress.

[0855] 4. Sending and displaying results

[0856] The server transmits the generated route information to the terminal.

[0857] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[0858] 5. Real-time updates and navigation

[0859] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0860] The terminal acquires traffic congestion information in real time and sends it to the server.

[0861] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[0862] The device notifies the user of the updated route information and displays it on the map screen.

[0863] In this way, the system provides the user with an efficient driving plan that avoids traffic jams, and also suggests routes that reflect the user's emotions.

[0864] The processing flow will be explained below.

[0865] Step 1: Data entry (user, device)

[0866] The user inputs the departure point (e.g., Tokyo), destination (e.g., Hakone), and departure date and time (e.g., 9:00 AM on a holiday) into the terminal.

[0867] The terminal formats the entered data and sends it to the server.

[0868] Step 2: Collecting weather and calendar information (server)

[0869] The server calls the weather forecast API and obtains weather information for the departure and destination locations for the specified date and time.

[0870] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[0871] Step 3: Obtaining past traffic congestion data (server)

[0872] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[0873] Step 4: Traffic congestion prediction (server)

[0874] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[0875] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[0876] Step 5: Generate Route Information (Server)

[0877] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[0878] Step 6: Emotion data analysis (server)

[0879] The server uses an emotion engine to analyze the user's emotion data (voice and facial expressions).

[0880] To detect a state in which a user is feeling stressed.

[0881] Step 7: Route adjustment according to user sentiment (server)

[0882] The server generates routes to reduce the user's stress and adds them to the regular route suggestions.

[0883] Step 8: Sending Route Information (Server)

[0884] The server transmits the generated route information to the terminal.

[0885] Step 9: View Route Information (Device)

[0886] The terminal draws the optimum route and detour route received from the server on a map screen.

[0887] The travel times for the main route and the detour route are also displayed to the user.

[0888] Step 10: Emotion Monitoring (Device)

[0889] The device acquires the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[0890] If the user is feeling stressed, the information is sent to the server.

[0891] Step 11: Navigation Start (User, Device)

[0892] The user starts the navigation function of the terminal and drives the car.

[0893] The terminal guides the user along the optimum route.

[0894] Step 12: Obtaining real-time traffic congestion information (device)

[0895] The device obtains traffic congestion information from the traffic information service in real time while driving.

[0896] Step 13: Recalculating the route (terminal, server)

[0897] The terminal transmits the acquired real-time traffic congestion information to the server.

[0898] The server recalculates route information based on real-time data.

[0899] Step 14: Sending and displaying updated routes (server, terminal)

[0900] The server sends the new route information to the terminal.

[0901] The terminal notifies the user of the updated route information and reflects it on the map screen.

[0902] Step 15: User arrives at destination

[0903] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[0904] This series of steps allows users to avoid traffic jams, reach their destination efficiently, and even suggests routes based on the user's emotions.

[0905] Example 2

[0906] 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."

[0907] Conventional navigation systems can predict traffic congestion and provide optimal routes using traffic and weather information, but they do not take the user's emotional state into account when proposing routes. As a result, even if the user is feeling stressed, only efficient routes are provided, which can potentially reduce the user's comfort. Furthermore, systems must be able to respond quickly to real-time traffic conditions, and a system that can reliably do so was needed.

[0908] 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.

[0909] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for analyzing user emotion data, means for adjusting the route based on the analyzed emotion data, and means for acquiring real-time congestion information and updating the route, thereby enabling efficient and real-time optimal route suggestions while taking user comfort into consideration.

[0910] "Traffic information" refers to information about road congestion, traffic regulations, accidents, etc.

[0911] "Weather information" refers to meteorological data and forecasts that indicate weather conditions at a particular time or location.

[0912] "Calendar information" is information about events, holidays, vacations, etc. associated with a specific date.

[0913] "Traffic congestion forecasting" is the prediction of future road congestion conditions based on collected traffic information and related data.

[0914] A "route" is a path from a starting point to a destination.

[0915] An "optimal route" is a route that is determined to be the most efficient, taking into account factors such as travel time, distance, and traffic conditions.

[0916] A "detour route" is an alternative route set aside from the main route to meet specific conditions (e.g., avoiding congestion, reducing stress).

[0917] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's voice, facial expression, etc.

[0918] "Emotion analysis" refers to identifying a user's emotional state based on collected emotional data.

[0919] "Real-time information" refers to information provided based on ongoing situations and the most recent data.

[0920] This invention is a system that helps users avoid traffic jams and reach their destinations, and combines an emotion engine that recognizes the user's emotions. This system is installed in devices such as smartphones and car navigation systems, and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[0921] Specifically, this system is broadly divided into server-side and terminal-side processing.

[0922] Server-side processing

[0923] Data collection

[0924] The server first collects traffic, weather, calendar, and historical congestion information using the following APIs and databases:

[0925] Weather forecast APIs (e.g., OpenWeatherMap API)

[0926] Calendar information API (e.g. Google Calendar API)

[0927] Traffic database: Stores historical traffic information and retrieves data by issuing SQL queries

[0928] Traffic congestion forecast

[0929] Based on the collected data, the server uses traffic prediction algorithms to predict congestion. Using the integrated dataset, machine learning models are applied to predict future congestion information.

[0930] Route Generation

[0931] The server generates main routes and detour routes based on predicted traffic congestion data. It optimizes routes using Google Maps API and Microsoft Bing Maps API. It also generates special routes, such as stress-reducing routes, based on the analysis of user emotion data.

[0932] Emotional Data Analysis

[0933] The server uses an emotion engine to analyze emotion data from the user's voice and facial expressions, utilizing IBM Watson Speech to Text API, IBM Watson Tone Analyzer, and Microsoft Azure Face API.

[0934] Sending the results

[0935] The generated route information and sentiment analysis results are sent to the device via a RESTful API via an HTTPS request.

[0936] Terminal side processing

[0937] Data input from the user

[0938] The user inputs the departure point, destination, and departure date and time into the terminal, and the input data is sent to the server.

[0939] View route information

[0940] The device displays the route information received from the server on a map screen, along with the travel times for the main route and detour routes, providing the user with a choice of routes.

[0941] Emotion Monitoring

[0942] The device captures the user's voice and facial expressions in real time and analyzes them with an emotion engine. If stress or irritation is detected, the information is sent to the server.

[0943] Real-time updates

[0944] The device obtains real-time traffic congestion information from a traffic information service (e.g., Google Traffic API) and sends it to the server. If new route information is available, it is displayed on the map screen and notified to the user.

[0945] Specific examples

[0946] Consider a case where a user goes for a drive from Tokyo to Hakone at 9:00 a.m. on a holiday.

[0947] 1. Data Collection

[0948] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal, and the terminal transmits this data to the server.

[0949] 2. Traffic congestion prediction and route generation

[0950] The server retrieves weather information for Tokyo and Hakone from the weather forecast API (e.g. sunny).

[0951] Obtain information from the calendar information API that the relevant date is a holiday.

[0952] Information on days with similar conditions is obtained from past traffic congestion data to predict traffic congestion.

[0953] The server generates the main route (e.g., Tomei Expressway) and the detour route (e.g., general roads) and calculates the required travel time.

[0954] 3. Emotional Data Analysis and User Feedback

[0955] If the user becomes irritated while driving, the emotion engine will detect this and suggest a quieter route to reduce stress.

[0956] 4. Sending and displaying results

[0957] The server sends the generated route information to the device, which then displays the route information on a map screen, along with the travel times for the main route and detour routes.

[0958] 5. Real-time updates and navigation

[0959] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[0960] The terminal acquires traffic congestion information in real time and sends it to the server.

[0961] When traffic congestion occurs, the server calculates a new detour route and sends it to the device, which then displays the updated route information on the map screen and notifies the user.

[0962] This system provides users with efficient driving plans that avoid traffic jams, and can also suggest routes based on the user's emotions.In addition, by utilizing real-time information, route guidance is always based on the latest conditions.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Step 1: Enter user data

[0965] The user inputs the origin, destination, and departure date and time into the device. The device retrieves this data and sends it to the server in a structured format (e.g., JSON). The server then starts optimizing the route based on the origin, destination, and departure date and time.

[0966] Step 2: Get weather information

[0967] The server sends a request to a weather forecast API (e.g., OpenWeatherMap API) to get current and forecast weather information for the origin and destination. As input, the API request is sent, and as output, weather information (e.g., temperature, chance of precipitation, and weather conditions) is returned in JSON format.

[0968] Step 3: Get Calendar Information

[0969] The server retrieves event information (e.g., public holidays, elementary and junior high school holidays) related to the specified departure date and time from a calendar information API (e.g., Google Calendar API). An API query is sent as input, and calendar event information is retrieved as output.

[0970] Step 4: Obtaining historical traffic congestion information

[0971] The server issues a query to the traffic database to retrieve historical traffic data for days with similar conditions to the specified date and time. The input is an SQL query, and the output is a dataset containing historical traffic information.

[0972] Step 5: Data integration and congestion prediction

[0973] The server integrates the collected weather information, calendar information, and past traffic congestion information and inputs it into a machine learning model. The input is the integrated dataset, and the output is the traffic congestion prediction result. Specifically, the model takes into account information such as weather and event dates to predict the probability of traffic congestion occurring.

[0974] Step 6: Generate main and detour routes

[0975] The server uses the Google Maps API and Microsoft Bing Maps API to generate optimal routes and detour routes based on predicted congestion data. The inputs are the departure point, destination, and congestion forecast data, and the output is route information and travel time for each route.

[0976] Step 7: Analyze the sentiment data

[0977] The device captures the user's voice and facial expression data in real time and sends it to the server, which then analyzes the data using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stress level).

[0978] Step 8: Adjust the route information

[0979] The server analyzes the user's emotional state and adjusts the route as necessary. If the user is feeling stressed, it will suggest a quieter route or a more scenic route. The input is the emotion analysis result, and the output is the adjusted route information.

[0980] Step 9: Submit route information

[0981] The server sends the generated and adjusted route information to the terminal. The route information is the input and the transmission to the terminal is the output.

[0982] Step 10: View Route Information

[0983] The terminal displays the route information received from the server on a map screen. Route information is input, and the route is displayed to the user as output. The required times for the main route and detour routes are also displayed, helping the user select the optimal route.

[0984] Step 11: Real-time updates

[0985] While driving, the device obtains real-time traffic congestion information from the traffic information service and sends it to the server. The server recalculates the route based on the new information and sends the optimal route and detour route to the device. The real-time traffic congestion information is the input, and the updated route information is the output.

[0986] Through these steps, the present invention provides comfortable and efficient navigation for the user.

[0987] (Application example 2)

[0988] 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."

[0989] Conventional navigation systems provide optimal routes based on traffic and weather information, but do not take into account the user's emotional state. As a result, users are likely to feel stressed due to traffic congestion and other traffic conditions. Even in cases where real-time updates are provided regarding traffic avoidance and route changes, route selection is not yet possible, taking into account the user's mental state. To solve this problem, it is necessary to propose optimal routes that take the user's emotions into account.

[0990] 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.

[0991] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time traffic information and updating the route, means for recognizing the user's emotions, and means for providing a route according to the user's emotional state, thereby making it possible to provide a stress-reducing route according to the user's emotional state.

[0992] "Traffic information" refers to data on road usage, such as road congestion, traffic congestion information, and whether or not road construction is occurring.

[0993] "Traffic congestion prediction" is the prediction of future road congestion based on past traffic data, current traffic conditions, and other related information.

[0994] "Weather information" is data on weather conditions such as temperature, probability of precipitation, and wind speed in a specified area.

[0995] "Calendar information" is schedule data such as holidays, school holidays, special events, etc., that correspond to specific dates and times.

[0996] "Past congestion information" is recorded data relating to road congestion conditions at specific past dates and times.

[0997] The "means for calculating a route" is a means for calculating the optimal route from a departure point to a destination using traffic information, weather information, calendar information, and past congestion information.

[0998] The "optimal route" is the optimal route calculated based on predicted congestion data, taking into account the required time, distance, traffic conditions, etc.

[0999] A "detour route" is an alternative route to avoid congestion on a designated main route.

[1000] "Generated route information" refers to data relating to the calculated and generated optimum route and detour route.

[1001] A "terminal" is an electronic device that displays information and exchanges data between a user and the Internet.

[1002] "Real-time traffic congestion information" is data that provides current road traffic conditions in real time.

[1003] The "means for updating a route" is a means for recalculating an existing route based on new traffic information and providing the latest route information.

[1004] "User emotions" refers to a person's psychological state or mood, and includes emotions such as stress, frustration, and relief.

[1005] "Means for recognizing emotions" refers to means for identifying the emotional state of a user through voice analysis, facial expression analysis, etc.

[1006] A "route according to emotional state" is a route selected to reduce stress, taking into consideration the user's current emotions.

[1007] This invention relates to a system that predicts congestion based on traffic information, recognizes the user's emotions, and provides the optimal route. This system is realized through the interaction between a server, a terminal, and a user.

[1008] The server includes a means for predicting congestion based on traffic information, a means for acquiring weather and calendar information, a means for calculating routes based on past congestion information, a means for generating optimal routes and detour routes based on predicted congestion data, a means for transmitting the generated route information to the terminal, a means for acquiring real-time congestion information and updating routes, a means for recognizing user emotions, and a means for providing routes according to the user's emotional state. This server generally operates in a cloud computing environment connected to the Internet. Specifically, it is implemented using a backend service using Python or JavaScript (e.g., Django, Node.js).

[1009] The device displays route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary. This device is typically a smartphone or car navigation system, and is implemented as an Android or iOS application. Specifically, map services such as Google Maps API are used to obtain and display real-time data, and libraries such as EmotionEngine are used for emotion analysis.

[1010] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination on the new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route. Emotion recognition uses voice input and facial expression analysis via a camera.

[1011] Specifically, the server obtains weather information from a weather information API and event information for a specific day from a calendar information API. It then obtains past traffic congestion information from a traffic database and predicts traffic congestion based on this information. In this way, an optimal route and detour route are generated and sent to the terminal. The terminal then displays this route information on a map screen and provides it to the user.

[1012] While driving, the user's voice and facial expression data are collected by the device and analyzed by the emotion engine. If the user feels stressed, this information is sent to the server, which then calculates a new detour route that corresponds to the user's emotions. This allows the user to reach their destination via a more comfortable route.

[1013] As a concrete example, consider a user heading from Tokyo to Hakone at 9 a.m. on a holiday. The system generates a main route and a detour route based on weather forecasts, holiday information, and past traffic congestion data. If the user's emotion is detected as irritation while driving, the emotion engine recognizes this and suggests changing the route to a quieter route on the server side.

[1014] An example prompt is, "If the user feels stressed while driving, the emotion engine should detect this and suggest a quieter route in real time. Also, notify the user of the new route along with the estimated travel time and distance."

[1015] In this way, the present invention can provide the user with a comfortable driving plan that is less stressful.

[1016] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1017] Step 1: User Input

[1018] The user inputs the departure point, destination, and departure date and time into the terminal.

[1019] Input data: origin, destination, departure date and time.

[1020] Output data: The input data is sent to the server.

[1021] Step 2: Get weather information

[1022] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1023] Input data: origin, destination.

[1024] Data processing: Obtain weather information using API.

[1025] Output data: Weather information.

[1026] Step 3: Get Calendar Information

[1027] The server uses the calendar information API to obtain information about holidays and special events for the relevant day.

[1028] Input data: Departure date and time.

[1029] Data processing: Calendar information is obtained using API.

[1030] Output data: Calendar information.

[1031] Step 4: Obtaining historical traffic congestion information

[1032] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1033] Input data: origin, destination, departure date and time.

[1034] Data processing: Obtain past traffic congestion information using database queries.

[1035] Output data: Past traffic congestion information.

[1036] Step 5: Traffic congestion prediction

[1037] The server uses a traffic prediction algorithm to predict congestion at a specified date and time based on collected weather information, calendar information, and past congestion information.

[1038] Input data: Weather information, calendar information, past traffic congestion information.

[1039] Data calculation: Traffic prediction algorithms are applied to predict congestion.

[1040] Output data: Predicted congestion data.

[1041] Step 6: Route Generation

[1042] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1043] Input data: predicted traffic congestion data, origin, destination.

[1044] Data calculation: Route calculation algorithm is applied to generate a route.

[1045] Output data: Optimal route, detour route.

[1046] Step 7: Submit the generated route information

[1047] The server transmits the generated route information to the terminal.

[1048] Input data: Generated route information.

[1049] Output data: Route information is sent to the terminal.

[1050] Step 8: View Route Information

[1051] The terminal displays the optimum route and detour route received from the server on a map screen to provide navigation.

[1052] Input data: Route information.

[1053] Output data: Route information is displayed on the map screen.

[1054] Step 9: Collect emotion data

[1055] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1056] Input data: User's voice data and facial expression data.

[1057] Data Computation: Analyze emotional state using emotion engine.

[1058] Output data: emotion data.

[1059] Step 10: Sending Emotion Data

[1060] The device transmits the emotion data to the server.

[1061] Input data: Emotion data.

[1062] Output data: Emotion data is sent to the server.

[1063] Step 11: Recalculate the route

[1064] The server recalculates the travel time of the detour route based on the emotion data and real-time traffic congestion information.

[1065] Input data: Emotion data, real-time traffic congestion information.

[1066] Data calculation: Route recalculation based on emotion data and traffic congestion information.

[1067] Output data: Updated route information.

[1068] Step 12: Submit updated route information

[1069] The server sends the updated route information to the terminal.

[1070] Input data: Updated route information.

[1071] Output data: Updated route information is sent to the device.

[1072] Step 13: View the updated route

[1073] The terminal displays the updated route information on the map screen and notifies the user.

[1074] Input data: Updated route information.

[1075] Output data: Updated route information is displayed on the map screen.

[1076] Step 14: Navigation Update

[1077] The user follows the instructions on the device and heads to the destination via a new route.

[1078] Input data: Updated route information.

[1079] Output data: User's driving route corrections.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] [Third embodiment]

[1084] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1085] 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.

[1086] 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).

[1087] 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.

[1088] 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.

[1089] 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).

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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."

[1096] The present invention is a system that enables users to reach their destination while avoiding traffic jams, and is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"). This system has the function of calculating the optimal route based on traffic information, weather information, calendar information, and past traffic jam information, and updating the route to reflect real-time information.

[1097] Program processing overview

[1098] Server-side processing

[1099] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route.

[1100] 1. Data Collection

[1101] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[1102] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1103] The server searches and retrieves information on traffic congestion that occurred under similar conditions in the past from a traffic database.

[1104] 2. Traffic congestion prediction

[1105] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion for a specified date and time.

[1106] 3. Route Generation

[1107] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1108] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[1109] 4. Sending the results

[1110] The server transmits the generated route information to the terminal.

[1111] Terminal side processing

[1112] The device displays the route information received from the server, provides navigation, and updates the route based on real-time traffic congestion information.

[1113] 1. Data Entry

[1114] The user inputs the departure point, destination, and departure date and time into the terminal.

[1115] The terminal transmits the input data to the server.

[1116] 2. Route display

[1117] The terminal displays the optimum route and detour route received from the server on a map screen.

[1118] It also displays the travel times for the main route and alternate routes, giving users a choice.

[1119] 3. Real-time updates

[1120] The device obtains real-time congestion information from traffic information services while driving.

[1121] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[1122] The terminal displays the updated route information and notifies the user.

[1123] User operation

[1124] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination along the new route.

[1125] Specific examples

[1126] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1127] 1. Data Collection

[1128] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1129] The terminal transmits this data to the server.

[1130] 2. Traffic congestion prediction and route generation

[1131] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[1132] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[1133] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[1134] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[1135] 3. Sending and displaying results

[1136] The server transmits the generated route information to the terminal.

[1137] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1138] 4. Real-time updates and navigation

[1139] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1140] The terminal acquires traffic congestion information in real time and sends it to the server.

[1141] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1142] The device notifies the user of the updated route information and displays it on the map screen.

[1143] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[1144] The processing flow will be explained below.

[1145] Step 1: Data entry (user, device)

[1146] The user inputs the departure point, destination, and departure date and time into the terminal.

[1147] The terminal formats the entered data and sends it to the server.

[1148] Step 2: Collecting weather and calendar information (server)

[1149] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1150] The server uses a calendar API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[1151] Step 3: Obtaining past traffic congestion data (server)

[1152] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1153] Step 4: Traffic congestion prediction (server)

[1154] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[1155] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[1156] Step 5: Generate Route Information (Server)

[1157] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[1158] Step 6: Sending Route Information (Server)

[1159] The server transmits the generated route information to the terminal.

[1160] Step 7: View Route Information (Device)

[1161] The terminal draws the optimum route and detour route received from the server on a map screen.

[1162] The travel times for the main route and the detour route are also displayed to the user.

[1163] Step 8: Navigation Start (User, Device)

[1164] The user starts the navigation function of the terminal and drives the car.

[1165] The terminal guides the user along the optimum route.

[1166] Step 9: Obtaining real-time traffic congestion information (device)

[1167] The device obtains traffic congestion information from the traffic information service in real time while driving.

[1168] Step 10: Route recalculation (terminal, server)

[1169] The terminal transmits the acquired real-time traffic congestion information to the server.

[1170] The server recalculates route information based on real-time data.

[1171] Step 11: Sending and displaying updated routes (server, terminal)

[1172] The server sends the new route information to the terminal.

[1173] The terminal notifies the user of the updated route information and reflects it on the map screen.

[1174] Step 12: User arrives at destination

[1175] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[1176] This series of steps allows users to avoid traffic jams and reach their destination efficiently.

[1177] Example 1

[1178] 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."

[1179] Conventional navigation systems have difficulty using real-time traffic congestion information to provide users with the optimal route. They also lack the functionality to predict traffic conditions using weather and calendar information and provide users with the optimal route. Furthermore, they are unable to provide detailed traffic congestion predictions using past traffic congestion information based on the departure point, destination, and departure date and time.

[1180] 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.

[1181] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and date and time information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted traffic data, means for transmitting the generated route information to a device, means for acquiring real-time traffic information and updating the route, means for a user to input a departure point, a destination point, and a departure time, means for displaying the optimal route and the detour route on a map screen, means for acquiring real-time traffic information from a traffic information service while driving and updating the route, and means for displaying the route information generated in real time on a map screen and notifying the user. This makes it possible to provide the user with an optimal route and a detour route based on real-time information.

[1182] "Traffic information" refers to data relating to traffic conditions, including congestion, accidents, road construction, traffic volume, and the like.

[1183] "Traffic congestion forecast" is the process of predicting road congestion at a specified date and time based on various data.

[1184] "Weather information" is data related to weather forecasts, including temperature, precipitation, wind speed, and the like.

[1185] "Date and time information" is data related to a calendar, and includes information about a specific date and time.

[1186] "Historical traffic information" refers to previously recorded traffic data, including traffic performance under specific conditions and dates.

[1187] A "route" is a route from a starting point to a destination, including a main route and a detour route.

[1188] An "optimal route" is the best route calculated taking into account various conditions (travel time, distance, congestion, etc.).

[1189] A "detour route" is an alternative route that is chosen when the normal route is difficult due to traffic congestion or other reasons.

[1190] "Device" refers to the terminal used by the user, including smartphones, car navigation systems, etc.

[1191] "Real-time information" means instantly updated data about current traffic conditions.

[1192] "User" refers to a person who operates and uses this system.

[1193] The "map screen" is a visual interface that displays route information, current location, etc.

[1194] "Traffic Information Service" means an external information platform that provides real-time traffic conditions.

[1195] The present invention is a system that enables users to avoid traffic jams and reach their destinations based on traffic information, weather information, date and time information, and past traffic information. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals").

[1196] Hardware and Software

[1197] server:

[1198] The server uses the traffic forecast database, weather information API, date and time information API, and traffic information API to collect and analyze various data based on user requests. Specifically, it uses the OpenWeatherMap API to obtain weather information, the Google Calendar API to obtain date and time information, and the HERE Traffic API to obtain past traffic information.

[1199] Device:

[1200] The terminal provides an interface for the user to input the starting point, destination point, and departure time, and displays the route information received from the server on a map screen. The terminal also obtains traffic data in real time from a traffic information service (e.g., Google Maps API) and sends it to the server.

[1201] User:

[1202] The user inputs the departure point, destination, and departure date and time into the device and refers to the information on the optimal route and detour route sent from the server. If the device notifies the user of route updates in real time while driving, the user follows the instructions and heads to the destination on the new route.

[1203] Data processing and calculation

[1204] Data collection:

[1205] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[1206] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1207] The server searches and retrieves traffic information that occurred under similar conditions in the past from a traffic database.

[1208] Traffic congestion forecast:

[1209] Based on the collected weather information, calendar information, and past traffic information, the server uses a traffic prediction algorithm to predict congestion at a specified date and time.

[1210] Route Generation:

[1211] The server generates the optimal route from the departure point to the destination and a detour route based on the predicted traffic data, taking into account various conditions (travel time, distance, road type, etc.) to avoid traffic congestion.

[1212] Specific examples

[1213] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1214] Data collection:

[1215] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1216] The terminal transmits this data to the server.

[1217] Weather information, calendar information, traffic forecast:

[1218] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from a weather forecast API (e.g., OpenWeatherMap API).

[1219] Obtain information from the Google Calendar API about holidays on the relevant dates and events that may attract a large number of tourists.

[1220] The HERE Traffic API is used to obtain traffic data from similar conditions in the past and predict the occurrence of traffic congestion.

[1221] Route Generation:

[1222] Based on the predicted data, the server generates the main route (e.g., expressway) and detour route (e.g., general road) and calculates the required travel time.

[1223] Send and view results:

[1224] The server transmits the generated route information to the terminal.

[1225] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1226] Real-time updates:

[1227] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1228] While driving, the device obtains real-time traffic information from the traffic information service and transmits it to the server.

[1229] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1230] The device notifies the user of the updated route information and displays it on the map screen.

[1231] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[1232] Example prompts to input to the generative AI model

[1233] "I want to create a system that calculates the optimal driving route to avoid traffic jams. I'm thinking of using weather forecast APIs, calendar information APIs, and traffic information APIs, but I'd like to know how to collect the data, calculate the route, and notify the user."

[1234] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1235] Step 1: Data entry

[1236] The user inputs the departure point, destination, and departure date and time into the terminal. For example, the user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time. This input data is sent from the terminal to the server.

[1237] Input: Departure point, destination, departure date and time

[1238] Output: The input data sent to the server

[1239] Step 2: Get weather information

[1240] The server uses weather information APIs to obtain weather information for the specified date and time near the departure and destination locations. For example, the server uses the OpenWeatherMap API to obtain detailed weather forecasts (temperature, precipitation, wind speed, etc.) for Tokyo and Hakone.

[1241] Input: Departure point, destination, departure date and time

[1242] Output: Weather information (temperature, precipitation, wind speed, etc.)

[1243] Step 3: Get Calendar Information

[1244] The server uses a date and time information API to obtain information about holidays and events for the specified date. For example, it uses the Google Calendar API to obtain information about holidays, school holidays, and other events for the specified date.

[1245] Input: Departure date and time

[1246] Output: Calendar information (holidays, school vacations, event information, etc.)

[1247] Step 4: Obtaining historical traffic information

[1248] The server uses a traffic information API to obtain traffic data that occurred under similar conditions in the past. For example, it uses the HERE Traffic API to obtain data on traffic conditions similar to the specified date and time and conditions.

[1249] Input: Departure point, destination, departure date and time

[1250] Output: Past traffic information

[1251] Step 5: Traffic congestion prediction

[1252] The server uses a traffic prediction algorithm to predict congestion for a specified date and time based on collected weather, calendar, and past traffic information, and generates forecast data on which roads will be congested at which times.

[1253] Input: Weather information, calendar information, past traffic information

[1254] Output: Traffic congestion forecast data (predicted congestion situation)

[1255] Step 6: Route Generation

[1256] The server generates the optimal route and detour routes from the departure point to the destination based on the predicted congestion data. Specifically, it calculates the main route and detour routes, taking into account factors such as travel time, distance, and road type.

[1257] Input: Traffic congestion forecast data

[1258] Output: Optimal route, detour route

[1259] Step 7: Sending the results

[1260] The server sends the generated route information to the device, including information on the optimal route, detours, travel time, and distance. The server sends this data in JSON format to the device.

[1261] Input: Optimal route, detour route

[1262] Output: Route information sent to the device

[1263] Step 8: View Route

[1264] The device receives route information from the server and displays it on a map screen, along with the travel times for the main route and detour routes, allowing the user to select the optimal route.

[1265] Input: Route information sent from the server

[1266] Output: Optimal route and detour route displayed on the map screen

[1267] Step 9: Real-time information acquisition

[1268] While driving, the device receives real-time traffic information from traffic information services (e.g., Google Maps API), allowing the user to understand current traffic congestion and accident information.

[1269] Input: Real-time information from traffic information services

[1270] Output: Real-time traffic information

[1271] Step 10: Route Update

[1272] The device sends real-time traffic information to the server and requests the server to calculate a new route. The server then calculates a new detour route based on this information and sends it to the device.

[1273] Input: Real-time traffic information

[1274] Output: New route information

[1275] Step 11: View the updated route

[1276] The device will notify the user of the updated route information and display the new route information on the map screen, allowing the user to adjust their driving route based on the latest information.

[1277] Input: New route information sent from the server

[1278] Output: Updated route information displayed on the map screen

[1279] (Application example 1)

[1280] 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."

[1281] Conventional navigation systems only predict congestion based on traffic information and present optimal routes, which can lead to problems with real-time performance and accuracy in autonomous vehicles. Furthermore, congestion conditions can vary significantly depending on city conditions and weather, and these systems are unable to adequately address these issues. Therefore, there is a need for a system that improves usability and provides a comfortable travel experience in autonomous vehicles.

[1282] 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.

[1283] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time congestion information and updating the route, means for operating the autonomous driving function, and means for displaying the route information on the in-vehicle display. This makes it possible to respond to changes in traffic conditions due to weather or events, and to simultaneously realize real-time route optimization and operation of the autonomous driving function.

[1284] "Traffic information" refers to information on the flow of traffic on roads, congestion, accidents, road construction, etc.

[1285] "Means for predicting traffic congestion" refers to algorithms that analyze existing and past traffic data and predict the occurrence of future traffic congestion.

[1286] "Weather information" is data relating to weather conditions in a specified area and at a specified date and time.

[1287] "Calendar information" is information about schedules such as holidays, school holidays, and events on specific dates.

[1288] "Method of calculating routes based on past congestion information" refers to an algorithm that uses past traffic data to calculate the optimal travel route.

[1289] The "means for generating optimal routes and detour routes based on predicted congestion data" refers to algorithms and mechanisms that calculate primary and alternative routes taking into account future traffic conditions.

[1290] The "means for transmitting generated route information to the terminal" is a function for transmitting the calculated travel route to the user's device via a communication means.

[1291] "Means for obtaining real-time traffic congestion information and updating routes" refers to a function that obtains the latest traffic conditions in real time while traveling and recalculates and updates the route if necessary.

[1292] "Means for operating autonomous driving functions" refers to a function that issues instructions to the autonomous driving system of an autonomous vehicle and controls the driving of the vehicle.

[1293] "Means for displaying route information on an in-vehicle display" refers to a function for visually displaying the calculated travel route and related information on a display device inside the vehicle.

[1294] This invention provides a system that allows users to use autonomous vehicles to select the optimal route based on traffic information, weather information, calendar information, and past congestion information, and reach their destination safely and comfortably. This system works in conjunction with the navigation software and on-board display of the autonomous vehicle.

[1295] System configuration

[1296] The system mainly consists of three components: a server, a terminal, and a user. The functions of each component are as follows:

[1297] server

[1298] The server collects and analyzes traffic information, weather information, calendar information, and past congestion information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route. Specifically, information is collected using a weather information API, calendar information API, and traffic database. The collected data is analyzed using a traffic prediction algorithm, and congestion predictions are made for the specified date and time. The optimal route and detour route are then generated and sent to the user's device.

[1299] Terminal

[1300] The terminal is installed in an autonomous vehicle and includes an on-board display and autonomous driving system. The terminal displays route information received from the server and provides navigation. It also has the function of updating routes based on traffic congestion information obtained in real time. The terminal obtains real-time traffic information through a GPS module and an internet connection, and sends the information to the server to calculate a new route as needed.

[1301] User

[1302] The user inputs the departure point, destination, and departure date and time into the device. Following the device's guidance, the user begins driving to the destination along the optimal route. If the device notifies the user of real-time route updates during autonomous driving, the user follows those instructions.

[1303] Specific examples

[1304] As a specific example, consider the case of heading from Tokyo to a tourist destination at 9:00 AM on a holiday. The user enters "Tokyo" as the departure point, "tourist destination" as the destination, and "9:00 AM on a holiday" as the departure date and time into the device. The device sends this data to the server. The server obtains weather information for Tokyo and the tourist destination from a weather forecast API and information that the relevant day is a holiday or that there may be a large number of tourists from a calendar information API. It obtains traffic congestion information under similar conditions from past traffic congestion data and predicts the occurrence of traffic congestion. The server generates a main route and a detour route based on the predicted data and calculates the required travel time. The server sends the generated route information to the device. The device displays the received route information on the in-vehicle display, along with the required travel time for the main route and the detour route. The user follows the device's guidance and begins driving toward the destination tourist destination via the optimal route. The device obtains traffic congestion information in real time and sends it to the server. If traffic congestion occurs, the server calculates a new detour route and sends it to the device. The device notifies the user of the updated route information and displays it on the in-vehicle display. In this way, the system can provide the user with an optimal driving plan that avoids traffic jams.

[1305] Example prompt sentences to use

[1306] Example prompts that may be used include:

[1307] "Calculate the best route from Tokyo to a tourist spot."

[1308] "Please tell me the route taking into account today's weather and traffic information."

[1309] Please suggest an alternative route to avoid the traffic jam.

[1310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1311] Step 1:

[1312] The user inputs the departure point, destination, and departure date and time into the terminal. The input data includes the departure point, destination, departure date and time, and is transmitted to the server by the terminal.

[1313] Step 2:

[1314] The server uses the weather forecast API to obtain weather information for the specified date and time near the departure point and destination. The input is the departure point and destination information, and based on that, a request is sent to the API to obtain weather information data. The output is the weather information for the specified date and time.

[1315] Step 3:

[1316] The server uses the calendar information API to obtain information about holidays, elementary and junior high school holidays, and other event information for the relevant day. The input is the departure date and time information, and based on that, a request is sent to the calendar API to obtain event information data. The output is calendar information.

[1317] Step 4:

[1318] The server searches and retrieves traffic information from a traffic database about traffic congestion that occurred under similar conditions in the past. The inputs include the departure point, destination, departure date and time, and a database query is executed based on these to retrieve traffic congestion information. The output is past traffic congestion information.

[1319] Step 5:

[1320] The server runs a traffic prediction algorithm based on the collected weather information, calendar information, and past traffic congestion information to predict traffic congestion for a specified date and time. The inputs are the acquired weather information, calendar information, and past traffic congestion information, which are combined and analyzed by the algorithm. The output is traffic congestion prediction data.

[1321] Step 6:

[1322] The server generates the optimal route and detour route from the departure point to the destination point based on the predicted congestion data. The inputs are the congestion prediction data, the departure point, and the destination, and the route generation algorithm is executed based on this information. The output is the optimal route and detour route information.

[1323] Step 7:

[1324] The server sends the generated route information to the terminal. The input is the generated route information, and the server executes a communication operation to send it to the terminal. The output is the route information that has been sent to the terminal.

[1325] Step 8:

[1326] The terminal displays the optimal route and detour route received from the server on the in-vehicle display. The input is the route information received from the server, and the terminal performs operations to display it on the display screen. The output is a visual display of the route information.

[1327] Step 9:

[1328] The device obtains real-time traffic information from the traffic information service while driving. The input is the current GPS location and a real-time traffic information API request, and the latest traffic information is obtained from the API. The output is real-time traffic information.

[1329] Step 10:

[1330] The terminal sends the acquired real-time traffic congestion information to the server and requests the calculation of a new route. The input is the real-time traffic congestion information, and the terminal performs a communication operation to send this information to the server. The output is the traffic congestion information that has been sent to the server.

[1331] Step 11:

[1332] The terminal receives the new detour route from the server and displays it on the in-vehicle display. The input is the updated route information, and the terminal performs an operation to display it again on the display. The output is the updated route information being displayed.

[1333] This series of steps allows users to always use the latest and most appropriate navigation information, enabling them to reach their destination smoothly.

[1334] 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.

[1335] This invention is a system that helps users avoid traffic jams and reach their destination, and combines an emotion engine that recognizes the user's emotions. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"), and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1336] Program processing overview

[1337] Server-side processing

[1338] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates optimal routes and detour routes.The server also analyzes the user's emotional data and provides routes that correspond to the user's emotional state.

[1339] 1. Data Collection

[1340] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1341] The server uses the calendar information API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[1342] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1343] 2. Traffic congestion prediction

[1344] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion at a specified date and time.

[1345] 3. Route Generation

[1346] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1347] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[1348] 4. Emotion Data Analysis

[1349] The server uses an emotion engine to analyze the user's emotion data, which includes analyzing the user's voice and facial expressions.

[1350] 5. Route adjustment based on user sentiment

[1351] If the user is feeling stressed, the server generates a route to alleviate that stress and adds it to the regular route suggestions.

[1352] 6. Submitting the results

[1353] The server transmits the generated route information to the terminal.

[1354] Terminal side processing

[1355] The device displays the route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary.

[1356] 1. Data Entry

[1357] The user inputs the departure point, destination, and departure date and time into the terminal.

[1358] The terminal transmits the input data to the server.

[1359] 2. Route display

[1360] The terminal displays the optimum route and detour route received from the server on a map screen.

[1361] It also displays the travel times for the main route and alternate routes, giving users a choice.

[1362] 3. Emotion Monitoring

[1363] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1364] If stress or irritation is detected, the information is sent to the server.

[1365] 4. Real-time updates

[1366] The device obtains real-time congestion information from traffic information services while driving.

[1367] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[1368] The device notifies the user of the updated route information and displays it on the map screen.

[1369] User operation

[1370] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of real-time route updates while driving, the user follows the instructions and heads to the destination on a new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route.

[1371] Specific examples

[1372] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1373] 1. Data Collection

[1374] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1375] The terminal transmits this data to the server.

[1376] 2. Traffic congestion prediction and route generation

[1377] The server obtains weather information (e.g. sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[1378] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[1379] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[1380] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[1381] 3. Emotional Data Analysis and User Feedback

[1382] If the user becomes frustrated while driving, the emotion engine will detect this and suggest a quieter route that will reduce stress.

[1383] 4. Sending and displaying results

[1384] The server transmits the generated route information to the terminal.

[1385] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1386] 5. Real-time updates and navigation

[1387] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1388] The terminal acquires traffic congestion information in real time and sends it to the server.

[1389] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1390] The device notifies the user of the updated route information and displays it on the map screen.

[1391] In this way, the system provides the user with an efficient driving plan that avoids traffic jams, and also suggests routes that reflect the user's emotions.

[1392] The processing flow will be explained below.

[1393] Step 1: Data entry (user, device)

[1394] The user inputs the departure point (e.g., Tokyo), destination (e.g., Hakone), and departure date and time (e.g., 9:00 AM on a holiday) into the terminal.

[1395] The terminal formats the entered data and sends it to the server.

[1396] Step 2: Collecting weather and calendar information (server)

[1397] The server calls the weather forecast API and obtains weather information for the departure and destination locations for the specified date and time.

[1398] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1399] Step 3: Obtaining past traffic congestion data (server)

[1400] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1401] Step 4: Traffic congestion prediction (server)

[1402] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[1403] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[1404] Step 5: Generate Route Information (Server)

[1405] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[1406] Step 6: Emotion data analysis (server)

[1407] The server uses an emotion engine to analyze the user's emotion data (voice and facial expressions).

[1408] To detect a state in which a user is feeling stressed.

[1409] Step 7: Route adjustment according to user sentiment (server)

[1410] The server generates routes to reduce the user's stress and adds them to the regular route suggestions.

[1411] Step 8: Sending Route Information (Server)

[1412] The server transmits the generated route information to the terminal.

[1413] Step 9: View Route Information (Device)

[1414] The terminal draws the optimum route and detour route received from the server on a map screen.

[1415] The travel times for the main route and the detour route are also displayed to the user.

[1416] Step 10: Emotion Monitoring (Device)

[1417] The device acquires the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1418] If the user is feeling stressed, the information is sent to the server.

[1419] Step 11: Navigation Start (User, Device)

[1420] The user starts the navigation function of the terminal and drives the car.

[1421] The terminal guides the user along the optimum route.

[1422] Step 12: Obtaining real-time traffic congestion information (device)

[1423] The device obtains traffic congestion information from the traffic information service in real time while driving.

[1424] Step 13: Recalculating the route (terminal, server)

[1425] The terminal transmits the acquired real-time traffic congestion information to the server.

[1426] The server recalculates route information based on real-time data.

[1427] Step 14: Sending and displaying updated routes (server, terminal)

[1428] The server sends the new route information to the terminal.

[1429] The terminal notifies the user of the updated route information and reflects it on the map screen.

[1430] Step 15: User arrives at destination

[1431] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[1432] This series of steps allows users to avoid traffic jams, reach their destination efficiently, and even suggests routes based on the user's emotions.

[1433] Example 2

[1434] 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."

[1435] Conventional navigation systems can predict traffic congestion and provide optimal routes using traffic and weather information, but they do not take the user's emotional state into account when proposing routes. As a result, even if the user is feeling stressed, only efficient routes are provided, which can potentially reduce the user's comfort. Furthermore, systems must be able to respond quickly to real-time traffic conditions, and a system that can reliably do so was needed.

[1436] 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.

[1437] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for analyzing user emotion data, means for adjusting the route based on the analyzed emotion data, and means for acquiring real-time congestion information and updating the route, thereby enabling efficient and real-time optimal route suggestions while taking user comfort into consideration.

[1438] "Traffic information" refers to information about road congestion, traffic regulations, accidents, etc.

[1439] "Weather information" refers to meteorological data and forecasts that indicate weather conditions at a particular time or location.

[1440] "Calendar information" is information about events, holidays, vacations, etc. associated with a specific date.

[1441] "Traffic congestion forecasting" is the prediction of future road congestion conditions based on collected traffic information and related data.

[1442] A "route" is a path from a starting point to a destination.

[1443] An "optimal route" is a route that is determined to be the most efficient, taking into account factors such as travel time, distance, and traffic conditions.

[1444] A "detour route" is an alternative route set aside from the main route to meet specific conditions (e.g., avoiding congestion, reducing stress).

[1445] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's voice, facial expression, etc.

[1446] "Emotion analysis" refers to identifying a user's emotional state based on collected emotional data.

[1447] "Real-time information" refers to information provided based on ongoing situations and the most recent data.

[1448] This invention is a system that helps users avoid traffic jams and reach their destinations, and combines an emotion engine that recognizes the user's emotions. This system is installed in devices such as smartphones and car navigation systems, and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1449] Specifically, this system is broadly divided into server-side and terminal-side processing.

[1450] Server-side processing

[1451] Data collection

[1452] The server first collects traffic, weather, calendar, and historical congestion information using the following APIs and databases:

[1453] Weather forecast APIs (e.g., OpenWeatherMap API)

[1454] Calendar information API (e.g. Google Calendar API)

[1455] Traffic database: Stores historical traffic information and retrieves data by issuing SQL queries

[1456] Traffic congestion forecast

[1457] Based on the collected data, the server uses traffic prediction algorithms to predict congestion. Using the integrated dataset, machine learning models are applied to predict future congestion information.

[1458] Route Generation

[1459] The server generates main routes and detour routes based on predicted traffic congestion data. It optimizes routes using Google Maps API and Microsoft Bing Maps API. It also generates special routes, such as stress-reducing routes, based on the analysis of user emotion data.

[1460] Emotional Data Analysis

[1461] The server uses an emotion engine to analyze emotion data from the user's voice and facial expressions, utilizing IBM Watson Speech to Text API, IBM Watson Tone Analyzer, and Microsoft Azure Face API.

[1462] Sending the results

[1463] The generated route information and sentiment analysis results are sent to the device via a RESTful API via an HTTPS request.

[1464] Terminal side processing

[1465] Data input from the user

[1466] The user inputs the departure point, destination, and departure date and time into the terminal, and the input data is sent to the server.

[1467] View route information

[1468] The device displays the route information received from the server on a map screen, along with the travel times for the main route and detour routes, providing the user with a choice of routes.

[1469] Emotion Monitoring

[1470] The device captures the user's voice and facial expressions in real time and analyzes them with an emotion engine. If stress or irritation is detected, the information is sent to the server.

[1471] Real-time updates

[1472] The device obtains real-time traffic congestion information from a traffic information service (e.g., Google Traffic API) and sends it to the server. If new route information is available, it is displayed on the map screen and notified to the user.

[1473] Specific examples

[1474] Consider a case where a user goes for a drive from Tokyo to Hakone at 9:00 a.m. on a holiday.

[1475] 1. Data Collection

[1476] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal, and the terminal transmits this data to the server.

[1477] 2. Traffic congestion prediction and route generation

[1478] The server retrieves weather information for Tokyo and Hakone from the weather forecast API (e.g. sunny).

[1479] Obtain information from the calendar information API that the relevant date is a holiday.

[1480] Information on days with similar conditions is obtained from past traffic congestion data to predict traffic congestion.

[1481] The server generates the main route (e.g., Tomei Expressway) and the detour route (e.g., general roads) and calculates the required travel time.

[1482] 3. Emotional Data Analysis and User Feedback

[1483] If the user becomes irritated while driving, the emotion engine will detect this and suggest a quieter route to reduce stress.

[1484] 4. Sending and displaying results

[1485] The server sends the generated route information to the device, which then displays the route information on a map screen, along with the travel times for the main route and detour routes.

[1486] 5. Real-time updates and navigation

[1487] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1488] The terminal acquires traffic congestion information in real time and sends it to the server.

[1489] When traffic congestion occurs, the server calculates a new detour route and sends it to the device, which then displays the updated route information on the map screen and notifies the user.

[1490] This system provides users with efficient driving plans that avoid traffic jams, and can also suggest routes based on the user's emotions.In addition, by utilizing real-time information, route guidance is always based on the latest conditions.

[1491] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1492] Step 1: Enter user data

[1493] The user inputs the origin, destination, and departure date and time into the device. The device retrieves this data and sends it to the server in a structured format (e.g., JSON). The server then starts optimizing the route based on the origin, destination, and departure date and time.

[1494] Step 2: Get weather information

[1495] The server sends a request to a weather forecast API (e.g., OpenWeatherMap API) to get current and forecast weather information for the origin and destination. As input, the API request is sent, and as output, weather information (e.g., temperature, chance of precipitation, and weather conditions) is returned in JSON format.

[1496] Step 3: Get Calendar Information

[1497] The server retrieves event information (e.g., public holidays, elementary and junior high school holidays) related to the specified departure date and time from a calendar information API (e.g., Google Calendar API). An API query is sent as input, and calendar event information is retrieved as output.

[1498] Step 4: Obtaining historical traffic congestion information

[1499] The server issues a query to the traffic database to retrieve historical traffic data for days with similar conditions to the specified date and time. The input is an SQL query, and the output is a dataset containing historical traffic information.

[1500] Step 5: Data integration and congestion prediction

[1501] The server integrates the collected weather information, calendar information, and past traffic congestion information and inputs it into a machine learning model. The input is the integrated dataset, and the output is the traffic congestion prediction result. Specifically, the model takes into account information such as weather and event dates to predict the probability of traffic congestion occurring.

[1502] Step 6: Generate main and detour routes

[1503] The server uses the Google Maps API and Microsoft Bing Maps API to generate optimal routes and detour routes based on predicted congestion data. The inputs are the departure point, destination, and congestion forecast data, and the output is route information and travel time for each route.

[1504] Step 7: Analyze the sentiment data

[1505] The device captures the user's voice and facial expression data in real time and sends it to the server, which then analyzes the data using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stress level).

[1506] Step 8: Adjust the route information

[1507] The server analyzes the user's emotional state and adjusts the route as necessary. If the user is feeling stressed, it will suggest a quieter route or a more scenic route. The input is the emotion analysis result, and the output is the adjusted route information.

[1508] Step 9: Submit route information

[1509] The server sends the generated and adjusted route information to the terminal. The route information is the input and the transmission to the terminal is the output.

[1510] Step 10: View Route Information

[1511] The terminal displays the route information received from the server on a map screen. Route information is input, and the route is displayed to the user as output. The required times for the main route and detour routes are also displayed, helping the user select the optimal route.

[1512] Step 11: Real-time updates

[1513] While driving, the device obtains real-time traffic congestion information from the traffic information service and sends it to the server. The server recalculates the route based on the new information and sends the optimal route and detour route to the device. The real-time traffic congestion information is the input, and the updated route information is the output.

[1514] Through these steps, the present invention provides comfortable and efficient navigation for the user.

[1515] (Application example 2)

[1516] 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."

[1517] Conventional navigation systems provide optimal routes based on traffic and weather information, but do not take into account the user's emotional state. As a result, users are likely to feel stressed due to traffic congestion and other traffic conditions. Even in cases where real-time updates are provided regarding traffic avoidance and route changes, route selection is not yet possible, taking into account the user's mental state. To solve this problem, it is necessary to propose optimal routes that take the user's emotions into account.

[1518] 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.

[1519] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time traffic information and updating the route, means for recognizing the user's emotions, and means for providing a route according to the user's emotional state, thereby making it possible to provide a stress-reducing route according to the user's emotional state.

[1520] "Traffic information" refers to data on road usage, such as road congestion, traffic congestion information, and whether or not road construction is occurring.

[1521] "Traffic congestion prediction" is the prediction of future road congestion based on past traffic data, current traffic conditions, and other related information.

[1522] "Weather information" is data on weather conditions such as temperature, probability of precipitation, and wind speed in a specified area.

[1523] "Calendar information" is schedule data such as holidays, school holidays, special events, etc., that correspond to specific dates and times.

[1524] "Past congestion information" is recorded data relating to road congestion conditions at specific past dates and times.

[1525] The "means for calculating a route" is a means for calculating the optimal route from a departure point to a destination using traffic information, weather information, calendar information, and past congestion information.

[1526] The "optimal route" is the optimal route calculated based on predicted congestion data, taking into account the required time, distance, traffic conditions, etc.

[1527] A "detour route" is an alternative route to avoid congestion on a designated main route.

[1528] "Generated route information" refers to data relating to the calculated and generated optimum route and detour route.

[1529] A "terminal" is an electronic device that displays information and exchanges data between a user and the Internet.

[1530] "Real-time traffic congestion information" is data that provides current road traffic conditions in real time.

[1531] The "means for updating a route" is a means for recalculating an existing route based on new traffic information and providing the latest route information.

[1532] "User emotions" refers to a person's psychological state or mood, and includes emotions such as stress, frustration, and relief.

[1533] "Means for recognizing emotions" refers to means for identifying the emotional state of a user through voice analysis, facial expression analysis, etc.

[1534] A "route according to emotional state" is a route selected to reduce stress, taking into consideration the user's current emotions.

[1535] This invention relates to a system that predicts congestion based on traffic information, recognizes the user's emotions, and provides the optimal route. This system is realized through the interaction between a server, a terminal, and a user.

[1536] The server includes a means for predicting congestion based on traffic information, a means for acquiring weather and calendar information, a means for calculating routes based on past congestion information, a means for generating optimal routes and detour routes based on predicted congestion data, a means for transmitting the generated route information to the terminal, a means for acquiring real-time congestion information and updating routes, a means for recognizing user emotions, and a means for providing routes according to the user's emotional state. This server generally operates in a cloud computing environment connected to the Internet. Specifically, it is implemented using a backend service using Python or JavaScript (e.g., Django, Node.js).

[1537] The device displays route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary. This device is typically a smartphone or car navigation system, and is implemented as an Android or iOS application. Specifically, map services such as Google Maps API are used to obtain and display real-time data, and libraries such as EmotionEngine are used for emotion analysis.

[1538] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination on the new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route. Emotion recognition uses voice input and facial expression analysis via a camera.

[1539] Specifically, the server obtains weather information from a weather information API and event information for a specific day from a calendar information API. It then obtains past traffic congestion information from a traffic database and predicts traffic congestion based on this information. In this way, an optimal route and detour route are generated and sent to the terminal. The terminal then displays this route information on a map screen and provides it to the user.

[1540] While driving, the user's voice and facial expression data are collected by the device and analyzed by the emotion engine. If the user feels stressed, this information is sent to the server, which then calculates a new detour route that corresponds to the user's emotions. This allows the user to reach their destination via a more comfortable route.

[1541] As a concrete example, consider a user heading from Tokyo to Hakone at 9 a.m. on a holiday. The system generates a main route and a detour route based on weather forecasts, holiday information, and past traffic congestion data. If the user's emotion is detected as irritation while driving, the emotion engine recognizes this and suggests changing the route to a quieter route on the server side.

[1542] An example prompt is, "If the user feels stressed while driving, the emotion engine should detect this and suggest a quieter route in real time. Also, notify the user of the new route along with the estimated travel time and distance."

[1543] In this way, the present invention can provide the user with a comfortable driving plan that is less stressful.

[1544] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1545] Step 1: User Input

[1546] The user inputs the departure point, destination, and departure date and time into the terminal.

[1547] Input data: origin, destination, departure date and time.

[1548] Output data: The input data is sent to the server.

[1549] Step 2: Get weather information

[1550] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1551] Input data: origin, destination.

[1552] Data processing: Obtain weather information using API.

[1553] Output data: Weather information.

[1554] Step 3: Get Calendar Information

[1555] The server uses the calendar information API to obtain information about holidays and special events for the relevant day.

[1556] Input data: Departure date and time.

[1557] Data processing: Calendar information is obtained using API.

[1558] Output data: Calendar information.

[1559] Step 4: Obtaining historical traffic congestion information

[1560] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1561] Input data: origin, destination, departure date and time.

[1562] Data processing: Obtain past traffic congestion information using database queries.

[1563] Output data: Past traffic congestion information.

[1564] Step 5: Traffic congestion prediction

[1565] The server uses a traffic prediction algorithm to predict congestion at a specified date and time based on collected weather information, calendar information, and past congestion information.

[1566] Input data: Weather information, calendar information, past traffic congestion information.

[1567] Data calculation: Traffic prediction algorithms are applied to predict congestion.

[1568] Output data: Predicted congestion data.

[1569] Step 6: Route Generation

[1570] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1571] Input data: predicted traffic congestion data, origin, destination.

[1572] Data calculation: Route calculation algorithm is applied to generate a route.

[1573] Output data: Optimal route, detour route.

[1574] Step 7: Submit the generated route information

[1575] The server transmits the generated route information to the terminal.

[1576] Input data: Generated route information.

[1577] Output data: Route information is sent to the terminal.

[1578] Step 8: View Route Information

[1579] The terminal displays the optimum route and detour route received from the server on a map screen to provide navigation.

[1580] Input data: Route information.

[1581] Output data: Route information is displayed on the map screen.

[1582] Step 9: Collect emotion data

[1583] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1584] Input data: User's voice data and facial expression data.

[1585] Data Computation: Analyze emotional state using emotion engine.

[1586] Output data: emotion data.

[1587] Step 10: Sending Emotion Data

[1588] The device transmits the emotion data to the server.

[1589] Input data: Emotion data.

[1590] Output data: Emotion data is sent to the server.

[1591] Step 11: Recalculate the route

[1592] The server recalculates the travel time of the detour route based on the emotion data and real-time traffic congestion information.

[1593] Input data: Emotion data, real-time traffic congestion information.

[1594] Data calculation: Route recalculation based on emotion data and traffic congestion information.

[1595] Output data: Updated route information.

[1596] Step 12: Submit updated route information

[1597] The server sends the updated route information to the terminal.

[1598] Input data: Updated route information.

[1599] Output data: Updated route information is sent to the device.

[1600] Step 13: View the updated route

[1601] The terminal displays the updated route information on the map screen and notifies the user.

[1602] Input data: Updated route information.

[1603] Output data: Updated route information is displayed on the map screen.

[1604] Step 14: Navigation Update

[1605] The user follows the instructions on the device and heads to the destination via a new route.

[1606] Input data: Updated route information.

[1607] Output data: User's driving route corrections.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] [Fourth embodiment]

[1612] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1613] 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.

[1614] 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).

[1615] 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.

[1616] 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.

[1617] 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).

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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."

[1625] The present invention is a system that enables users to reach their destination while avoiding traffic jams, and is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"). This system has the function of calculating the optimal route based on traffic information, weather information, calendar information, and past traffic jam information, and updating the route to reflect real-time information.

[1626] Program processing overview

[1627] Server-side processing

[1628] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route.

[1629] 1. Data Collection

[1630] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[1631] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1632] The server searches and retrieves information on traffic congestion that occurred under similar conditions in the past from a traffic database.

[1633] 2. Traffic congestion prediction

[1634] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion for a specified date and time.

[1635] 3. Route Generation

[1636] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1637] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[1638] 4. Sending the results

[1639] The server transmits the generated route information to the terminal.

[1640] Terminal side processing

[1641] The device displays the route information received from the server, provides navigation, and updates the route based on real-time traffic congestion information.

[1642] 1. Data Entry

[1643] The user inputs the departure point, destination, and departure date and time into the terminal.

[1644] The terminal transmits the input data to the server.

[1645] 2. Route display

[1646] The terminal displays the optimum route and detour route received from the server on a map screen.

[1647] It also displays the travel times for the main route and alternate routes, giving users a choice.

[1648] 3. Real-time updates

[1649] The device obtains real-time congestion information from traffic information services while driving.

[1650] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[1651] The terminal displays the updated route information and notifies the user.

[1652] User operation

[1653] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of a route update in real time while driving, the user follows the instructions and heads to the destination along the new route.

[1654] Specific examples

[1655] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1656] 1. Data Collection

[1657] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1658] The terminal transmits this data to the server.

[1659] 2. Traffic congestion prediction and route generation

[1660] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[1661] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[1662] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[1663] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[1664] 3. Sending and displaying results

[1665] The server transmits the generated route information to the terminal.

[1666] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1667] 4. Real-time updates and navigation

[1668] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1669] The terminal acquires traffic congestion information in real time and sends it to the server.

[1670] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1671] The device notifies the user of the updated route information and displays it on the map screen.

[1672] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[1673] The processing flow will be explained below.

[1674] Step 1: Data entry (user, device)

[1675] The user inputs the departure point, destination, and departure date and time into the terminal.

[1676] The terminal formats the entered data and sends it to the server.

[1677] Step 2: Collecting weather and calendar information (server)

[1678] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1679] The server uses a calendar API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[1680] Step 3: Obtaining past traffic congestion data (server)

[1681] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1682] Step 4: Traffic congestion prediction (server)

[1683] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[1684] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[1685] Step 5: Generate Route Information (Server)

[1686] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[1687] Step 6: Sending Route Information (Server)

[1688] The server transmits the generated route information to the terminal.

[1689] Step 7: View Route Information (Device)

[1690] The terminal draws the optimum route and detour route received from the server on a map screen.

[1691] The travel times for the main route and the detour route are also displayed to the user.

[1692] Step 8: Navigation Start (User, Device)

[1693] The user starts the navigation function of the terminal and drives the car.

[1694] The terminal guides the user along the optimum route.

[1695] Step 9: Obtaining real-time traffic congestion information (device)

[1696] The device obtains traffic congestion information from the traffic information service in real time while driving.

[1697] Step 10: Route recalculation (terminal, server)

[1698] The terminal transmits the acquired real-time traffic congestion information to the server.

[1699] The server recalculates route information based on real-time data.

[1700] Step 11: Sending and displaying updated routes (server, terminal)

[1701] The server sends the new route information to the terminal.

[1702] The terminal notifies the user of the updated route information and reflects it on the map screen.

[1703] Step 12: User arrives at destination

[1704] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[1705] This series of steps allows users to avoid traffic jams and reach their destination efficiently.

[1706] Example 1

[1707] 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."

[1708] Conventional navigation systems have difficulty using real-time traffic congestion information to provide users with the optimal route. They also lack the functionality to predict traffic conditions using weather and calendar information and provide users with the optimal route. Furthermore, they are unable to provide detailed traffic congestion predictions using past traffic congestion information based on the departure point, destination, and departure date and time.

[1709] 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.

[1710] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and date and time information, means for calculating a route based on past traffic information, means for generating an optimal route and a detour route based on the predicted traffic data, means for transmitting the generated route information to a device, means for acquiring real-time traffic information and updating the route, means for a user to input a departure point, a destination point, and a departure time, means for displaying the optimal route and the detour route on a map screen, means for acquiring real-time traffic information from a traffic information service while driving and updating the route, and means for displaying the route information generated in real time on a map screen and notifying the user. This makes it possible to provide the user with an optimal route and a detour route based on real-time information.

[1711] "Traffic information" refers to data relating to traffic conditions, including congestion, accidents, road construction, traffic volume, and the like.

[1712] "Traffic congestion forecast" is the process of predicting road congestion at a specified date and time based on various data.

[1713] "Weather information" is data related to weather forecasts, including temperature, precipitation, wind speed, and the like.

[1714] "Date and time information" is data related to a calendar, and includes information about a specific date and time.

[1715] "Historical traffic information" refers to previously recorded traffic data, including traffic performance under specific conditions and dates.

[1716] A "route" is a route from a starting point to a destination, including a main route and a detour route.

[1717] An "optimal route" is the best route calculated taking into account various conditions (travel time, distance, congestion, etc.).

[1718] A "detour route" is an alternative route that is chosen when the normal route is difficult due to traffic congestion or other reasons.

[1719] "Device" refers to the terminal used by the user, including smartphones, car navigation systems, etc.

[1720] "Real-time information" means instantly updated data about current traffic conditions.

[1721] "User" refers to a person who operates and uses this system.

[1722] The "map screen" is a visual interface that displays route information, current location, etc.

[1723] "Traffic Information Service" means an external information platform that provides real-time traffic conditions.

[1724] The present invention is a system that enables users to avoid traffic jams and reach their destinations based on traffic information, weather information, date and time information, and past traffic information. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals").

[1725] Hardware and Software

[1726] server:

[1727] The server uses the traffic forecast database, weather information API, date and time information API, and traffic information API to collect and analyze various data based on user requests. Specifically, it uses the OpenWeatherMap API to obtain weather information, the Google Calendar API to obtain date and time information, and the HERE Traffic API to obtain past traffic information.

[1728] Device:

[1729] The terminal provides an interface for the user to input the starting point, destination point, and departure time, and displays the route information received from the server on a map screen. The terminal also obtains traffic data in real time from a traffic information service (e.g., Google Maps API) and sends it to the server.

[1730] User:

[1731] The user inputs the departure point, destination, and departure date and time into the device and refers to the information on the optimal route and detour route sent from the server. If the device notifies the user of route updates in real time while driving, the user follows the instructions and heads to the destination on the new route.

[1732] Data processing and calculation

[1733] Data collection:

[1734] The server uses a weather forecast API to obtain weather information for the specified date and time near the departure point and destination.

[1735] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1736] The server searches and retrieves traffic information that occurred under similar conditions in the past from a traffic database.

[1737] Traffic congestion forecast:

[1738] Based on the collected weather information, calendar information, and past traffic information, the server uses a traffic prediction algorithm to predict congestion at a specified date and time.

[1739] Route Generation:

[1740] The server generates the optimal route from the departure point to the destination and a detour route based on the predicted traffic data, taking into account various conditions (travel time, distance, road type, etc.) to avoid traffic congestion.

[1741] Specific examples

[1742] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1743] Data collection:

[1744] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1745] The terminal transmits this data to the server.

[1746] Weather information, calendar information, traffic forecast:

[1747] The server obtains weather information (e.g., sunny or rainy) for Tokyo and Hakone from a weather forecast API (e.g., OpenWeatherMap API).

[1748] Obtain information from the Google Calendar API about holidays on the relevant dates and events that may attract a large number of tourists.

[1749] The HERE Traffic API is used to obtain traffic data from similar conditions in the past and predict the occurrence of traffic congestion.

[1750] Route Generation:

[1751] Based on the predicted data, the server generates the main route (e.g., expressway) and detour route (e.g., general road) and calculates the required travel time.

[1752] Send and view results:

[1753] The server transmits the generated route information to the terminal.

[1754] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1755] Real-time updates:

[1756] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1757] While driving, the device obtains real-time traffic information from the traffic information service and transmits it to the server.

[1758] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1759] The device notifies the user of the updated route information and displays it on the map screen.

[1760] In this way, the system provides the user with an optimal driving plan that avoids traffic jams.

[1761] Example prompts to input to the generative AI model

[1762] "I want to create a system that calculates the optimal driving route to avoid traffic jams. I'm thinking of using weather forecast APIs, calendar information APIs, and traffic information APIs, but I'd like to know how to collect the data, calculate the route, and notify the user."

[1763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1764] Step 1: Data entry

[1765] The user inputs the departure point, destination, and departure date and time into the terminal. For example, the user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time. This input data is sent from the terminal to the server.

[1766] Input: Departure point, destination, departure date and time

[1767] Output: The input data sent to the server

[1768] Step 2: Get weather information

[1769] The server uses weather information APIs to obtain weather information for the specified date and time near the departure and destination locations. For example, the server uses the OpenWeatherMap API to obtain detailed weather forecasts (temperature, precipitation, wind speed, etc.) for Tokyo and Hakone.

[1770] Input: Departure point, destination, departure date and time

[1771] Output: Weather information (temperature, precipitation, wind speed, etc.)

[1772] Step 3: Get Calendar Information

[1773] The server uses a date and time information API to obtain information about holidays and events for the specified date. For example, it uses the Google Calendar API to obtain information about holidays, school holidays, and other events for the specified date.

[1774] Input: Departure date and time

[1775] Output: Calendar information (holidays, school vacations, event information, etc.)

[1776] Step 4: Obtaining historical traffic information

[1777] The server uses a traffic information API to obtain traffic data that occurred under similar conditions in the past. For example, it uses the HERE Traffic API to obtain data on traffic conditions similar to the specified date and time and conditions.

[1778] Input: Departure point, destination, departure date and time

[1779] Output: Past traffic information

[1780] Step 5: Traffic congestion prediction

[1781] The server uses a traffic prediction algorithm to predict congestion for a specified date and time based on collected weather, calendar, and past traffic information, and generates forecast data on which roads will be congested at which times.

[1782] Input: Weather information, calendar information, past traffic information

[1783] Output: Traffic congestion forecast data (predicted congestion situation)

[1784] Step 6: Route Generation

[1785] The server generates the optimal route and detour routes from the departure point to the destination based on the predicted congestion data. Specifically, it calculates the main route and detour routes, taking into account factors such as travel time, distance, and road type.

[1786] Input: Traffic congestion forecast data

[1787] Output: Optimal route, detour route

[1788] Step 7: Sending the results

[1789] The server sends the generated route information to the device, including information on the optimal route, detours, travel time, and distance. The server sends this data in JSON format to the device.

[1790] Input: Optimal route, detour route

[1791] Output: Route information sent to the device

[1792] Step 8: View Route

[1793] The device receives route information from the server and displays it on a map screen, along with the travel times for the main route and detour routes, allowing the user to select the optimal route.

[1794] Input: Route information sent from the server

[1795] Output: Optimal route and detour route displayed on the map screen

[1796] Step 9: Real-time information acquisition

[1797] While driving, the device receives real-time traffic information from traffic information services (e.g., Google Maps API), allowing the user to understand current traffic congestion and accident information.

[1798] Input: Real-time information from traffic information services

[1799] Output: Real-time traffic information

[1800] Step 10: Route Update

[1801] The device sends real-time traffic information to the server and requests the server to calculate a new route. The server then calculates a new detour route based on this information and sends it to the device.

[1802] Input: Real-time traffic information

[1803] Output: New route information

[1804] Step 11: View the updated route

[1805] The device will notify the user of the updated route information and display the new route information on the map screen, allowing the user to adjust their driving route based on the latest information.

[1806] Input: New route information sent from the server

[1807] Output: Updated route information displayed on the map screen

[1808] (Application example 1)

[1809] 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."

[1810] Conventional navigation systems only predict congestion based on traffic information and present optimal routes, which can lead to problems with real-time performance and accuracy in autonomous vehicles. Furthermore, congestion conditions can vary significantly depending on city conditions and weather, and these systems are unable to adequately address these issues. Therefore, there is a need for a system that improves usability and provides a comfortable travel experience in autonomous vehicles.

[1811] 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.

[1812] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for acquiring real-time congestion information and updating the route, means for operating the autonomous driving function, and means for displaying the route information on the in-vehicle display. This makes it possible to respond to changes in traffic conditions due to weather or events, and to simultaneously realize real-time route optimization and operation of the autonomous driving function.

[1813] "Traffic information" refers to information on the flow of traffic on roads, congestion, accidents, road construction, etc.

[1814] "Means for predicting traffic congestion" refers to algorithms that analyze existing and past traffic data and predict the occurrence of future traffic congestion.

[1815] "Weather information" is data relating to weather conditions in a specified area and at a specified date and time.

[1816] "Calendar information" is information about schedules such as holidays, school holidays, and events on specific dates.

[1817] "Method of calculating routes based on past congestion information" refers to an algorithm that uses past traffic data to calculate the optimal travel route.

[1818] The "means for generating optimal routes and detour routes based on predicted congestion data" refers to algorithms and mechanisms that calculate primary and alternative routes taking into account future traffic conditions.

[1819] The "means for transmitting generated route information to the terminal" is a function for transmitting the calculated travel route to the user's device via a communication means.

[1820] "Means for obtaining real-time traffic congestion information and updating routes" refers to a function that obtains the latest traffic conditions in real time while traveling and recalculates and updates the route if necessary.

[1821] "Means for operating autonomous driving functions" refers to a function that issues instructions to the autonomous driving system of an autonomous vehicle and controls the driving of the vehicle.

[1822] "Means for displaying route information on an in-vehicle display" refers to a function for visually displaying the calculated travel route and related information on a display device inside the vehicle.

[1823] This invention provides a system that allows users to use autonomous vehicles to select the optimal route based on traffic information, weather information, calendar information, and past congestion information, and reach their destination safely and comfortably. This system works in conjunction with the navigation software and on-board display of the autonomous vehicle.

[1824] System configuration

[1825] The system mainly consists of three components: a server, a terminal, and a user. The functions of each component are as follows:

[1826] server

[1827] The server collects and analyzes traffic information, weather information, calendar information, and past congestion information based on the departure point, destination, and departure date and time entered by the user, and generates the optimal route and detour route. Specifically, information is collected using a weather information API, calendar information API, and traffic database. The collected data is analyzed using a traffic prediction algorithm, and congestion predictions are made for the specified date and time. The optimal route and detour route are then generated and sent to the user's device.

[1828] Terminal

[1829] The terminal is installed in an autonomous vehicle and includes an on-board display and autonomous driving system. The terminal displays route information received from the server and provides navigation. It also has the function of updating routes based on traffic congestion information obtained in real time. The terminal obtains real-time traffic information through a GPS module and an internet connection, and sends the information to the server to calculate a new route as needed.

[1830] User

[1831] The user inputs the departure point, destination, and departure date and time into the device. Following the device's guidance, the user begins driving to the destination along the optimal route. If the device notifies the user of real-time route updates during autonomous driving, the user follows those instructions.

[1832] Specific examples

[1833] As a specific example, consider the case of heading from Tokyo to a tourist destination at 9:00 AM on a holiday. The user enters "Tokyo" as the departure point, "tourist destination" as the destination, and "9:00 AM on a holiday" as the departure date and time into the device. The device sends this data to the server. The server obtains weather information for Tokyo and the tourist destination from a weather forecast API and information that the relevant day is a holiday or that there may be a large number of tourists from a calendar information API. It obtains traffic congestion information under similar conditions from past traffic congestion data and predicts the occurrence of traffic congestion. The server generates a main route and a detour route based on the predicted data and calculates the required travel time. The server sends the generated route information to the device. The device displays the received route information on the in-vehicle display, along with the required travel time for the main route and the detour route. The user follows the device's guidance and begins driving toward the destination tourist destination via the optimal route. The device obtains traffic congestion information in real time and sends it to the server. If traffic congestion occurs, the server calculates a new detour route and sends it to the device. The device notifies the user of the updated route information and displays it on the in-vehicle display. In this way, the system can provide the user with an optimal driving plan that avoids traffic jams.

[1834] Example prompt sentences to use

[1835] Example prompts that may be used include:

[1836] "Calculate the best route from Tokyo to a tourist spot."

[1837] "Please tell me the route taking into account today's weather and traffic information."

[1838] Please suggest an alternative route to avoid the traffic jam.

[1839] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1840] Step 1:

[1841] The user inputs the departure point, destination, and departure date and time into the terminal. The input data includes the departure point, destination, departure date and time, and is transmitted to the server by the terminal.

[1842] Step 2:

[1843] The server uses the weather forecast API to obtain weather information for the specified date and time near the departure point and destination. The input is the departure point and destination information, and based on that, a request is sent to the API to obtain weather information data. The output is the weather information for the specified date and time.

[1844] Step 3:

[1845] The server uses the calendar information API to obtain information about holidays, elementary and junior high school holidays, and other event information for the relevant day. The input is the departure date and time information, and based on that, a request is sent to the calendar API to obtain event information data. The output is calendar information.

[1846] Step 4:

[1847] The server searches and retrieves traffic information from a traffic database about traffic congestion that occurred under similar conditions in the past. The inputs include the departure point, destination, departure date and time, and a database query is executed based on these to retrieve traffic congestion information. The output is past traffic congestion information.

[1848] Step 5:

[1849] The server runs a traffic prediction algorithm based on the collected weather information, calendar information, and past traffic congestion information to predict traffic congestion for a specified date and time. The inputs are the acquired weather information, calendar information, and past traffic congestion information, which are combined and analyzed by the algorithm. The output is traffic congestion prediction data.

[1850] Step 6:

[1851] The server generates the optimal route and detour route from the departure point to the destination point based on the predicted congestion data. The inputs are the congestion prediction data, the departure point, and the destination, and the route generation algorithm is executed based on this information. The output is the optimal route and detour route information.

[1852] Step 7:

[1853] The server sends the generated route information to the terminal. The input is the generated route information, and the server executes a communication operation to send it to the terminal. The output is the route information that has been sent to the terminal.

[1854] Step 8:

[1855] The terminal displays the optimal route and detour route received from the server on the in-vehicle display. The input is the route information received from the server, and the terminal performs operations to display it on the display screen. The output is a visual display of the route information.

[1856] Step 9:

[1857] The device obtains real-time traffic information from the traffic information service while driving. The input is the current GPS location and a real-time traffic information API request, and the latest traffic information is obtained from the API. The output is real-time traffic information.

[1858] Step 10:

[1859] The terminal sends the acquired real-time traffic congestion information to the server and requests the calculation of a new route. The input is the real-time traffic congestion information, and the terminal performs a communication operation to send this information to the server. The output is the traffic congestion information that has been sent to the server.

[1860] Step 11:

[1861] The terminal receives the new detour route from the server and displays it on the in-vehicle display. The input is the updated route information, and the terminal performs an operation to display it again on the display. The output is the updated route information being displayed.

[1862] This series of steps allows users to always use the latest and most appropriate navigation information, enabling them to reach their destination smoothly.

[1863] 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.

[1864] This invention is a system that helps users avoid traffic jams and reach their destination, and combines an emotion engine that recognizes the user's emotions. This system is installed in smartphones and car navigation systems (hereinafter referred to as "terminals"), and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1865] Program processing overview

[1866] Server-side processing

[1867] The server collects and analyzes various information based on the departure point, destination, and departure date and time entered by the user, and generates optimal routes and detour routes.The server also analyzes the user's emotional data and provides routes that correspond to the user's emotional state.

[1868] 1. Data Collection

[1869] The server calls the weather forecast API to obtain weather information for the departure and destination locations.

[1870] The server uses the calendar information API to obtain information about public holidays and elementary and junior high school holidays for the relevant day.

[1871] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1872] 2. Traffic congestion prediction

[1873] Based on the collected weather information, calendar information, and past traffic congestion information, the server uses a traffic prediction algorithm to predict traffic congestion at a specified date and time.

[1874] 3. Route Generation

[1875] The server generates the optimal route and detour route from the departure point to the destination based on the predicted congestion data.

[1876] When generating a route, various conditions (travel time, distance, type of road, etc.) are taken into consideration to avoid traffic jams.

[1877] 4. Emotion Data Analysis

[1878] The server uses an emotion engine to analyze the user's emotion data, which includes analyzing the user's voice and facial expressions.

[1879] 5. Route adjustment based on user sentiment

[1880] If the user is feeling stressed, the server generates a route to alleviate that stress and adds it to the regular route suggestions.

[1881] 6. Submitting the results

[1882] The server transmits the generated route information to the terminal.

[1883] Terminal side processing

[1884] The device displays the route information received from the server and provides navigation. It also uses an emotion engine to analyze the user's emotions in real time and update the route as necessary.

[1885] 1. Data Entry

[1886] The user inputs the departure point, destination, and departure date and time into the terminal.

[1887] The terminal transmits the input data to the server.

[1888] 2. Route display

[1889] The terminal displays the optimum route and detour route received from the server on a map screen.

[1890] It also displays the travel times for the main route and alternate routes, giving users a choice.

[1891] 3. Emotion Monitoring

[1892] The device collects the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1893] If stress or irritation is detected, the information is sent to the server.

[1894] 4. Real-time updates

[1895] The device obtains real-time congestion information from traffic information services while driving.

[1896] If a traffic jam occurs, the information is sent to the server and a new route is calculated.

[1897] The device notifies the user of the updated route information and displays it on the map screen.

[1898] User operation

[1899] The user inputs the departure point, destination, and departure date and time into the device and refers to the route information sent from the server. If the device notifies the user of real-time route updates while driving, the user follows the instructions and heads to the destination on a new route. If the emotion engine detects the user's emotions and suggests a route that reduces stress, the user can select that route.

[1900] Specific examples

[1901] As an example, consider the case where a user goes for a drive from Tokyo to Hakone at 9:00 AM on a holiday.

[1902] 1. Data Collection

[1903] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal.

[1904] The terminal transmits this data to the server.

[1905] 2. Traffic congestion prediction and route generation

[1906] The server obtains weather information (e.g. sunny or rainy) for Tokyo and Hakone from the weather forecast API.

[1907] Obtain information from the calendar information API, such as whether the relevant day is a holiday or whether there is a possibility of a large number of tourists.

[1908] Traffic congestion information under similar conditions is obtained from past traffic congestion data, and traffic congestion is predicted.

[1909] Based on the predicted data, the server generates the main route (e.g., Tomei Expressway) and detour route (e.g., ordinary roads) and calculates the required travel time.

[1910] 3. Emotional Data Analysis and User Feedback

[1911] If the user becomes frustrated while driving, the emotion engine will detect this and suggest a quieter route that will reduce stress.

[1912] 4. Sending and displaying results

[1913] The server transmits the generated route information to the terminal.

[1914] The device displays the received route information on a map screen, along with the travel time for the main route and detour routes.

[1915] 5. Real-time updates and navigation

[1916] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[1917] The terminal acquires traffic congestion information in real time and sends it to the server.

[1918] If congestion occurs, the server calculates a new detour route and sends it to the terminal.

[1919] The device notifies the user of the updated route information and displays it on the map screen.

[1920] In this way, the system provides the user with an efficient driving plan that avoids traffic jams, and also suggests routes that reflect the user's emotions.

[1921] The processing flow will be explained below.

[1922] Step 1: Data entry (user, device)

[1923] The user inputs the departure point (e.g., Tokyo), destination (e.g., Hakone), and departure date and time (e.g., 9:00 AM on a holiday) into the terminal.

[1924] The terminal formats the entered data and sends it to the server.

[1925] Step 2: Collecting weather and calendar information (server)

[1926] The server calls the weather forecast API and obtains weather information for the departure and destination locations for the specified date and time.

[1927] The server uses the calendar information API to obtain information about public holidays, elementary and junior high school holidays, and other event information for the relevant day.

[1928] Step 3: Obtaining past traffic congestion data (server)

[1929] The server searches the traffic database for past congestion information and obtains data for days with similar conditions.

[1930] Step 4: Traffic congestion prediction (server)

[1931] The server uses a prediction algorithm to predict traffic congestion at a specified date and time based on the acquired weather information, calendar information, and past traffic congestion data.

[1932] The prediction results are reflected in map data, and the optimal route and detour route are calculated.

[1933] Step 5: Generate Route Information (Server)

[1934] The server generates an optimal route and a detour route from the departure point to the destination point based on the predicted congestion data.

[1935] Step 6: Emotion data analysis (server)

[1936] The server uses an emotion engine to analyze the user's emotion data (voice and facial expressions).

[1937] To detect a state in which a user is feeling stressed.

[1938] Step 7: Route adjustment according to user sentiment (server)

[1939] The server generates routes to reduce the user's stress and adds them to the regular route suggestions.

[1940] Step 8: Sending Route Information (Server)

[1941] The server transmits the generated route information to the terminal.

[1942] Step 9: View Route Information (Device)

[1943] The terminal draws the optimum route and detour route received from the server on a map screen.

[1944] The travel times for the main route and the detour route are also displayed to the user.

[1945] Step 10: Emotion Monitoring (Device)

[1946] The device acquires the user's voice and facial expression data in real time and analyzes it using an emotion engine.

[1947] If the user is feeling stressed, the information is sent to the server.

[1948] Step 11: Navigation Start (User, Device)

[1949] The user starts the navigation function of the terminal and drives the car.

[1950] The terminal guides the user along the optimum route.

[1951] Step 12: Obtaining real-time traffic congestion information (device)

[1952] The device obtains traffic congestion information from the traffic information service in real time while driving.

[1953] Step 13: Recalculating the route (terminal, server)

[1954] The terminal transmits the acquired real-time traffic congestion information to the server.

[1955] The server recalculates route information based on real-time data.

[1956] Step 14: Sending and displaying updated routes (server, terminal)

[1957] The server sends the new route information to the terminal.

[1958] The terminal notifies the user of the updated route information and reflects it on the map screen.

[1959] Step 15: User arrives at destination

[1960] The user follows the guidance on the terminal to reach the destination via the optimal route or the updated route.

[1961] This series of steps allows users to avoid traffic jams, reach their destination efficiently, and even suggests routes based on the user's emotions.

[1962] Example 2

[1963] 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."

[1964] Conventional navigation systems can predict traffic congestion and provide optimal routes using traffic and weather information, but they do not take the user's emotional state into account when proposing routes. As a result, even if the user is feeling stressed, only efficient routes are provided, which can potentially reduce the user's comfort. Furthermore, systems must be able to respond quickly to real-time traffic conditions, and a system that can reliably do so was needed.

[1965] 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.

[1966] In this invention, the server includes means for predicting congestion based on traffic information, means for acquiring weather information and calendar information, means for calculating a route based on past congestion information, means for generating an optimal route and a detour route based on the predicted congestion data, means for transmitting the generated route information to the terminal, means for analyzing user emotion data, means for adjusting the route based on the analyzed emotion data, and means for acquiring real-time congestion information and updating the route, thereby enabling efficient and real-time optimal route suggestions while taking user comfort into consideration.

[1967] "Traffic information" refers to information about road congestion, traffic regulations, accidents, etc.

[1968] "Weather information" refers to meteorological data and forecasts that indicate weather conditions at a particular time or location.

[1969] "Calendar information" is information about events, holidays, vacations, etc. associated with a specific date.

[1970] "Traffic congestion forecasting" is the prediction of future road congestion conditions based on collected traffic information and related data.

[1971] A "route" is a path from a starting point to a destination.

[1972] An "optimal route" is a route that is determined to be the most efficient, taking into account factors such as travel time, distance, and traffic conditions.

[1973] A "detour route" is an alternative route set aside from the main route to meet specific conditions (e.g., avoiding congestion, reducing stress).

[1974] "Emotion data" is data that indicates the emotional state of the user, obtained from the user's voice, facial expression, etc.

[1975] "Emotion analysis" refers to identifying a user's emotional state based on collected emotional data.

[1976] "Real-time information" refers to information provided based on ongoing situations and the most recent data.

[1977] This invention is a system that helps users avoid traffic jams and reach their destinations, and combines an emotion engine that recognizes the user's emotions. This system is installed in devices such as smartphones and car navigation systems, and calculates the optimal route based on traffic information, weather information, calendar information, and past traffic congestion information, and updates the route to reflect real-time information. The emotion engine also has the function of recognizing the user's emotional state and suggesting appropriate responses.

[1978] Specifically, this system is broadly divided into server-side and terminal-side processing.

[1979] Server-side processing

[1980] Data collection

[1981] The server first collects traffic, weather, calendar, and historical congestion information using the following APIs and databases:

[1982] Weather forecast APIs (e.g., OpenWeatherMap API)

[1983] Calendar information API (e.g. Google Calendar API)

[1984] Traffic database: Stores historical traffic information and retrieves data by issuing SQL queries

[1985] Traffic congestion forecast

[1986] Based on the collected data, the server uses traffic prediction algorithms to predict congestion. Using the integrated dataset, machine learning models are applied to predict future congestion information.

[1987] Route Generation

[1988] The server generates main routes and detour routes based on predicted traffic congestion data. It optimizes routes using Google Maps API and Microsoft Bing Maps API. It also generates special routes, such as stress-reducing routes, based on the analysis of user emotion data.

[1989] Emotional Data Analysis

[1990] The server uses an emotion engine to analyze emotion data from the user's voice and facial expressions, utilizing IBM Watson Speech to Text API, IBM Watson Tone Analyzer, and Microsoft Azure Face API.

[1991] Sending the results

[1992] The generated route information and sentiment analysis results are sent to the device via a RESTful API via an HTTPS request.

[1993] Terminal side processing

[1994] Data input from the user

[1995] The user inputs the departure point, destination, and departure date and time into the terminal, and the input data is sent to the server.

[1996] View route information

[1997] The device displays the route information received from the server on a map screen, along with the travel times for the main route and detour routes, providing the user with a choice of routes.

[1998] Emotion Monitoring

[1999] The device captures the user's voice and facial expressions in real time and analyzes them with an emotion engine. If stress or irritation is detected, the information is sent to the server.

[2000] Real-time updates

[2001] The device obtains real-time traffic congestion information from a traffic information service (e.g., Google Traffic API) and sends it to the server. If new route information is available, it is displayed on the map screen and notified to the user.

[2002] Specific examples

[2003] Consider a case where a user goes for a drive from Tokyo to Hakone at 9:00 a.m. on a holiday.

[2004] 1. Data Collection

[2005] The user inputs "Tokyo" as the departure point, "Hakone" as the destination, and "9:00 AM on a holiday" as the departure date and time into the terminal, and the terminal transmits this data to the server.

[2006] 2. Traffic congestion prediction and route generation

[2007] The server retrieves weather information for Tokyo and Hakone from the weather forecast API (e.g. sunny).

[2008] Obtain information from the calendar information API that the relevant date is a holiday.

[2009] Information on days with similar conditions is obtained from past traffic congestion data to predict traffic congestion.

[2010] The server generates the main route (e.g., Tomei Expressway) and the detour route (e.g., general roads) and calculates the required travel time.

[2011] 3. Emotional Data Analysis and User Feedback

[2012] If the user becomes irritated while driving, the emotion engine will detect this and suggest a quieter route to reduce stress.

[2013] 4. Sending and displaying results

[2014] The server sends the generated route information to the device, which then displays the route information on a map screen, along with the travel times for the main route and detour routes.

[2015] 5. Real-time updates and navigation

[2016] The user follows the instructions on the device and begins driving towards their destination, Hakone, along the optimal route.

[2017] The terminal acquires traffic congestion information in real time and sends it to the server.

[2018] When traffic congestion occurs, the server calculates a new detour route and sends it to the device, which then displays the updated route information on the map screen and notifies the user.

[2019] This system provides users with efficient driving plans that avoid traffic jams, and can also suggest routes based on the user's emotions.In addition, by utilizing real-time information, route guidance is always based on the latest conditions.

[2020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2021] Step 1: Enter user data

[2022] The user inputs the origin, destination, and departure date and time into the device. The device retrieves this data and sends it to the server in a structured format (e.g., JSON). The server then starts optimizing the route based on the origin, destination, and departure date and time.

[2023] Step 2: Get weather information

[2024] The server sends a request to a weather forecast API (e.g., OpenWeatherMap API) to get current and forecast weather information for the origin and destination. As input, the API request is sent, and as output, weather information (e.g., temperature, chance of precipitation, and weather conditions) is returned in JSON format.

[2025] Step 3: Get Calendar Information

[2026] The server retrieves event information (e.g., public holidays, elementary and junior high school holidays) related to the specified departure date and time from a calendar information API (e.g., Google Calendar API). An API query is sent as input, and calendar event information is retrieved as output.

[2027] Step 4: Obtaining historical traffic congestion information

[2028] The server issues a query to the traffic database to retrieve historical traffic data for days with similar conditions to the specified date and time. The input is an SQL query, and the output is a dataset containing historical traffic information.

[2029] Step 5: Data integration and congestion prediction

[2030] The server integrates the collected weather information, calendar information, and past traffic congestion information and inputs it into a machine learning model. The input is the integrated dataset, and the output is the traffic congestion prediction result. Specifically, the model takes into account information such as weather and event dates to predict the probability of traffic congestion occurring.

[2031] Step 6: Generate main and detour routes

[2032] The server uses the Google Maps API and Microsoft Bing Maps API to generate optimal routes and detour routes based on predicted congestion data. The inputs are the departure point, destination, and congestion forecast data, and the output is route information and travel time for each route.

[2033] Step 7: Analyze the sentiment data

[2034] The device captures the user's voice and facial expression data in real time and sends it to the server, which then analyzes the data using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stress level).

[2035] Step 8: Adjust the route information

[2036] The server analyzes the user's emotional state and adjusts the route as necessary. If the user is feeling stressed, it will suggest a quieter route or a more scenic route. The input is the emotion analysis result, and the output is the adjusted route information.

[2037] Step 9: Submit route information

[2038] The server sends the generated and adjusted route information to the terminal. The route information is the input and the transmission to the terminal is the output.

[2039] Step 10: View Route Information

[2040] The terminal displays the route information received from the server on a map screen. Route information is input, and the route is displayed to the user as output. The required times for the main route and detour routes are also displayed, helping the user select the optimal route.

[2041] Step 11: Real-time updates

[2042] While driving, the device obtains real-time traffic congestion information from the traffic information service and sends it to the server. The server recalculates the route based on the new information and sends the optimal route and detour route to t...

Claims

1. A means for predicting congestion based on traffic information; means for obtaining weather and calendar information; A means of calculating routes based on past traffic congestion information, means for generating an optimal route and a detour route based on predicted congestion data; means for transmitting the generated route information to a terminal; A means to obtain real-time traffic information and update routes; A system including:

2. 2. The system according to claim 1, further comprising means for calculating the probability of congestion occurring for each time period based on the predicted congestion data and weather information.

3. 2. The system according to claim 1, further comprising means for recalculating the required time for the detour route based on real-time congestion information.

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